CN116414118A - Path planning method and system based on obstacle marks and self-moving robot - Google Patents
Path planning method and system based on obstacle marks and self-moving robot Download PDFInfo
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Abstract
The invention discloses a path planning method and system based on obstacle marks and a self-moving robot, wherein the method comprises the following steps: planning an unobstructed navigation path from a current starting point to a target point of the mobile robot; identifying an obstacle path segment having a likelihood of passing if the navigation path is not available without an obstacle; constructing a navigation path from the starting point to the target point according to the identified obstacle path segment; the self-moving robot is controlled to move to the target point according to the navigation path, so that the path planning and navigation capacity of the self-moving robot are improved, and the intelligence of the self-moving robot is improved.
Description
Technical Field
The invention belongs to the technical field of robot path planning, and particularly relates to a path planning method and system based on obstacle marks and a self-moving robot.
Background
Along with the progress of science and technology, the cleaning robot slowly walks into the daily life of people. The cleaning robot completes a high-efficiency intelligent cleaning task in an indoor environment, and a path with short path distance, high walking efficiency and good safety performance needs to be planned in a working scene. Meanwhile, the robot is required to avoid all static and dynamic obstacles along the way, and the abrasion of the robot is reduced as much as possible to prolong the service life of the robot while pursuing efficient cleaning. Therefore, the Point-to-Point (abbreviated PTP) path planning technology of the mobile robot has important application value, and becomes a research hotspot for researchers at home and abroad. Currently, many PTP path planning algorithms have been proposed for intelligent robots.
However, as people increasingly rely on cleaning robots in daily life, it is desired to meet the cleaning requirements of the whole house to the greatest extent, and at the same time, it is desired to cope with the complex and changeable characteristics of the indoor environment, and higher requirements are put on the cleaning robots.
In the existing cleaning robot, a PTP path searching algorithm is adopted to conduct path planning and navigation, in one case, when the cleaning robot enters a room, if a room door is closed or an obstacle exists at the room door, the path of the cleaning robot leaving the room without the obstacle cannot be found by adopting the PTP path searching algorithm when the cleaning robot leaves the room, and therefore the searching algorithm is invalid. At this time, the cleaning robot may be trapped in the room, and not know how to walk.
As can be seen, the conventional PTP algorithm has failed to meet the user's needs for path planning and navigation of the cleaning robot in various fine scenarios. Therefore, how to improve the path planning and navigation capability of the cleaning robot in a fine scene becomes one of the key attack techniques of cleaning robot research.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is that the existing self-moving robot cannot plan a navigation path by using a traditional path navigation algorithm, so that the self-moving walking equipment cannot know how to walk to reach a target point, needs to be manually relieved, and has poor use experience.
In order to solve the above technical problems, the present invention provides a path planning method based on obstacle markers, for a self-moving robot, comprising:
planning an unobstructed navigation path from a current starting point to a target point of the self-moving robot;
identifying an obstacle path segment having a likelihood of passing if the navigation path is not available without an obstacle;
constructing a navigation path from the starting point to the target point according to the identified obstacle path segment;
and controlling the self-moving robot to move towards the target point according to the navigation path.
In one embodiment, the constructing a navigation path from the starting point to the target point according to the identified obstacle path segment specifically includes:
adopting a common local navigation mode, planning a first local navigation path from the starting point to the near end of the obstacle path section, and planning a second local navigation path from the far end of the obstacle path section to the target point;
and sequentially connecting the first local navigation path, the obstacle path section and the second local navigation path end to construct the navigation path.
In one embodiment, the controlling the self-moving robot to move to the target point according to the navigation path includes:
in a normal local navigation mode, controlling the self-moving robot to move to the near end of the obstacle path section according to the first local navigation path;
when the self-moving robot moves to the near end of the obstacle path section, controlling the self-moving robot to switch from the normal local navigation mode to a fine local navigation mode;
under the fine local navigation mode, current environment information is acquired, and whether the obstacle path section is passable is judged based on the environment information;
and if the judgment result is that the robot can pass, controlling the self-moving robot to move to the far end of the obstacle path section along the obstacle path section.
