CN117369445A - Control method of movable robot and movable robot - Google Patents

Abstract

The embodiment of the application provides a control method of a movable robot and the movable robot. The method comprises the following steps: determining the traffic attribute of each position in the target area according to a first map of the target area, wherein the first map comprises information of whether each position in the target area has an obstacle or not, and the traffic attribute of the position represents the feasibility of the movable robot passing through the position; according to the traffic attribute of each position, carrying out regional division on the target area to generate a second map of the target area, wherein the second map comprises a passable area, an unviewable area and a potential passable area; and determining a traffic policy of the movable robot for executing the current processing task according to the second map, and controlling the movable robot to execute the current processing task according to the traffic policy, wherein the traffic policy for the potential traffic zone comprises an attempt traffic policy. The scheme realizes the accurate control of the movable robot and remarkably improves the processing effect of the current processing task.

Description

Control method of movable robot and movable robot

Technical Field

The present application relates to the field of robotics, and more particularly, to a control method of a mobile robot, and a computer-readable storage medium.

Background

With the development of the intelligent industry, the robot technology is widely applied to various fields. In particular, for a mobile robot, it can flexibly move in a target area and can assist a user in performing a processing task on the whole or part of the target area during the movement. For example, the cleaning robot may complete a cleaning task for a user-designated area during movement of the target area.

The mobile robot in the related art generally includes a sensor that can collect surrounding information. In the moving process of the movable robot, an obstacle map can be generated according to the environmental information acquired by the sensor. A simple passable map may then be generated from the obstacle map. That is, the target area is divided into two areas, i.e., an unviewable area with an obstacle and a passable area without an obstacle. And finally, controlling the movable robot to execute corresponding processing tasks according to the established passable map.

However, the construction of a passable map in this solution, while simple, is very poor in accuracy. For example, there is a high possibility that there are areas in the target area where the sensor of the movable robot cannot accurately collect its information, areas where different types of sensors collect different information, or areas where obstacle information changes at different times, and these areas are likely to be simply misjudged as passable areas or non-passable areas. The movable robot is caused to have lower processing efficiency or poorer processing effect for executing processing tasks according to the passable map.

Disclosure of Invention

The present application has been made in view of the above-described problems. According to an aspect of the present application, there is provided a control method of a movable robot, including:

determining the traffic attribute of each position in the target area according to a first map of the target area, wherein the first map comprises information of whether each position in the target area has an obstacle or not, and the traffic attribute of the position represents the feasibility of the movable robot passing through the position;

according to the traffic attribute of each position, carrying out regional division on the target area to generate a second map of the target area, wherein the second map comprises a passable area, an unviewable area and a potential passable area; and

and determining a traffic policy of the movable robot for executing the current processing task according to the second map, and controlling the movable robot to execute the current processing task according to the traffic policy, wherein the traffic policy of the potential traffic zone comprises an attempt traffic policy.

According to another aspect of the present application, there is provided a mobile robot including a control module for executing the above-described control method.

According to another aspect of the present application, a computer readable storage medium is provided, storing a computer program/instruction for executing the control method of the movable robot described above when running.

According to the control method of the embodiment of the application, the passing attribute of each position in the target area is accurately determined according to the determined obstacle map of the target area. And precisely dividing the target area into a passable area, an unviewable area and a potential passable area according to the passing attribute. Finally, a traffic policy of the mobile robot for executing the current processing task is determined according to the generated second map of the area comprising the three traffic attributes. And, since the determined traffic policy for the potentially trafficable region includes an attempt traffic policy, traffic may be intelligently attempted according to the actual needs of the user while walking to such region when performing processing tasks. Therefore, the processing effect of the current processing task can be obviously improved, and the user experience is better. Meanwhile, as the determined passing attribute of each position is higher in precision, unnecessary behaviors and invalid operations of the movable robot during execution of processing tasks can be avoided, so that the behaviors of the movable robot are more intelligent, and the precise control of the movable robot is realized.

The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.

Drawings

The foregoing and other objects, features and advantages of the present application will become more apparent from the following more particular description of embodiments of the present application, as illustrated in the accompanying drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the application and not constitute a limitation to the application. In the drawings, like reference numerals generally refer to like parts or steps.

FIG. 1 shows a schematic flow chart of a method of controlling a mobile robot according to one embodiment of the present application;

FIG. 2 shows a schematic diagram of a second map according to one embodiment of the present application;

FIG. 3 shows a schematic diagram of a first topology according to one embodiment of the present application;

FIG. 4 shows a schematic diagram of a first topology according to another embodiment of the present application;

FIG. 5 shows a schematic diagram of a second topology according to one embodiment of the present application;

FIG. 6 shows a schematic diagram of a fourth map according to one embodiment of the present application; and

fig. 7 shows a schematic flow chart of a control method of a mobile robot according to another embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application described herein, all other embodiments that may be made by one skilled in the art without the exercise of inventive faculty are intended to fall within the scope of protection of the present application.

As before, the accuracy of the passable map established by the mobile robot in the related art is poor, resulting in low efficiency or poor processing effect of the mobile robot in performing the processing task. Take a sweeping robot as an example. The passable map established by some sweeping robots misjudges some actually unvented areas as passable, so that the sweeping robots repeatedly try to pass through the areas when executing a sweeping task, the time of wandering around the areas and the time of decision giving up are overlong, and the cleaning efficiency is low; the passable map established by some sweeping robots misjudges some actually passable areas as not passable, so that the sweeping robots do not sweep the areas or sweep other areas to be swept which can be reached through the areas, and the cleaning effect is poor.

In order to at least partially solve the above technical problems, according to an aspect of the embodiments of the present application, a control method of a movable robot is provided. The method may be used with various types of robots that are operated in a movable manner, and is not limited in this application. For example, the robot may be used for a cleaning robot such as a floor cleaning robot, a water surface cleaning robot, or the like, may be used for an outdoor work robot such as a mowing robot, a watering robot, or the like, and may be used for a service robot such as a meal delivery robot, a shopping guide robot, a greeting robot, or the like.

Fig. 1 shows a schematic flow chart of a control method 1000 of a mobile robot according to one embodiment of the present application. As shown in fig. 1, the control method 1000 includes step S1200, step S1400, and step S1600. For simplicity, various implementations of the control method 1000 will be described below with respect to a sweeping robot.

Step S1200, determining the traffic attribute of each position in the target area according to the first map of the target area. Wherein the first map includes information of whether or not there is an obstacle at each location in the target area, and a traffic attribute of the location indicates feasibility of the movable robot to pass through the location.

According to embodiments of the present application, the target area may be an area where the movable robot is desired to process. For example, for a cleaning robot, the target area may be a surface area where cleaning by the cleaning robot is desired. Taking a home sweeping robot as an example, the target area may be a floor area of a user's home, such as a full house floor area.

The first map may be any suitable type of obstacle map as long as information of obstacles at various positions in the target area is included therein. According to the embodiment of the application, the obstacle map may be generated according to information about obstacles in the target area acquired by the sensor of the movable robot. The movable robot can be controlled to move in the range of the target area before the movable robot performs the processing task, and information of obstacles around the movable robot can be acquired in real time by utilizing various sensors. The information of the obstacle may include position information and form information of the obstacle in the target area. Illustratively, the mobile robot may include at least one type of sensor from the group consisting of: laser radar sensors, collision sensors, cliff sensors, line laser sensors, vision sensors, etc. The sensors may be used to collect information about obstacles in the target area and any existing or future developed mapping algorithm may be used to process the information about the obstacles to create an obstacle map. For example, prior to establishing the obstacle map, the morphology information of the obstacles collected by the one or more sensors may be further processed by using a deep learning model to identify the kind information of each obstacle in the target area. Then, the type information of the obstacle and the position information and the form information of the obstacle acquired by the sensor are used as the input of a mapping algorithm, and the established obstacle map is output. Thus, the position and the type of the obstacle known by various sensors can be marked on the obstacle map. For example, the location of an identified obstacle (e.g., wall, slipper, sofa, door, bed), an unknown collision obstacle, a lidar obstacle, etc., in the target area may be marked.

After the obstacle map is acquired, the traffic attribute of each position in the target area may be determined at least from the obstacle map. According to the embodiment of the application, the traffic attribute may at least express an attribute of whether the movable robot can safely pass through the position. To some extent, the traffic attribute may indicate whether the mobile robot can walk at that location and the size of the cost of walking, or how difficult it is to do so. The classification definition of the traffic attribute can be set according to actual requirements, and the classification definition is not limited in the application. According to the embodiment of the application, the traffic attribute can at least comprise three types of traffic, non-traffic and potential traffic. In one example, the potential passable may be further divided into a first potential passable, a second potential passable, and a third potential passable attribute according to the level of difficulty of the passable. The difficulty of passing through the location area with the passing attribute belonging to the first potential passable location area is lower, the difficulty of passing through the location area with the passing attribute belonging to the second potential passable location area is medium, and the difficulty of passing through the location area with the passing attribute belonging to the third potential passable location area is higher.

The traffic attributes for each location may be determined based on the obstacle map using any suitable method.

Alternatively, the traffic attribute of each location may be determined directly from the obstacle map. In one example, the traffic attributes for each location may be determined directly from the type of obstacle in the obstacle map. In the case where the positions of the identified obstacles, the unknown collision obstacles, the laser radar obstacles, and the like are marked in the obstacle map, the traffic attribute of the positions of the identified obstacles may be determined based on the types of the identified obstacles. For example, the pass attribute of the identified location of the obstacle such as a wall, slipper, sofa, slipper, floor closet, etc. may be determined to be non-passable; the traffic attribute of the identified location of the window covering, step, etc. may be determined to be potentially passable. In another example, different positions where the same obstacle is located may also be determined as different traffic attributes according to the type of the identified obstacle, the position of the obstacle in the map, the state of the obstacle, and the like. For example, for an identified slipper, if the slipper is in an open room, the slipper's location does not block movement of the sweeping robot in other locations of the room, the pass attribute of the slipper's location may be determined to be non-passable. If the slippers are at the entrance of a certain room, which affects the entrance and exit of the sweeping robot into and out of the room, the position of the slippers can be determined as potential passable. For the case where the identified obstacle is a door, if the state of the door is a half-open state, the traffic attribute of the location area where the door is located may be determined as potentially trafficable. And if the state of the door is a closed state, the pass attribute of the position where the door is located may be determined as non-passable. In yet another example, for some unknown types of obstacles, the traffic attribute of the location of the obstacle may also be determined based on the source of the obstacle information. For example, the passing attribute of the position where the unknown obstacle at the same position identified by the collision sensor and the laser sensor is located may be directly determined as non-passing. The passing attribute of the position where no obstacle information is acquired by all the sensors can be determined as passing.

Alternatively, the traffic attribute of each location may be determined comprehensively according to the obstacle map and other available information acquired. By way of example and not limitation, step S1200 determines traffic attributes for various locations in the target area from the first map of the target area, including step S1210. Step S1210, determining the traffic attribute of each position in the target area according to the first map, the behavior information of the movable robot and the forbidden zone information set by the user. The behavior information of the movable robot may be behavior information of the movable robot such as behavior of avoiding an obstacle along the edge, navigation behavior, exploration behavior, obstacle surmounting behavior, getting rid of poverty, unwinding behavior, and the like. For example, if the movable robot has performed obstacle surmounting or getting out of the way for a certain location, the traffic attribute of the location may be determined according to the behavior information of the robot. In a particular example, a traffic attribute of a location where the movable robot performed obstacle-crossing and obstacle-getting-out actions may be determined to be potentially trafficable. The exclusion area information set by the user is an area for prohibiting processing set by the user. For example, in the case where it is acquired that a certain location is a restricted area, the traffic attribute of the location may be determined as non-trafficable. It can be understood that in this scheme, the traffic attribute of each position in the target area is determined by integrating a plurality of information, so that the accuracy of the determined traffic attribute of each position can be ensured to be high, and thus, the precise and efficient control of the movable robot can be ensured.

For example, the information of each obstacle marked in the obstacle map may be analyzed, and the difficulty level of the movable robot passing through the position of the obstacle may be determined by combining the behavior information of the movable robot. The traffic attributes for each location may then be determined based on the difficulty level. For example, the obstacle map and the behavior information of the mobile robot may be combined, and appropriate determination logic may be used to determine the difficulty of the mobile robot traveling at each location in the target area. The passable difficulty may be represented by any suitable data. For example, the passable difficulty may be represented by a numerical value. For example, any suitable algorithm may be used to determine the difficulty of the mobile robot traveling at each location in the target area. And may set an appropriate threshold to determine the traffic attribute for each location. For example, the pass difficulty value may be any value between [0,1], the pass attribute of the position where the pass difficulty value is 0 may be determined to be passable, the pass attribute of the position where the pass difficulty value is 1 may be determined to be non-passable, and the pass attribute of the position where the pass difficulty value falls within the (0, 1) interval may be determined to be potentially passable.

For example, in the initial state, i.e. before the movable robot does not perform any detection of the target area, the traffic properties of the respective positions of the entire target area may be defaulted to be trafficable. In other words, each location in the target area is globally navigable by default in the initial state. Then, as the movable robot moves in the target area, various sensors are utilized to collect data of obstacles in the target area, and the traffic attribute of each position in the target area can be updated according to the robot behavior information and the obstacle data. For example, if it is determined from obstacle information and/or robot behavior that a certain location meets an impassable condition, the pass attribute of the location may be updated to impassable; if it is determined that the location meets the potentially passable condition based on the obstacle information and/or robot behavior, the passable attribute of the location may be updated to be potentially passable. For example, before the movable robot performs the cleaning task, or during the process of performing the cleaning task, the passing attribute of each position in the target area may be updated according to the information of the obstacle collected by the sensor in real time, the information of the obstacle identified by the deep learning model in real time, the real-time behavior information of the movable robot, and the forbidden zone update information. Taking a sweeping robot as an example, the following update policies may be employed to update traffic attributes for various locations in the target area. 1) For a location set as default passable, the passable attribute of the location may be updated according to actual conditions. For example, for a location where no obstacle is identified by various types of sensors, if the current location meets the agile updating condition, a traffic attribute of the location may be determined as being trafficable; if the current location meets the lazy update condition, the traffic attribute for the location may be determined to be non-traffic. Where agile updates represent direct updates that do not take other conditions into account once triggered. Lazy update means that after triggering the condition, other conditions are checked for conflicts and no conflicts are updated. 2) And (5) the pass attribute of the position of the exclusion zone is updated to be unvented. 3) The traffic attribute of the position of the erased obstacle is updated to be passable. 4) And updating the passing attribute of the confirmed obstacle boundary to be non-passing. 5) The traffic attribute of the location where the uncertain obstacle is located is updated to be potentially trafficable. 6) The first particular object identified by the deep learning model is updated to be unvented. The first specific object may be an item that is significantly detrimental to the user experience. Such as pet faeces, electrical wiring on the ground, etc. 7) The second particular object identified by the deep learning model is updated to be potentially passable. The second specific object may be a small paper lump or the like. 8) For a position with a traffic attribute of potential traffic, if the robot tries to pass through, confirming that the position is traffic, the traffic attribute of the position can be updated to be traffic; if the sweeping robot tries to pass and confirms that the position is not passable, the passing attribute of the position can be updated to be not passable. The update policy of the traffic attribute may also have priority when executed, for example. For example, if the plurality of update policies are triggered at the same time at the same position, the update policies with the preceding sequence numbers may be preferentially executed in the order of the policy sequence numbers.

In step S1400, the target area is divided into areas according to the traffic attribute of each location, so as to generate a second map of the target area. The second map comprises a passable area, an unviewable area and a potential passable area.

The second map is a map capable of expressing traffic attributes of respective positions in the target area. For simplicity, the second map may be referred to as a passable map. It will be appreciated that the passable map describes objective information about the location of the target area, the information in the passable map being related to the actual terrain environment of the target area. Any suitable method may be used to generate the passable map. In the passable map, the display style of the regions having the same passable attribute may be the same, and the display style of the regions having different passable attributes may be different. As previously described, the traffic attributes may include at least trafficable, potentially trafficable, and non-trafficable. Thus, the passable map may include at least three regions of different display styles: passable zones, potentially passable zones, and non-passable zones. The passable zone may include any area where there is no risk of passing, such as an area where no obstacle is present. Some default passable areas may also be included. For example, the area under a table and chair, etc. The non-passable zone may include a restricted zone set by the user; fixed obstacle boundaries, including, for example, wall boundaries, tall cliff boundaries, tall obstacle boundaries, and short space boundaries such as under tea tables and beds, etc.; the deep learning model identifies the area where the items that are significantly harmful to the user experience are located, including, for example, pet faeces and electrical wiring on the ground. The potentially passable area may include areas where low obstructions such as socks, slippers, sills, etc. are located; the area where hanging obstacles such as curtains and the like are located; the region where the dummy gate is located; the region where a specific object, such as a small paper cluster, is located, etc., as identified by the deep learning model. Of course, for the case where the traffic attribute also includes other subdivided attributes, more regions with different display styles may also be included in the passable map. Those skilled in the art will appreciate various extensions of this scheme, and are not described in detail herein.

Fig. 2 shows a schematic diagram of a second map according to one embodiment of the present application. As shown, the target area may be a full house area of a user's home of the sweeping robot. The pixels of the passable, potential passable, and non-passable regions in the full-house area shown in the figure differ in pixel values. For example, the pixel value of each pixel in the passable region is 259, the pixel value of each pixel in the potential passable region is 137, and the pixel value of each pixel in the non-passable region is 172. In the second map, the non-passable area includes a restricted area set by a user, a wall position, a closed door position, a floor wardrobe boundary, a tea table boundary, a television cabinet boundary, a box bed boundary, an area where a base station of the sweeping robot is located, a slipper area in the middle of a living room, and positions where table legs and chair legs are located. The potential passable area includes the location of the curtains, the location of the virtual doors, the location of the steps, and the area of the slippers at the entrance to the bedroom. The passable zone is a region other than the passable zone and the potential passable zone.

Step S1600, determining a traffic strategy of the movable robot for executing the current processing task according to the second map, and controlling the movable robot to execute the current processing task according to the traffic strategy. Wherein the traffic policy for the potential trafficable region includes an attempted traffic policy.

After the second map is determined, a traffic policy for the mobile robot to execute the current processing task may be determined at least according to traffic attributes of each region in the second map. And can control the mobile robot to execute the current processing task according to the determined traffic strategy.

In one example, the reachable properties from the current location of the robot to each location in the target area may be determined based on the navigable map and the current location of the robot, and then a traffic policy for the mobile robot to perform the current processing task may be determined based on the determined reachable properties. Specifically, the first position where the movable robot is currently located may be acquired first. Then, the reachable properties of the mobile robot from the first location to each location in the target area may be determined from the traffic properties of each location in the passable map. For example, it may be determined that the mobile robot starts from the first position, determines the reachable position, determines the unreachable position, and the possible reachable position, etc. Then, according to the position and the first position of the area to be processed, which is required to be processed by the movable robot, in the current processing task and the determined reachable attributes of the movable robot from the first position to each position in the target area, a traffic strategy of the movable robot for executing the current processing task can be further determined. It will be appreciated that the area to be processed is the area where the mobile robot is currently required to perform processing operations. The region to be processed may be the entire target region or may be a local region in the target region. Taking a sweeping robot as an example, the target area may be a full-house floor area in a user's home. For the case that the current cleaning task of the sweeping robot is a full house sweeping task, the area to be cleaned may be a full house floor area. In the case where the current cleaning task for the robot is a cleaning task for a bedroom, for example, the area to be cleaned may be the area in which the bedroom is located. The traffic policy may include, for example, a traffic policy during movement of the movable robot from the first position to the area to be treated, movement in the area to be treated, treatment of the area to be treated, and the like. Alternatively, in some examples, the traffic policy may further include a traffic policy in which the mobile robot returns to a specific location such as a base station after the processing task is performed on the area to be processed.