In one embodiment, the controlling the self-moving robot to move along the obstacle path section to the distal end of the obstacle path section includes:
controlling the self-mobile robot to switch from the fine local navigation mode to the normal local navigation mode;
and controlling the self-moving robot to move from the current position to the target point in a common local navigation mode.
In one embodiment, the identifying the obstacle path segment with the passing possibility specifically includes:
if the target point is positioned in a region which has been driven in the execution process of the current work task, acquiring a track map of the self-moving robot for executing the current work task, and determining the obstacle path section based on the track map; wherein the obstacle path segment is located on the trajectory map.
In one embodiment, the identifying the obstacle path segment with the passing possibility specifically includes:
if the target point is positioned in an area which is not driven by the current work task, constructing the obstacle path section with the preset attribute; or,
acquiring a historical track map stored in a historical cleaning process, and determining the obstacle path section through the historical track map; wherein the obstacle path segment is located on the historical track map.
In one embodiment, the constructing the obstacle path segment with a predetermined attribute specifically includes:
identifying whether the area where the target point is located is communicated with the current area;
and in the case that the area where the identification target point is located is communicated with the current area, scanning surrounding environment information, and identifying the obstacle path section with the possibility of passing based on the surrounding environment information.
In one embodiment, the identifying the obstacle path segment with the passing possibility specifically includes:
acquiring position coordinates of the obstacle with marked attribute;
and determining the obstacle path section according to the position coordinates of the obstacle with the marked attribute.
In addition, the invention also provides a path planning system based on the obstacle mark, which comprises the following steps:
the navigation module is used for planning an accessible navigation path from the current starting point to the target point of the mobile robot;
the obstacle path identification module is in communication connection with the navigation module and is used for identifying an obstacle path section with a passing possibility under the condition that the navigation path without obstacle cannot be obtained;
the navigation path planning module is in communication connection with the obstacle path identification module and is used for constructing a navigation path from the starting point to the target point based on the obstacle path segment;
and the control module is in communication connection with the navigation path planning module and is used for controlling the self-moving robot to move towards the target point according to the navigation path.
In addition, the present invention also provides a self-moving robot for automatically walking and working in a working area, comprising:
a body;
the controller is arranged on the machine body;
wherein the controller is configured to:
acquiring an unobstructed navigation path from a current starting point to a target point of the self-moving robot;
identifying an obstacle path segment having a likelihood of passing when the navigation path is not available without an obstacle;
constructing a navigation path from the starting point to the target point according to the identified obstacle path segment;
and controlling the self-moving robot to move towards the target point according to the navigation path.
The technical scheme provided by the invention has the following advantages:
according to the path planning method and system based on the obstacle mark and the self-moving robot, provided by the invention, under the condition that a traditional navigation algorithm cannot obtain an obstacle-free navigation path, a navigation path from a current starting point to a target point is constructed by identifying an obstacle path section with a possible passage, and the self-moving robot is controlled to move towards the target point according to the identified obstacle path section, so that the opportunity and possibility of the self-moving robot to reach the target point are improved, the self-moving robot is more intelligent, the use experience of a user is improved, and the embarrassment of how to walk is not known under the condition that the self-moving robot adopts a transmission navigation algorithm to mark the navigation path without rules.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a path planning method based on obstacle markers according to an embodiment of the present invention;
FIG. 2 is a simplified schematic diagram of a navigation path across two connected domains according to an embodiment of the present invention;
FIG. 3 is a simplified schematic diagram of a navigation path across two connected domains according to another embodiment of the present invention;
FIG. 4 is a simplified schematic diagram of a general local navigation mode in a single communication domain according to an embodiment of the present invention;
FIG. 5 is a simplified schematic view of a local fine navigation mode of an obstacle path segment according to an embodiment of the present invention;
FIG. 6 is a schematic view of a partial fine navigation mode simple scenario of an obstacle (step) path segment provided by an embodiment of the present invention;
fig. 7 is a path planning system based on obstacle markers according to an embodiment of the present invention.