In another example, the reachable attribute from the current position of the robot to each position in the target area may not be determined, and after the first position where the movable robot is currently located is acquired, a traffic policy of the movable robot for executing the current processing task may be directly determined according to the position of the area to be processed in the current processing task, the first position, and the traffic attribute of each position in the traffic map. Taking the example that the movable robot is not currently in the target area, it can be understood that the movable robot performs the current processing task including a navigation task to move from the current position to the area to be processed and an operation task to process the area to be processed. Taking the example of determining a traffic policy in the mobile robot performing the navigation task, the traffic policy in the mobile robot performing the navigation task includes a determination policy of the navigation path. In one example, the plurality of movement paths from the first location to the area to be processed may be determined using any suitable path planning algorithm based on the positional relationship between the first location in the target area where the robot is currently located and the location of the area to be processed, regardless of the traffic attributes of the respective locations in the traffic map. Then, the traffic attribute of each location passed through in each moving path may be determined from the passable map. It will be appreciated that in such an example, an invalid movement path may be included in the determined plurality of movement paths that may not actually be available. Thus, invalid movement paths through the non-trafficable region may be filtered out, resulting in valid movement paths through the trafficable region and/or the potentially trafficable region. Then, various suitable sorting methods may be employed to sort the remaining individual travel paths according to the traffic attributes of the individual locations traversed in these effective travel paths. For example, for each travel path, the more the number of potentially trafficable regions the travel path passes through, the later the travel path is ordered. Alternatively, in another example, a plurality of effective movement paths of the movable robot from the first position to avoid the non-passable area to the area to be processed may also be directly determined according to a positional relationship between the first position where the movable robot is currently located and the position of the area to be processed, in combination with the passable map. Then, the optimum navigation path can be determined by adopting proper judgment logic according to the traffic attribute of each position where each moving path passes through. For example, it may be first determined whether there is a movement path that does not pass through a potentially passable zone among the plurality of effective movement paths. If so, the movement path can be used directly as a navigation path for the mobile robot to reach the area to be processed. If not, the movement path with the least number of potential passable areas being passed through can be determined as the navigation path of the movable robot to the area to be processed from the rest of the movement paths. Alternatively, in this example, the traffic difficulty value of each moving path may be calculated directly from the traffic difficulty value of each area through which each moving path passes. And the moving path corresponding to the calculated minimum traffic difficulty value can be determined as the navigation path of the movable robot reaching the area to be processed.

Similarly, the processing path of the movable robot when the processing task is executed in the area to be processed may be determined according to the method described above, and the return path of the movable robot to the specific position such as the base station after the processing task is executed may be determined.

According to the embodiment of the application, in the process that the movable robot executes the current processing task, different traffic strategies can be adopted for areas with different traffic attributes. Take a sweeping robot as an example. For the case that the area to be cleaned of the current cleaning task and the navigation area from the first position where the sweeping robot is currently located to the area to be cleaned comprise a passable area, a passable policy may be adopted to pass through the passable area. The mobile robot, when passing through these passable zones, may pass through the zone without risk or perform processing operations, including for example performing arcuate coverage tasks, navigation tasks or edge-following tasks, etc. Specifically, any suitable coverage pass algorithm may be employed to pass through the area. For example, arcuate coverage traffic algorithms, edgewise coverage traffic algorithms, arcuate coverage traffic algorithms, path following coverage traffic algorithms, and the like may be used. For example, different coverage traffic algorithms may be employed through the navigation area and the traffic zone in the area to be cleaned; for different cleaning modes, different coverage traffic algorithms can also be used to pass through the passable zone in the area to be cleaned. For the case where the navigation area and the area to be cleaned include an unvented area, an evading traffic policy may be employed to avoid the unvented area. In the case that the navigation area and the area to be cleaned comprise potential passable areas, as the passage of the sweeping robot in the area is at risk, the trafficability of the sweeping robot needs to be tested, and a passing strategy for attempting to pass can be adopted to pass through the area under the condition that preset conditions are met. For example, for a potentially passable area where low-level obstacles such as socks and slippers are located, an attempted pass strategy such as bumping open the obstacles may be employed through the area where the current cleaning mode is a deep cleaning mode to ensure cleaning effectiveness and reduce instances of missed cleaning. For a potentially passable area where the threshold is located, further features of the threshold may be identified, for example, the height of the threshold may be identified, and an attempted pass strategy may be employed to pass the threshold if the height of the threshold is below a height threshold. For example, the passage may be cleared by performing an obstacle crossing operation. For a potentially passable zone such as a curtain, an attempted pass strategy such as crashing open an obstacle may also be employed through the zone, depending on the current cleaning mode. The potentially passable area for some special carpets and the like may also be passed through the area using an attempted pass strategy such as lifting the mop, based on a deep cleaning pattern. For potential passable areas such as the area where the virtual-masked gate is located, narrow lanes, etc., an attempted pass strategy such as edge passes, over-narrow lanes, etc., may also be employed to pass through the area based on the depth cleaning mode.

As described above, the accuracy of the passable map established by the mobile robot in the prior art is poor, resulting in low efficiency or poor processing effect of the mobile robot in performing the processing task. Take a sweeping robot as an example. Some sweeping robots with low sensor precision cannot effectively identify the positions of some specific obstacles, so that the actually unperctable areas can be misjudged as the passable areas. For example, for robots that include only lidar sensors, the lidar sensors are typically located on top of the mobile robot, and thus may not be able to effectively detect low obstacles, such as weight scales, etc., that are below the height of the robot. Thus, the sweeping robot will constantly try to pass through the area while sweeping the area. This not only results in unnecessary computations, but also extends the cleaning time. The obstacle can be accurately identified for some sweeping robots with higher sensor precision, but the area where the identified obstacle is located is usually simply determined as an impassable area. For example, the areas of the obstacle such as slippers and curtains are all determined as the non-passable area. In this way, the sweeping robot can avoid without trying when walking to the areas. Thus, the cleaning effect is poor. For example, the doorway of a room to be cleaned is blocked by a pair of slippers, and the sweeping robot avoids the slipper area and cannot enter the room to be cleaned to clean and returns to the base station directly. In this case, a large area of the room to be cleaned is left to leak, and the cleaning effect is poor.

According to the control method of the embodiment of the application, the passing attribute of each position in the target area is accurately determined according to the determined obstacle map of the target area. And precisely dividing the target area into a passable area, an unviewable area and a potential passable area according to the passing attribute. Finally, a traffic policy of the mobile robot for executing the current processing task is determined according to the generated second map of the area comprising the three traffic attributes. And, since the determined traffic policy for the potentially trafficable region includes an attempt traffic policy, traffic may be intelligently attempted according to the actual needs of the user while walking to such region when performing processing tasks. Therefore, the processing effect of the current processing task can be obviously improved, and the user experience is better. Meanwhile, as the determined passing attribute of each position is higher in precision, unnecessary behaviors and invalid operations of the movable robot in executing processing tasks can be avoided, so that the behaviors of the movable robot are more intelligent. The precise control of the movable robot is realized.

Illustratively, step S1600 determines a traffic policy for the mobile robot to perform the current processing task according to the second map, including step S1610 and step S1620.

Step S1610, determining the reachable attribute of each sub-area in the target area according to the second map.

The subareas in the target area can be areas which are segmented according to actual requirements. If the area of the target area is large and there are a plurality of partition areas, the entire target area may be divided into a plurality of sub-areas according to an area division algorithm, and each sub-area may correspond to one partition area. One sub-area or a plurality of sub-areas may be included in the area to be processed in the current processing task of the mobile robot. Alternatively, the region to be processed may include only a partial region in a certain sub-region. Taking the target area shown in fig. 2 as an example, a whole house area in a user's home has a plurality of rooms, and different rooms may be separated by a door, a threshold, a step, a curtain, and the like. Thus, a region division algorithm may be employed to divide the full house region into 13 sub-regions as shown in the figure. It will be appreciated that the 13 sub-regions shown in the figures correspond to one partition region, except that the sub-region designated 12 corresponds to a low space under the tea table, the remaining 11 sub-regions. It will be appreciated that the travel of a mobile robot within a partition area is generally only affected by the traffic attributes of various locations within the area. But also by the traffic properties of the various positions involved in its movement from the starting position to the area to be treated when it is desired that the movable robot performs a processing task from one partition area to another. In particular by the passage properties of the doorway position of the through-going partition area. For example, when the sweeping robot in fig. 2 moves from the sub-area denoted by 1 to the sub-area denoted by 2 and the sub-area denoted by 8 to perform a sweeping task, it is also limited by the traffic attribute of the entry positions of the sub-area denoted by 2 and the sub-area denoted by 8. In other words, the passing properties of the necessary doors, thresholds, steps, etc. affect the performance of their cleaning tasks when the sweeping robot moves from one room to another.

According to the embodiment of the application, in order to more accurately control the movable robot to execute the processing task, the reachable attribute of the movable robot from the current position to each position in the target area is further determined on the basis of determining the passing attribute of each position in the target area. Unlike the traffic attribute, the reachable attribute represents position communication information after associating the current position of the movable robot. The reachability attribute is closely related not only to the terrain environment of each location, but also to the location where the mobile robot is currently located. In particular, the reachability attribute indicates whether the movable machine can reach the corresponding area from the current location and the cost of reaching the corresponding area. The reachability attribute may indicate which navigable areas the mobile robot starts with under the current location are connected from the perspective of the entire target area.

The classification definition of the reachable attributes can also be set according to actual requirements, and the classification definition is not limited in the application. According to embodiments of the present application, reachable properties may include at least three properties of reachable, unreachable, and potentially reachable. Of course, the potential reachability may be further divided into a first potential reachability, a second potential reachability, and a third potential reachability attribute according to the level of the reachable difficulty level. For example, for an reachable property belonging to a first potentially reachable sub-region, the difficulty of the mobile robot reaching the region from the first location is lower; for the reachable attribute belonging to the second potentially reachable sub-region, the difficulty of the mobile robot reaching the region from the first position is moderate; for the reachable properties belonging to the third potentially reachable sub-area, the difficulty of the mobile robot to reach the area from the first position is higher.