Reference numerals illustrate:
102-a navigation module; 104-an obstacle path recognition module; 106, a navigation path planning module; 108-a control module.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. The invention will be described in detail hereinafter with reference to the drawings in conjunction with embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
In the present invention, unless otherwise indicated, terms of orientation such as "upper, lower, top, bottom" are used generally with respect to the orientation shown in the drawings or with respect to the component itself in the vertical, upright or gravitational direction; also, for ease of understanding and description, "inner and outer" refers to inner and outer relative to the profile of each component itself, but the above-mentioned orientation terms are not intended to limit the present invention.
The embodiment provides a path planning method based on obstacle markers, which is used for a self-moving robot.
The self-moving robot is a robot that automatically performs a work task in a work area. In one embodiment, the self-moving robot is a cleaning robot, and the working area is the floor of the room to be cleaned. The cleaning robot automatically performs a cleaning plan covering the floor in the room. In another implementation scenario, the self-moving robot is a mowing robot, and the working area is a lawn to be mowed. Currently, the self-moving robot may also include other types of robots, such as inspection robots, nurse robots, and the like, which are not limited herein.
Currently, many PTP (Point-to-Point, abbreviated PTP) path planning algorithms have been proposed for self-moving robots, such as probabilistic roadmapping (Probabilistic Roadmap, PRM), fast-explored random-Exploring Random Tree (RRT), artificial potential field (Artifical Potential Field, APF), a star algorithm and some other heuristic algorithms. Compared with the algorithms, the A star algorithm is widely applied because the concepts of BFS ((Breadth First Search) algorithm and Dijkstra algorithm are combined, and the algorithm has the advantages of high searching efficiency, short planning path and the like.
However, for navigation of a complex working scene, since the working area generally includes a plurality of different areas having paths of mutual communication therebetween, the plurality of different areas may also be referred to as a plurality of different communication areas. For example, referring to fig. 2 and 3, in the case where a start point (english start) and a target point (english gold) of the self-moving robot are in different connected domains, the self-moving robot needs to find a passable path between two different connected domains. The communication path between the two different communication domains is narrow and is easy to be closed, in this case, all possible passing paths from the starting point (Start) to the target point (gold) are blocked, and the self-mobile robot cannot acquire an unobstructed navigation path based on the existing navigation algorithm.
In order to improve the intelligence of the self-mobile robot in the case that the self-mobile robot cannot acquire an unobstructed navigation path, the possibility of the self-mobile robot reaching a target point is increased. The invention provides a path planning method based on obstacle marks, which comprises the following steps when being implemented in particular:
s10, planning an unobstructed navigation path from a current starting point to a target point of the self-moving robot;
s20, identifying an obstacle path section with a passing possibility under the condition that the navigation path without obstacle cannot be obtained;
s30, constructing a navigation path from the starting point to the target point according to the identified obstacle path segment;
and S40, controlling the self-moving robot to move towards the target point according to the navigation path.
In one implementation, the self-moving robot is a cleaning robot. As people increasingly rely on cleaning robots in daily life, it is desirable to meet the cleaning requirements of the whole house to the greatest extent, and at the same time, to cope with the complex and changeable characteristics of indoor environments. Typically, for an indoor work scenario, the work area includes a plurality of different rooms, each having a path of mutual communication therebetween, so each room corresponds to one communication domain. The living room and the bedroom are two different communicating areas, and the living room and the bedroom are communicated through the opening of the bedroom door, so that the living room and the bedroom can be understood as the two different communicating areas. If the current cleaning robot is located in the living room and the target point is located in the bedroom, the planned navigation path is a navigation path crossing the two connected domains. It can be understood that compared with navigation in the communication domain, navigation across two communication domains is clumsy and not intelligent because the communication paths are easy to be closed due to the narrow communication paths between two different communication domains, and navigation path planning failure is easy to be caused, so that the cleaning robot stops at the current communication domain and walks without knowing how. That is, in the case where a Start point (Start point) and a target point (end point, gold) of the cleaning robot are in different connected domains (rooms), the cleaning robot needs to find a passable path between the two different connected domains.
In the above step S10, the sampled navigation algorithm is the existing PTP path navigation algorithm. In one embodiment, the navigation algorithm employs the A Star algorithm. Of course, other path navigation algorithms may be employed, and are not limited in this regard. These navigation algorithms are able to plan a navigation path in case there is a path from the current starting point to the target point. When all the paths from the current starting point to the target point are blocked by the obstacle, the conventional navigation algorithms will fail, and the navigation paths are marked out without regulations.