In this step, the reachable properties of the mobile robot moving from the current position to each sub-area in the target area may be determined using any suitable method based on the determined passable map. By way of example and not limitation, the reachable properties between every two sub-regions in the target region may be determined based on the relative positional relationship of the sub-regions and the traffic properties of the different locations of the sub-regions. Then, a first position of the movable robot may be input, and reachable properties between the sub-region where the first position is located and other sub-regions in the target region are determined.

According to the embodiment of the application, for any two sub-regions in the target region, for example, sub-region a and sub-region B, the reachable property moving from sub-region a to sub-region B and the reachable property moving from sub-region B to sub-region a may be the same or different. Specifically, taking the determination of the reachable attribute from the sub-area a to the sub-area B as an example, the path from the sub-area a to the sub-area B may be determined according to the relative positional relationship of the two sub-areas. For example, the subarea a and the subarea B are adjacent in position, and the entrance of the subarea B is communicated with the subarea a, and then the reachable attribute from the subarea a to the subarea B can be determined directly according to the traffic attributes of the two subareas. For example, if all the passable areas in the subarea a are connected, it may be determined that the passable attribute of the subarea a is passable. The traffic attribute of sub-region B may be determined from the traffic attribute at the entrance of sub-region B. For example, if the traffic attribute at the entrance of the sub-region B is passable, it may be determined that the traffic attribute of the sub-region B is passable. Further, if the traffic attributes of both sub-areas are trafficable, it may be determined that the reachable attribute of the movable robot from sub-area a to sub-area B is reachable. It is noted that in some specific examples, a sub-area may also have multiple entrances and exits, such that the passable properties of a sub-area may be different with respect to different sub-areas adjacent to its location, but the principle of determining between different sub-areas is similar. If the positions of the subarea A and the subarea B are not adjacent, the reachable attribute from the subarea A to the subarea B can be comprehensively determined according to the traffic attribute of each subarea involved in the path from the subarea A to the subarea B. For example, the path from sub-region a to sub-region B may be divided into a plurality of segmented paths, each of which involves only two sub-regions adjacent to each other. For example, the traffic attribute between two adjacent sub-regions involved in each segment path may be determined first, and then the reachable attribute from sub-region a to sub-region B may be determined based on the traffic attribute between the plurality of adjacent sub-regions determined by the plurality of segment paths, respectively. For example, if the sub-area a needs to reach the sub-area B via the sub-area C, the traffic attribute from the sub-area a to the sub-area C and the traffic attribute from the sub-area C to the sub-area B may be determined by a similar method as described above, and any suitable determination logic may be used to determine the reachable attribute from the sub-area a to the sub-area B according to the determined two traffic attributes. In one example, the determination logic may include an AND or NOR logic determination or the like. In another example, for determining the reachable properties between two sub-areas with adjacent positions, the traffic difficulty value of the movable robot moving from one sub-area to another sub-area may also be calculated from the traffic difficulty value of at least part of the movable robot in each sub-area. The reachable properties may then be determined based on the determined traffic difficulty value and a preset plurality of thresholds. For determining the reachable attribute between two non-adjacent subregions, a method for dividing the segmented paths can be adopted, the passing difficulty value between two adjacent subregions of each segmented path is determined, then mathematical operation can be carried out on a plurality of obtained passing difficulty values, and then the reachable attribute is determined according to the passing difficulty value obtained after calculation and a plurality of preset thresholds.

After determining the reachable properties between every two sub-areas in the target area, the sub-area where the first position is located, for example called the start sub-area, may be determined from the first position of the movable robot. In the case of communication of the passable zone in the starting point sub-zone, the reachable property of the movable robot from the first position to the starting point sub-zone may be determined as reachable directly. Further, the reachable property between each sub-region that moves from the start sub-region to outside the start sub-region in the target region can be determined.

Step S1620, determining the traffic strategy of the movable robot for executing the current processing task according to the reachable attribute of each sub-area. Wherein the reachability attribute of a sub-region represents the reachability of the movable robot from the first position where it is currently located to the sub-region. The first sub-region is reachable, the second sub-region is potentially reachable, and the third sub-region is unreachable. The path from the first location to the first sub-area comprises a first path via the passable zone only. The path from the first location to the second sub-area includes a second path via the potentially passable area but not via the non-passable area and does not include the first path. The path from the first location to the third sub-area does not include the first path and the second path.

For example, a first sub-region may be referred to as a reachable region, a second sub-region as a potentially reachable region, and a third sub-region as a non-reachable region. Illustratively, after determining the reachable property of each sub-area in the target area by the movable robot moving from the first position by using the method of step S1610, each sub-area in the target area may be further marked, for example, a sub-area having a reachable property may be marked as a reachable area, a sub-area having a reachable property may be marked as a potentially reachable area, and a sub-area having a reachable property may be marked as a non-reachable area. It is understood that the path from the first location to the reach area includes a first path through only the passable area. In other words, there is a communicated passable zone from the first location to the accessible zone, so that the mobile robot can move to the accessible zone through the communicated passable zone. The path from the first location to the potentially reachable area includes a second path via the potentially reachable area but not via the non-reachable area and does not include the first path. In other words, there is a requisite potential passable zone from the first location to the potential accessible zone. The path from the first location to the non-passable zone does not include the first path and the second path. In other words, there is a must-pass non-passable zone from the first location to the non-passable zone.

According to the embodiment of the application, after the reachable attribute of each sub-area is determined, the traffic strategy of the movable robot for executing the current processing task is also determined according to the reachable attribute. Specifically, a region to be processed of the current processing task may be determined, and a navigation region in which the movable robot moves from the first position to the region to be processed may be determined according to a relative positional relationship between the first position and the region to be processed in the target region. The traffic policy may then be determined from the reachable properties of the respective sub-areas involved in the navigation area and the area to be processed. In particular, different traffic policies may be determined for sub-areas with different reachability attributes. For example, if the area to be processed comprises only one sub-area and the reachable properties of that sub-area are reachable, a fast decision can be made, using e.g. a default traffic policy or a traffic risk free policy, controlling the mobile robot to move fast in the communicating trafficable area within the navigation area until the area to be processed is reached. The mobile robot is then controlled to move in the traversable area within the area to be treated. In particular, the movement path in the navigation area may be the shortest movement path from the first position to the area to be processed, and the movement path or processing path in the area to be processed may be a full coverage path. If the region to be processed comprises only one sub-region and the reachability attribute of the sub-region is not reachable, a message such as "region to be processed is not reachable-! "prompt message to the user. After the user has clear the obstacle or has carried the robot to the proper position, the user re-initiates the processing instruction. If the area to be treated includes only one sub-area, and the accessibility attribute of the sub-area is potentially accessible, the mobile robot may be controlled to move to the area to be treated using a preset condition such as an attempt to pass policy based on a user instruction or based on a current cleaning mode, and after the mobile robot successfully reaches the area to be treated by attempting to pass, the area to be treated (or the potentially accessible area may be included) may be treated in the area to be treated using a predetermined passable policy, for example, according to various suitable full coverage paths.

It is understood that the scenes that the movable robot needs to process are mostly dynamic scenes in which the position or posture of the obstacle in the target area often changes. Taking a home sweeping robot as an example, a user's home door often changes between open, closed, or unmasked states. Obstacles such as garbage cans, table chairs, slippers, etc. in the user's home may also often be moved to different positions. In the prior art, the movable robot only establishes a passable map with poor precision, and the accessibility of each subarea in the target area in the dynamic scene cannot be accurately obtained, so that the passable strategy cannot be flexibly adjusted in real time according to the dynamic scene, and the processing efficiency and the processing effect of the movable robot are poor. According to the control method of the movable robot, on the basis of providing the passable map, the accessible, inaccessible and potentially accessible areas of the movable robot from the current position can be determined according to the position of the robot in real time, so that the passing strategy can be flexibly adjusted according to the dynamic scene. The control efficiency and the accuracy of the method are high. The user experience is also better.

In the above scheme, the passable map can be updated in real time according to the latest information of the obstacle and the robot behavior acquired in real time by the sensor of the sweeping robot in the process of executing the cleaning task by the sweeping robot, and the accessible attribute from the position of the robot to each position in the target area can be acquired in real time according to the position of the robot in the moving process of the sweeping robot, so that quick response can be realized according to the accessible attribute, different passing strategies and cleaning strategies can be determined in real time according to dynamic scenes, and high-efficiency and accurate control of the sweeping robot can be realized.

Illustratively, step S1610 determines the reachable properties of each sub-region in the target region according to the second map, including step S1611 and step S1612.

Step S1611, determining a first topological graph according to the positions of all sub-areas and the traffic attribute of all sub-areas in the second map. The first topology map may be determined by any suitable topology algorithm, which is not limited in this application, as long as the determined first topology map can accurately represent the relative positional relationship between the sub-areas and the traffic attribute of the sub-areas. For example, the first topology may be a topology structure diagram in which a plurality of nodes are connected. The plurality of nodes may be in one-to-one correspondence with respective sub-regions in the target region. The characteristics of the nodes corresponding to the subregions with different traffic attributes are different and/or the characteristics of the connecting lines between two nodes corresponding to the adjacent subregions with different traffic attributes are different. The node connection may represent a traffic attribute between the sub-regions to which two nodes correspond. For example, the color or shape of the nodes corresponding to the subregions of different traffic attributes are different. In this way, the traffic attribute may be represented by characteristics of the node and/or characteristics of the node connection. The node connection may have a direction, which may represent a direction of movement of the movable robot.

In step S1612, the reachable attribute of the mobile robot moving from the first location to each sub-area in the target area is determined according to the first location where the mobile robot is currently located and the first topology map. Take different characteristics of nodes with different traffic attributes as an example. A node in the first topology map corresponding to the sub-region where the first location is located may be determined. Then, starting from the node, each node between each other node and the node can be searched, and according to the characteristics of the nodes, through various suitable judging logics, the reachable attribute between the movable robot moving from the first position to the sub-region corresponding to each other node can be determined. Currently, the reachable properties of the mobile robot moving from the first position to each sub-area can also be determined according to the characteristics of the node connection. Specific examples of such schemes will be explained later, and are not described here.

In the scheme, the topology graph is utilized to express the traffic attribute among the unnecessary subareas in the target area, and the reachable attribute from the robot position to each subarea in the target area is determined through the topology graph and the position of the robot. The method has small calculated amount, and the traffic path and the traffic attribute can be intuitively and accurately expressed through the topological graph, so that the reachable attribute from the robot position to each subarea can be rapidly and accurately determined.

Illustratively, step S1611 determines a first topology map according to the location of each sub-region and the traffic attribute of each sub-region in the second map, including steps S1611a and S1611b.

In step S1611a, the topology of the first topology map is determined according to the positions of the sub-regions in the second map. The first topological graph is a directed acyclic graph of a plurality of nodes connected by directed edges, and each node in the first topological graph uniquely corresponds to one sub-area in the second map.

Fig. 3 shows a schematic diagram of a first topology according to an embodiment of the present application. The map may be regarded as a first topology map determined by the passable map shown in fig. 2. As shown, 13 nodes in the graph correspond one-to-one with 13 sub-regions in fig. 2. The relative positional relationship of each node in the graph is the same as the relative positional relationship of 13 sub-areas. For example, the sub-region numbered 1 (for simplicity referred to as sub-region 1) is located adjacent to sub-region 2, sub-region 3, sub-region 4, sub-region 5, sub-region 6 and sub-region 10, respectively, and node 1 is located adjacent to node 2, node 3, node 4, node 5, node 6 and node 10.

Step S1611b, for each directed edge, determining a characteristic of the directed edge according to the traffic attribute of the first terminal area and the second terminal area corresponding to the two nodes connected in sequence by the directed edge. Wherein the characteristic of the directed edge represents a traffic attribute from the first terminal area to the second terminal area. The differently characterized directed edges in the first topology are represented in different styles. Adjacent nodes in the first topological graph are connected through directed edges. The feature of the directed edge represents a traffic attribute between the two regions. The characteristics of the directed edges representing different traffic attributes are different. For example, different styles of directed edges represent different traffic attributes, e.g., the color or line type of the directed edge may be different. In one example, for the case where the traffic attribute between the sub-regions to which two nodes correspond is traffic or potentially traffic, the two nodes may be connected by a directed edge. For the case that the traffic attribute between the sub-areas corresponding to the two nodes is non-traffic, the two nodes may not be connected, i.e. the connection relationship is not represented. This solution is more intuitive and simpler to calculate. As shown in fig. 3, the solid and dashed lines with arrows indicate that the traffic attribute between adjacent areas is trafficable and potentially trafficable, respectively. For example, the nodes 1 and 6 are connected by a solid line directional edge with an arrow. And node 6 and node 12 are not connected because the space between sub-area 6 and sub-area 10 is blocked by an impenetrable wall. In another example, the directional edge representing non-passable may also be represented by other lines, such as wavy lines, as long as differentiation is facilitated.

Step S1612 determines, according to the first location where the movable robot is currently located and the first topology map, the reachable attribute of each sub-area in the target area, which is moved from the first location where the movable robot is currently located, including step S1612a and step S1612b.

Step S1612a, determining the reachable attribute of the movable robot from the first position to the start sub-region according to the traffic attribute of the start sub-region where the first position is located. As shown in fig. 2, the first position is exemplified in a sub-area 1 representing a living room. The starting sub-area is sub-area 1, and since the accessible areas in sub-area 1 are communicated, the reachable attribute from the first position to sub-area 1 can be directly determined as reachable.

Step S1612b, traversing each second node except the first node corresponding to the start sub-region in the first topological graph, and determining the reachable attribute of the movable robot moving from the first position to the end sub-region corresponding to each second node according to the characteristics of each directed edge between the first node and the second node. For example, the starting sub-region is sub-region 1 in fig. 2, each other node except node 1 in the topology graph in fig. 3 may be traversed, and the reachable attribute of the movable robot moving from the first position to the end sub-region corresponding to each other node is determined according to the characteristics of the directed edge between node 1 and each other node. As shown in fig. 3, since there is only one directed edge between the nodes 1 and 6, and the directed edge is a solid line directed edge, the passage attribute from the sub-area 1 to the sub-area 6 may be determined to be passable, and the reachable attribute from the sub-area 1 to the sub-area 6 may be determined to be reachable directly from the passage attribute. It will be appreciated that for some cases there may be other sub-areas between the two sub-areas that are necessary, i.e. an indirect connection between the two sub-areas is possible. For example, in case of an open door between sub-area 1 and sub-area 2, the directed edge between node 1 and node 2 may be a solid line directed edge, and thus for determining the reachable property moving from sub-area 1 to sub-area 11, the reachable property of the movable robot moving from the first position in sub-area 1 to sub-area 11 may be determined from the characteristics of the directed edge between node 1 and node 2 and the characteristics of the directed edge between node 2 and node 11. In particular, any suitable determination method may be employed to determine the reachable properties from the characteristics of the two directed edges. As previously described, the reachable properties may be determined using logical judgment, mathematical operations, and the like.

In the scheme, the relative position relation of each subarea in the target area can be intuitively and accurately represented through the first topological graph, and the traffic attribute between adjacent subareas can be accurately represented. And converting the problem of determining the reachable property of the movable robot from the first position to each sub-region in the target region into the problem of determining the reachable property from the first position to the starting sub-region and the reachable property from the starting sub-region to other sub-regions. And the reachable properties of each sub-region can be rapidly and accurately determined through the characteristics of the directed edges in the first topological graph. The scheme has simple and reasonable execution logic, small calculation amount and higher calculation speed and calculation precision. Therefore, accurate control of the movable robot is achieved, and user experience is good.

Illustratively, step S1611b determines the characteristics of the directed edge according to the traffic attributes of the first terminal area and the second terminal area corresponding to the two nodes connected in sequence by the directed edge, including step S1611b.1 and step S1611b.2.

Step s1611b.1, determining a third pass attribute from the first terminal area to the second terminal area according to the first pass attribute of the first terminal area and the second pass attribute of the second terminal area. Wherein, for the case that the first pass attribute is passable, the third pass attribute is determined to be the same as the second pass attribute. As previously described, for two sub-regions that are adjacent in position, sub-region a and sub-region B. The traffic attribute of the sub-area a corresponding to the node corresponding to the start of the directed edge may be first determined. If the passable areas in the subarea a are all connected, it may be determined that the passable attribute of the subarea a is passable. The traffic properties of sub-region B may then be determined. The traffic attribute of sub-region B may be determined from the traffic attribute at the entrance of sub-region B. For example, if the traffic attribute at the entrance of the sub-region B is passable, it may be determined that the traffic attribute of the sub-region B is passable; if the traffic attribute at the entrance of the subarea B is non-traffic, determining that the traffic attribute of the subarea B is non-traffic; if the traffic attribute at the entrance of sub-region B is potentially trafficable, it may be determined that the traffic attribute of sub-region B is potentially trafficable. The traffic attribute for moving from sub-area a to sub-area B may then be determined from the traffic attribute for sub-area B. And under the condition that the traffic attribute of the subarea A is passable, determining that the traffic attribute of the subarea A moving to the subarea B is the same as the traffic attribute of the subarea B.

And S1611b.2, determining the characteristics of the directed edge according to the third line attribute. For example, if the third pass attribute is determined to be passable, it may be determined that the directed edge between the respective two nodes is a solid line with an arrow; if the third pass attribute is determined to be non-passable, determining that no directed edge exists between the corresponding two nodes; if the third pass attribute is determined to be potentially passable, a directional edge between the respective two nodes may be determined to be a dashed line with an arrow.

The method for determining the characteristics of the directed edges is simple and reasonable, and the calculated amount is small.

Illustratively, step S1612b determines the reachable attribute of the movable robot moving from the first position to the destination sub-region corresponding to each second node according to the characteristics of each directed edge between the first node and the second node, including steps s1612b.1, s1612b.2, and s1612b.3.

Step s1612b.1, for each second node, determining that, in at least one path formed by iterative connection of directed edges from a first node to the second node, if there is a first path including only a first directed edge, an reachable property of the movable robot moving from the first position to an end sub-region corresponding to the second node is reachable. Wherein the traffic attribute represented by the characteristic of the first directed edge is traffic-enabled.

Step s1612b.2, for each second node, determining that the reachable property of the movable robot moving from the first position to the end sub-region corresponding to the second node is potentially reachable if there is no first path but there is a second path including only the first directed edge and the second directed edge in at least one path formed by iterative connection of directed edges from the first node to the second node. Wherein the traffic attribute represented by the feature of the second directed edge is potentially passable.

Step s1612b.3, for each second node, determining that the reachable attribute of the movable robot moving from the first position to the endpoint sub-region corresponding to the second node is unreachable if the first path and the second path do not exist in at least one path formed by iterative connection of directed edges from the first node to the second node.

Fig. 4 shows a schematic diagram of a first topology according to another embodiment of the present application. As shown, taking the first position in the sub-area 1 as an example, in step s1612b.1, it may be determined that the reachable properties of the movable robot moving from the first position to the sub-areas corresponding to the node 6 and the node 2, respectively, which are iteratively adjacent to the node 1, are reachable. In step s1612b.2, it is determined that the reachable properties of the movable robot moving from the first position to the sub-areas respectively corresponding to node 3, node 4, node 5, node 10, node 8 and node 11 are all potentially reachable. In step s1612b.3, it is determined that the reachable properties of the movable robot moving from the first position to the sub-areas respectively corresponding to node 0, node 9, node 7 and node 12 are all unreachable.

In the scheme, the reachable attribute of the robot from the first position to each sub-region is rapidly and accurately determined by carrying out logic judgment on the characteristics of the directed edge in each path between the first node corresponding to the starting point sub-region where the robot is located and each other second node. The logic judgment scheme is simple, the calculated amount is small, and the accuracy is high, so that the real-time and accurate control of the movable robot can be realized.

The directional edge includes a second directional edge, and the traffic attributes of the first terminal area and the second terminal area corresponding to the two nodes connected by the second directional edge are respectively traffic and potential traffic. Step S1611 determines a first topological graph according to the position of each sub-region in the second map and the traffic attribute of each sub-region, and further includes step S1611c and step S1611d. Step S1611c, for each second directed edge, determining a traffic difficulty value for the movable robot to move from the first terminal area to the second terminal area according to the first information about the second terminal area in the first map and the behavior information of the movable robot. Step S1611d, determining the weight information of the second directed edge according to the traffic difficulty value.

According to an embodiment of the application, the traffic difficulty value is a scalar representing the size of the travel cost of the movable robot moving from the first terminal area to the second terminal area. The greater the pass difficulty value, the greater the difficulty level of the robot to walk, and the greater the possibility of controlling the pass of the robot to present abnormal risks such as trapped and slipping.

According to the embodiment of the application, the passing difficulty from one sub-area to another sub-area can be expressed in the first topological graph by the type of the directed edges between the nodes. The weight information of the directed edge may be a type of directed edge. For example, a traffic difficulty value of the movable robot moving from the first terminal area to the second terminal area may be obtained by using various suitable traffic difficulty value calculation methods according to the first information, and the traffic difficulty value may be set as the type information of the second directed edge. Illustratively, the pass difficulty value may be a value between (0, 1). The closer the pass difficulty value is to 1, the greater the travelling cost of the movable robot moving from the first terminal area to the second terminal area is indicated. The closer the pass difficulty value is to 0, the smaller the travelling cost of the movable robot moving from the first terminal area to the second terminal area is.

Reference is again made to fig. 2 and 3. For example, for the second directed edge connecting node 1 and node 4. The difficulty of passage value of the mobile robot moving from sub-area 1 to sub-area 4 may be determined from the obstacle map and the first information about the obstacle within the potential passable zone at the entrance of sub-area 4 in the behavior information of the mobile robot. The first information may include type information and form information of the obstacle at the position, material information, terrain information in the vicinity of the obstacle, and action and result information of the trial pass that the robot has performed. For example, a function for calculating the traffic difficulty value may be constructed in advance from various information in the first information. In this step, a traffic difficulty value may be calculated from the constructed function. And the pass difficulty value can be used as the type information of the corresponding second directed edge. For example, for two adjacent sub-regions, such as sub-region a and sub-region B, the traffic difficulty value from sub-region a to sub-region B may be the same as or different from the traffic difficulty value from sub-region B to sub-region a. For example, since there is a downward step in the sub-region 11 in fig. 2, the difficulty value of passage moving from the sub-region 2 to the sub-region 11 is smaller than the difficulty value of passage moving from the sub-region 11 to the sub-region 2.

It will be appreciated that the prior art mobile robots are limited to passable and non-passable due to the difficulty of passing through they provide only passable maps with poor accuracy. In real scenes, however, situations like "both paths can pass, but one is flatter and the other is bumpy, and the risk of robot getting trapped is higher" tend to occur. It is likely to select a path with a higher difficulty of passage, resulting in a higher probability of robot being trapped. In the scheme, the passing difficulty value between two adjacent sub-areas is calculated, and the passing difficulty value is used as the weight of the second directed edge in the first topological graph, so that the passing difficulty value can be intuitively provided. The mobile robot can conveniently determine a better passing strategy based on the passing difficulty value, so that the efficiency and the precision of controlling the mobile robot can be further improved.

Illustratively, the first information includes one or more of the following:

the width of the narrowest channel of the second terminal region;

previous pass experience data of the movable robot in the second terminal area; and

a coefficient of difficulty of passage of the depression and/or the obstacle in the path of the second terminal area.

By way of example and not limitation, one or more traffic difficulty functions may be provided and corresponding traffic difficulty coefficients may be calculated from each function, and then the traffic difficulty coefficients may be summed to obtain a final traffic difficulty value. For example, a narrow-lane difficulty function and a robot behavior information difficulty function may be constructed in advance. Then, the width of the narrowest channel of the second terminal area can be substituted into the narrow-channel difficulty function, and the first pass difficulty coefficient is calculated. Wherein the smaller the width of the narrow lane, the larger the value of the first pass difficulty coefficient. The previous traffic experience data of the movable robot in the second terminal area can be substituted into the robot behavior information difficulty function to obtain a second traffic difficulty coefficient. For example, it may be queried whether the robot triggers actions such as obstacle crossing, getting rid of poverty, etc. in the process from the first terminal area to the second terminal area in the history cleaning record. The second implementation difficulty coefficient can be calculated according to the behavior data. If the robot has more times of obstacle crossing, getting rid of the obstacle and the like, the obtained value of the second travelling difficulty coefficient is larger. For example, if information including a low obstacle such as a threshold or the like or information of a depression between the first terminal area and the second terminal area is obtained from the first information, it may be determined that the difficulty coefficient of passage thereof is equal to the preset difficulty coefficient.

Of course, in other schemes, other types of traffic difficulty cost functions can be constructed to calculate more traffic difficulty coefficients, and then the final traffic difficulty value can be calculated by using the traffic difficulty coefficients.

The scheme can accurately quantify the passing difficulty between two adjacent subareas. Moreover, the calculation amount of the method for calculating the traffic difficulty value is small.

Illustratively, the control method 1000 further includes step S1630.

Step S1630, a third graph of the target region is generated according to the reachable properties of each sub-region. Wherein each sub-region in the third graph having a different reachable property has a different identity.

The third diagram may be in any form according to embodiments of the present application. It may be a topography map, or a topology map, which can represent the reachable properties of the different sub-areas. One or more third graphs may also be determined based on the reachable properties of each sub-region. The sub-regions of different reachable properties may be represented by any suitable different identification, as long as it can be distinguished easily.

In one example, the third graph is a second topology graph including a plurality of nodes. Wherein at least some nodes in the second topology are connected using directed edges; the plurality of nodes at least comprise a first node corresponding to the subarea where the first position is located, a second node connected with the first node through a first directed edge and a third node connected with the first node through a second directed edge; the reachable attribute of the sub-region corresponding to the second node from the first position is reachable; the reachable attribute of the sub-region corresponding to the third node from the first position is potential reachable; the first directed edge and the second directed edge are represented in different styles.

As described above, the first topology map can express the traffic attribute between each sub-region and the adjacent sub-region in the target region, but cannot directly express the reachable attribute between the non-adjacent two sub-regions. And, the first topology map is independent of the current location of the robot. Illustratively, as shown in fig. 3, each node in the first topology is indiscriminate and may include two oppositely directed edges between the two nodes. In other words, the first topology map cannot express the sub-region in which the movable robot is currently located. The second topology map may express a topology map of reachable properties from the sub-region where the robot is currently located to other respective sub-regions in the target region. In the second topological map, the node corresponding to the sub-region where the movable robot is currently located is a unique starting node, and the nodes corresponding to each other sub-region may be all end nodes. And, only a single unidirectional directed edge can be included between the node corresponding to the subarea where the movable robot is currently located and other nodes.

By way of example, referring again to fig. 2 and 4, the first position where the mobile robot is currently located is for example in sub-area 1. The reachable properties of the mobile robot moving from the first position to each sub-region in the target region can be obtained from the first topology in fig. 4 and the first position where the mobile robot is currently located. For example, it is determined that the reachable properties of the movable robot moving from the first position to sub-region 1, sub-region 2 and sub-region 6 are reachable; determining that the reachable properties of the movable robot moving from the first position to the subarea 3, the subarea 4, the subarea 5, the subarea 10, the subarea 8 and the subarea 11 are potential reachable; and determining that the reachable properties of the sub-areas corresponding to the sub-areas 0, 9, 7 and 12 respectively, which are moved from the first position by the movable robot, are unreachable. And may generate a second topology map based on the reachability attributes. Fig. 5 shows a schematic diagram of a second topology according to an embodiment of the present application. For simplicity, the second topology may be referred to as the reachable topology. As shown in the figure, in the reachable topological graph, the node corresponding to the subarea 1 where the movable robot is located is a starting node, and the nodes corresponding to other subareas are all ending nodes. In other words, the nodes corresponding to the sub-region 1 and the nodes corresponding to other sub-regions all have a direct association relationship. For example, the display styles of nodes of different reachable properties may be different. For example, sub-region 1, sub-region 2, and sub-region 6 are represented by white filled nodes; sub-region 3, sub-region 4, sub-region 5, sub-region 10, sub-region 8 and sub-region 11 are represented by light gray filled nodes; while sub-areas 0, 9, 7 and 12 are represented by dark grey filled nodes. Illustratively, different display styles of directed edges between nodes may represent different reachable properties of sub-region 1 to each other sub-region. For example, in the second topology, the solid lines with arrows indicate accessibility between sub-regions, and the dashed lines with arrows indicate potential accessibility between sub-regions; no directed edges are displayed between the sub-areas that are not reachable. In some examples, the traffic difficulty value between the sub-regions may be further calculated, and the weight information of the second directed edge may be determined according to the calculated traffic difficulty value by a method similar to the above step S1611 d.

In this way, not only the reachability from the first position to each sub-region but also the positional iterative relationship between the respective sub-regions can be intuitively represented by the second topological graph.

In another example, the third map is a fourth map comprising a plurality of sub-regions. Each sub-region in the fourth map having a different reachable property is represented by a different style. Fig. 6 shows a schematic diagram of a fourth map according to one embodiment of the present application. The fourth map may be referred to as a reachable map. As shown in the figure, in the reachability map, the pixel values of pixels in the sub-areas having different reachability attributes are different. In other examples, sub-regions of different accessibility attributes may also be marked with different icons or filled with different patterns. The terrain information of the target area and the reachable attribute from the current position of the robot to each sub-area in the target area can be intuitively expressed through the reachable map.

The scheme for generating the third graph provides two map levels, namely a passable map representing passability and an reachable topological graph representing accessibility, so that the user experience is better.

Illustratively, step S1600 determines a traffic policy for the mobile robot to perform the current processing task according to the second map, including steps S1640 and S1650. In step S1640, a to-be-processed area of the current processing task in the target area is determined in the second map. Step S1650, determining a traffic strategy of the movable robot in the navigation area and the area to be processed according to the area to be processed and the first position of the movable robot. Wherein the navigation area is a connected area between the first position and the boundary of the area to be processed. And determining a passing strategy corresponding to the potential passable zone as an attempted passing strategy based on preset conditions for the case that the potential passable zone is included in the navigation zone or the to-be-processed zone.

The preset conditions can be set according to actual requirements. Optionally, the preset condition includes a condition that the current processing mode is a preset processing mode. Still alternatively, the preset condition may further include a condition that the area to be treated belongs to a potentially passable area. Still alternatively, the preset condition may further include a condition that a traffic difficulty value of the movable robot walking in the potentially trafficable region is smaller than a preset threshold value. Alternatively, the preset condition may further include a condition that a specific processing instruction of the user is acquired. Take a sweeping robot as an example. Referring again to fig. 2, the sweeping robot may be currently in a base station in sub-area 1 (beside the door of sub-area 6 in the drawing), and the area to be cleaned of the current cleaning task may be sub-area 4. It can be seen from the passable map that the sweeping robot can enter the subarea 4 to sweep mainly through the potential passable area at the entrance of the subarea 4. For example, the sweeping robot may be controlled to pass through the potentially trafficable region according to an attempted pass policy based on a condition that the sweeping robot is currently in a deep cleaning mode, or based on a condition that only a sub-region having a traffic attribute as the potentially trafficable region is included in the region to be cleaned. It will be appreciated that in this case the sweeping robot may enter the room of sub-area 4 by striking the slippers at the doorway, so that effective cleaning of the room may be achieved. That is, reasonable attempts can be made for areas that are indeterminate as to whether they are cleanable. If the cleaning is not confirmed after the attempt, timely giving up; if the confirmation is cleanable after the attempt, it can be ensured that no sweep is missed.

The scheme can obviously improve the processing effect and the processing efficiency of the movable robot for executing the processing task.

Illustratively, step S1650 determines a traffic policy of the mobile robot in the navigation area and the area to be processed according to the area to be processed and the first position where the mobile robot is located, including steps S1651 and S1652. Step S1651, determining a traffic policy of the movable robot in the navigation area and the first sub-area in the area to be processed as a direct traffic policy. Step S1652, determining the traffic strategy of the movable robot in the navigation area and the third subarea in the area to be processed as a direct non-traffic strategy.

Taking the sweeping robot as an example, the traffic policy of the reachable area of the sweeping robot from the current position can be determined as a direct traffic policy. In this way, the actual cleanable areas can be determined in real time, and can be efficiently cleaned by using various suitable cleaning coverage algorithms, so that the actual cleanable areas can be ensured not to be missed. The traffic policy of the unreachable area where the sweeping robot is unreachable from the current position may be determined as a direct unreachable policy. That is, the actually uncleanable areas can be determined in real time, so that cleaning of the areas can be timely abandoned, and the cleaning efficiency can be improved.

According to the scheme, the optimal passing strategy can be rapidly determined in real time according to the reachable attribute of each sub-region in the target region and the position association of the robot, and the processing efficiency of the movable robot for executing the processing task can be improved.

Illustratively, the control method 1000 further includes step S1300.

Step S1300, for the case that the area to be processed includes the potential passable area, determining a passable difficulty value of the movable robot in the potential passable area according to the first map and the second information about the potential passable area in the behavior information of the movable robot.

Step S1650 of determining a traffic policy of the mobile robot in the navigation area and the area to be processed according to the area to be processed and the first position where the mobile robot is located, includes step S1653.

Step S1653, determining the traffic strategy of the movable robot in the potential traffic zone according to the processing mode and the traffic difficulty value of the movable robot.

Step S1653 includes step S1653a and step S1653b.

In step S1653a, if the processing mode is the deep processing mode and the traffic difficulty value of the potentially trafficable region is less than the third threshold, determining the traffic policy of the movable robot in the potentially trafficable region as the trial traffic policy.

Step S1653b, if the processing mode is a fast processing mode and the traffic difficulty value of the potentially trafficable region is greater than a fourth threshold, determining a traffic policy of the mobile robot in the potentially trafficable region as a no-traffic policy, wherein the fourth threshold is less than the third threshold.

Also taking the sweeping robot as an example, the cleaning mode of the sweeping robot may include a deep cleaning mode and a quick cleaning mode. Referring again to fig. 2, the sweeping robot may be currently in the base station in the sub-area 1, and the area to be cleaned of the current cleaning task may be a full house area. For example, for the case where the current cleaning mode is the deep cleaning mode, the pass difficulty values of the potentially passable areas at the entrances of the sub-areas 4, 3, 5, and 10 may be calculated, respectively, using the method in the foregoing example. And the method can be compared with a preset third threshold value, and if the traffic difficulty value of the areas is smaller than the third threshold value, the corresponding passing attempt strategy can be adopted when the sweeping robot moves to the positions, so that the sweeping robot is controlled to try to pass the positions to enter the corresponding subareas for sweeping. For example, in deep cleaning mode, an attempted pass strategy may be employed to clean potentially passable areas such as obstacle areas such as child toy areas, threshold areas, areas below low obstacles, and the like. And for the case that the current cleaning mode is the quick cleaning mode, the passing difficulty values of the potential passable areas can be compared with a preset fourth threshold value. If the traffic difficulty value of the areas is larger than the fourth threshold value, cleaning of the areas can be timely abandoned.

The scheme can provide the passing difficulty value of the potential passable area, so that the passing strategy of the movable robot for executing the processing task can be flexibly adjusted according to the passing difficulty value. For the situation of needing advanced treatment, the treatment of some areas with larger passing difficulty can be tried as much as possible; for the quick processing, only the area with smaller passing difficulty can be tried to be processed. Therefore, the method can also adopt a more matched passing strategy for processing according to the actual demands of users. The scheme is more humanized and better in user experience.

Illustratively, the control method 1000 further includes step S1300. Step S1300, for the case that the area to be processed includes the potential passable area, determining a passable difficulty value of the movable robot in the potential passable area according to the first map and the second information about the potential passable area in the behavior information of the movable robot.

Step S1650 determines a traffic policy of the mobile robot in the navigation area and the area to be processed according to the area to be processed and the first position where the mobile robot is located, and further includes step S1654 and step S1655. In step S1654, the total traffic difficulty value of the movable robot in the navigation area and the area to be processed for a plurality of moving paths is determined according to the traffic difficulty values of the movable robot in the navigation area and the area to be processed for the potential traffic areas. In step S1655, the movement path with the smallest total traffic difficulty value is determined as the movement path for the movable robot to execute the task.

Also taking a sweeping robot as an example, if there are multiple paths from the first location where the robot is located to the area to be cleaned, the total traffic difficulty value for each moving path may be determined using any suitable method. For example, the traffic difficulty values of the potential trafficable areas that each path must pass through may be summed to obtain a total traffic difficulty value. Then, the path with the smallest total traffic difficulty value can be determined as the optimal navigation path.

In the scheme, the movable robot can be controlled to move and pass through a path with smaller total passing difficulty and difficult clamping according to the passing difficulty value of the potential passable area. The safety, the high efficiency and the accurate control to the movable robot can be further realized, so that the intelligent degree of the movable robot is better. The user experience is significantly improved.

Fig. 7 shows a schematic flow chart of a control method of a mobile robot according to another embodiment of the present application. As shown in fig. 7, first, information about obstacles in a target area acquired by sensors of a movable robot, such as a laser radar sensor, a collision sensor, a cliff sensor, a line laser sensor, a vision sensor, etc., may be acquired, and the form information of the obstacles acquired by the one or more sensors may be processed using a trained deep learning model to identify the type information of each obstacle in the target area. Then, the information of the obstacles can be integrated to construct an obstacle map of the target area. And can acquire various behavior information of the movable robot and forbidden zone information set by the user. Then, the traffic attribute of each position in the target area may be determined according to the first map, the behavior information of the movable robot, and the forbidden zone information set by the user. Then, the target area can be divided into areas according to the traffic attributes of all the positions in the target area, and a passable map of the target area is generated. The passable map comprises a passable area, an unviewable area and a potential passable area. Then, a traffic topology map that can represent trafficability between adjacent sub-areas can be determined according to the positions of the sub-areas in the trafficable map and the traffic attributes of the sub-areas. The traversable topology graph is a directed acyclic graph of a plurality of nodes connected by directed edges, and each node uniquely corresponds to a sub-region in the traversable map. Specifically, for each directed edge in the passable topological graph, the characteristics of the directed edge can be determined according to the passing attributes of the first terminal area and the second terminal area, which correspond to the two nodes connected in sequence by the directed edge. Wherein the characteristic of the directed edge represents a traffic attribute from the first terminal area to the second terminal area. The differently characterized directed edges in the passable topology are represented in different styles. The traffic difficulty value of the movable robot moving from the first terminal area to the second terminal area can be determined according to the first information about the second terminal area in the behavior information of the first map and the movable robot, and the weight information of each second directed edge in the traffic topology map can be determined according to the traffic difficulty value. Further, a first location where the mobile robot is currently located may be determined, and an reachable property of each sub-area in the target area from the first location where the mobile robot is currently located may be determined according to the first location where the mobile robot is currently located and the navigable topology map. Specifically, the reachable attribute of the movable robot from the first position to the starting sub-area can be determined according to the traffic attribute of the starting sub-area where the first position is located. Furthermore, each second node except the first node corresponding to the starting point sub-region in the passable topological graph can be traversed, and the reachable attribute of the movable robot moving from the first position to the terminal point sub-region corresponding to each second node is determined according to the characteristics of each directed edge between the first node and the second node. Then, a reachability map and a reachability topology map of the target area may be generated from the reachability attributes of the movable robot moving from the first position to each sub-area. Further, the determined maps can be used for determining the traffic strategy of the movable robot for executing the current processing task. And may control the mobile robot to perform processing tasks in accordance with the determined traffic policy.

According to another aspect of the present application, a mobile robot is provided. The mobile robot includes a control module. The control module is configured to execute the control method 1000 described above.

Illustratively, the mobile robot is a cleaning robot, and the processing tasks include a mopping task, a dust collection task, and a sweeping task. The cleaning robot has the advantages that the cleaning efficiency and the cleaning effect of executing the cleaning task are high, the cleaning robot is intelligent, and the user experience is good.

According to another aspect of the present application, a computer-readable storage medium is provided. A computer program/instructions is stored on a storage medium, which computer program/instructions, when run, is adapted to perform the above-described control method 1000 of the mobile robot. The computer readable storage medium may include a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable read-only memory (CD-ROM), a USB memory, or any combination of the foregoing storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

Those skilled in the art will appreciate the specific implementation and advantages of the mobile robot and the computer readable storage medium described above by reading the above description of the control method 1000 for the mobile robot, and for brevity, will not be described in detail herein.

Although the illustrative embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the above illustrative embodiments are merely illustrative and are not intended to limit the scope of the present application thereto. Various changes and modifications may be made therein by one of ordinary skill in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as set forth in the appended claims.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some of the modules in a mobile robot according to embodiments of the present application may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present application may also be embodied as device programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such a program embodying the present application may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.

The above description is merely illustrative of specific embodiments of the present application or the descriptions of specific embodiments, the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes or substitutions are covered in the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims (19)

1. A control method of a movable robot, comprising:

determining the traffic attribute of each position in a target area according to a first map of the target area, wherein the first map comprises information of whether each position in the target area has an obstacle or not, and the traffic attribute of the position represents the feasibility of the movable robot passing through the position;

dividing the target area according to the traffic attribute of each position to generate a second map of the target area, wherein the second map comprises a passable area, an unviewable area and a potential passable area; and

and determining a traffic policy of the movable robot for executing the current processing task according to the second map, and controlling the movable robot to execute the current processing task according to the traffic policy, wherein the traffic policy of the potential traffic zone comprises an attempt traffic policy.

2. The method for controlling a mobile robot according to claim 1, wherein determining a traffic policy for the mobile robot to perform a current processing task according to the second map comprises:

Determining the reachable attribute of each sub-area in the target area according to the second map; and

determining a traffic policy of the movable robot for executing the current processing task according to the reachable attribute of each sub-area;

wherein the reachability attribute of the sub-region represents the reachability of the movable robot moving from the first position where the movable robot is currently located to the sub-region; the first sub-region is reachable, the second sub-region is potentially reachable, and the third sub-region is unreachable; the path from the first location to the first sub-area comprises a first path via the passable zone only; the path from the first location to the second sub-area includes a second path via a potentially passable zone but not via an unviewable zone and does not include the first path; the path from the first location to the third sub-region does not include the first path and the second path.

3. The control method of a movable robot according to claim 2, characterized in that the control method further comprises:

and generating a third graph of the target area according to the reachable attribute of each sub-area, wherein each sub-area with different reachable attribute in the third graph has different identification.

4. A control method of a movable robot according to claim 3, wherein the third map is a second topology map including a plurality of nodes;

wherein at least some nodes in the second topology are connected using directed edges; the plurality of nodes at least comprise a first node corresponding to the subarea where the first position is located, a second node connected with the first node through a first directed edge and a third node connected with the first node through a second directed edge; the reachable attribute from the first position to the sub-region corresponding to the second node is reachable; the reachable attribute from the first position to the sub-region corresponding to the third node is potential reachable; the first directed edge and the second directed edge are represented in different styles.

5. A control method of a mobile robot according to claim 3, characterized in that the third map is a fourth map comprising a plurality of sub-areas, each sub-area in the fourth map having different accessibility properties being represented by a different style.

6. The method of claim 2, wherein determining the reachable property of each sub-region in the target region according to the second map comprises:

Determining a first topological graph according to the positions of all subareas in the second map and the traffic attribute of all subareas;

and determining the reachable attribute of each sub-area in the target area, which is moved from the current first position of the movable robot, according to the current first position of the movable robot and the first topological graph.

7. The method according to claim 6, wherein determining the first topological graph according to the position of each sub-region in the second map and the traffic attribute of each sub-region comprises:

determining a topological structure of the first topological graph according to the position of each subarea in the second map, wherein the first topological graph is a directed acyclic graph of a plurality of nodes connected by directed edges, and each node in the first topological graph uniquely corresponds to one subarea in the second map; and

for each directed edge, determining the characteristics of the directed edge according to the passing attributes of a first terminal area and a second terminal area corresponding to two nodes connected in sequence by the directed edge, wherein the characteristics of the directed edge represent the passing attributes from the first terminal area to the second terminal area; the directional edges with different characteristics in the first topological graph are represented by different patterns;

The determining, according to the first location where the movable robot is currently located and the first topological graph, the reachable attribute of the movable robot moving from the first location where the movable robot is currently located to each sub-area in the target area includes:

determining the reachable attribute of the movable robot from the first position to the starting point sub-area according to the traffic attribute of the starting point sub-area where the first position is located; and

traversing each second node except the first node corresponding to the starting point sub-region in the first topological graph, and determining the reachable attribute of the movable robot moving from the first position to the end point sub-region corresponding to each second node according to the characteristics of each directed edge between the first node and the second node.

8. The method for controlling a mobile robot according to claim 7, wherein determining the characteristic of the directed edge according to the traffic attribute of the first terminal area and the second terminal area corresponding to the two nodes connected in sequence by the directed edge includes:

determining a third pass attribute from the first terminal area to the second terminal area according to the first pass attribute of the first terminal area and the second pass attribute of the second terminal area, wherein the third pass attribute is determined to be the same as the second pass attribute when the first pass attribute is passable; and

And determining the characteristics of the directed edge according to the third running attribute.

9. The method according to claim 7, wherein determining the reachable property of the movable robot from the first position to the destination sub-region corresponding to each second node according to the characteristics of each directed edge between the first node and the second node comprises:

for each second node, in at least one path formed by directed-edge iterative connection from the first node to the second node,

if a first path only comprising a first directed edge exists, determining that the reachable attribute of the movable robot moving from the first position to the terminal subarea corresponding to the second node is reachable, wherein the passing attribute represented by the characteristic of the first directed edge is passable;

if the first path does not exist, but a second path only comprising a first directed edge and a second directed edge exists, determining that the reachable attribute of the movable robot moving from the first position to the endpoint subarea corresponding to the second node is potential reachable, wherein the traffic attribute represented by the characteristic of the second directed edge is potential trafficable; and

And if the first path and the second path do not exist, determining that the reachable attribute of the movable robot moving from the first position to the end sub-region corresponding to the second node is unreachable.

10. The method of claim 7, wherein the directed edge includes a second directed edge, and the first terminal area and the second terminal area corresponding to two nodes connected by the second directed edge are respectively passable and potentially passable,

the determining the first topological graph according to the position of each subarea in the second map and the traffic attribute of each subarea further comprises:

for each of the second directed edges,

determining a passing difficulty value of the movable robot moving from the first terminal area to the second terminal area according to first information about the second terminal area in the first map and the behavior information of the movable robot; and

and determining the weight information of the second directed edge according to the passing difficulty value.

11. The method of claim 10, wherein the first information includes one or more of the following information:

A width of a narrowest channel of the second terminal region;

previous traffic experience data of the mobile robot in the second terminal area; and

a coefficient of difficulty of passage of the depression and/or the obstacle in the path of the second terminal area.

12. The method of controlling a mobile robot according to any one of claims 1 to 11, wherein determining a traffic policy for the mobile robot to perform a current processing task based on the second map comprises:

determining a to-be-processed area of the current processing task in the target area in the second map; and

determining a passing strategy of the movable robot in a navigation area and the area to be processed according to the area to be processed and a first position where the movable robot is located;

the navigation area is a communication area between the first position and the boundary of the area to be processed, and for the situation that the navigation area or the area to be processed comprises a potential passable area, a passing strategy corresponding to the potential passable area is determined to be the trial passing strategy based on a preset condition.

13. The control method of a movable robot according to claim 12, characterized in that the control method further comprises:

For the situation that the area to be processed comprises a potential passable area, determining a passable difficulty value of the movable robot in the potential passable area according to second information about the potential passable area in the behavior information of the movable robot and the first map;

the determining a traffic strategy of the movable robot in the navigation area and the area to be processed according to the first positions of the area to be processed and the movable robot comprises the following steps:

determining a passing strategy of the movable robot in the potential passable area according to the processing mode of the movable robot and the passing difficulty value;

wherein if the processing mode is a deep processing mode and the traffic difficulty value of the potential trafficable region is smaller than a third threshold value, determining a traffic strategy of the movable robot in the potential trafficable region as the trial traffic strategy; and

and if the processing mode is a quick processing mode and the traffic difficulty value of the potential traffic zone is larger than a fourth threshold value, determining the traffic strategy of the movable robot in the potential traffic zone as a non-traffic strategy, wherein the fourth threshold value is smaller than the third threshold value.

14. The method for controlling a movable robot according to claim 12 when dependent on claim 2, wherein the determining a traffic strategy of the movable robot in a navigation area and the area to be processed according to the area to be processed and a first position where the movable robot is located further comprises:

determining a traffic strategy of the movable robot in a first subarea of the navigation area and the area to be processed as a direct traffic strategy; and

and determining the traffic strategy of the movable robot in a third subarea in the navigation area and the area to be processed as a direct non-traffic strategy.

15. The control method of a movable robot according to claim 12, characterized in that the control method further comprises:

for the situation that the area to be processed comprises a potential passable area, determining a passable difficulty value of the movable robot in the potential passable area according to second information about the potential passable area in the behavior information of the movable robot and the first map;

the determining, according to the first positions of the area to be processed and the movable robot, a traffic policy of the movable robot in the navigation area and the area to be processed, further includes:

Determining total pass difficulty values of a plurality of moving paths of the movable robot in the navigation area and the area to be processed according to pass difficulty values of potential passable areas of the movable robot in the navigation area and the area to be processed; and

and determining the moving path with the minimum total pass difficulty value as the moving path of the movable robot for executing the task.

16. The control method of a movable robot according to any one of claims 1 to 11, wherein the determining traffic attributes of respective positions in a target area based on a first map of the target area includes:

and determining the passing attribute of each position in the target area according to the first map, the behavior information of the movable robot and the forbidden zone information set by the user.

17. A mobile robot comprising a control module for performing the control method of any one of claims 1 to 16.

18. The mobile robot of claim 17, wherein the mobile robot is a cleaning robot and the treatment tasks include a mopping task, a dust collection task, and a sweeping task.

19. A computer-readable storage medium, characterized in that a computer program/instruction is stored, which computer program/instruction, when run, is adapted to perform the method of controlling a mobile robot according to any of claims 1 to 16.