The starting point may be simply called a starting point, the English may be called a Start, the target point may be called an end point, and the English may be called a Goal.
In the event that an unobstructed navigation path is not available, an obstructed path segment with a potential for passage is identified. The above-described "obstacle path segment with possibility of passage" is an obstacle path segment that is currently identified as non-passable by the navigation algorithm. At the same time, the possibility of still having traffic is identified taking into account the attribute characteristics of the marked obstacle of the obstacle path segment. Here, having "a possibility of passing" is to be understood as that the obstacle path segment is located on the communication path between the current area and the area where the target point is located, and may be a communication path indicated by a SLAM map or a communication path indicated by a track map, and the obstacle path segment may be a path segment that has been passed. Thus, the obstacle path segment is still considered by the self-moving robot to have the possibility of re-passing. For example, the obstacle path segment may be a door that is currently closed or a step marked as an obstacle.
The "navigation path" is actually a navigation path through an obstacle. When all the paths are blocked by the obstacle, it is considered that the self-moving robot can still try to pass through the obstacle path segment with the possibility of passing, thereby reaching the destination. In the implementation scene of the cleaning robot, when a person stands at the door, the path planning is invalid, the self-moving robot can still walk to the position of the door through the ordinary navigation by identifying the possible obstacle path section of the passage, and the person can leave the position of the door even if the person does not leave the position, and the person can actively leave the position, so that the self-moving robot can pass. Therefore, in this case, the construction of the navigation path obviously improves the intelligence and flexibility of the self-moving robot, and has high practical value.
According to the path planning method based on the obstacle mark, under the condition that a navigation path without obstacle cannot be obtained by adopting a traditional navigation algorithm, a navigation path with a possible passage is identified, a navigation path from a current starting point to a target point is constructed according to the identified obstacle path, and the self-moving robot is controlled to move towards the target point according to the navigation path, so that the opportunity and possibility that the self-moving robot reaches the target point are improved, the self-moving robot is more intelligent, the use experience of a user is improved, and the embarrassment situation of how to walk is not known under the condition that the self-moving robot is limited in a current area under the condition that the navigation path is marked by adopting a transmission navigation algorithm without regulation.
In one embodiment, step S30, that is, "constructing a navigation path from the starting point to the target point according to the identified obstacle path segment", specifically includes:
s31, planning a first local navigation path from the starting point to the near end of the obstacle path section and planning a second local navigation path from the far end of the obstacle path section to the target point by adopting a common local navigation mode;
s32, sequentially connecting the first local navigation path, the obstacle path section and the second local navigation path end to construct the navigation path.
The above-mentioned ordinary local navigation mode is understood as the sectional navigation of the cleaning robot in the same communication domain, the navigation algorithm adopted correspondingly is the ordinary navigation algorithm, and the corresponding navigation mode is the ordinary local navigation mode. As shown in fig. 4, with the current position as a starting point and the obstacle path segment at one end of the current connected domain as an intermediate target point, normal navigation is performed, and since the current starting point and the intermediate target point are located in the same connected domain, normal navigation in the current connected domain can be performed, thereby obtaining the first local navigation path. Correspondingly, the communication domain where the distal end of the obstacle path section and the target point are located is also the same communication domain, so that the common navigation in the target communication domain can be performed, and a second local navigation path is obtained.
And connecting the first local navigation path, the obstacle path section and the second local navigation path at first to form a navigation path. It will be appreciated that the above-described navigation path is not a path enabling an unobstructed passage from a mobile robot in an absolute sense, but a navigation path constructed in segments with an obstructed path segment located in the middle portion of the navigation path.
In an embodiment, step S40, namely a step of controlling the self-moving robot to move to the target point according to the navigation path, specifically includes:
s41, under a common local navigation mode, controlling the self-moving robot to move to the near end of the obstacle path section according to the first local navigation path;
s42, when the self-moving robot moves to the near end of the obstacle path section, controlling the self-moving robot to switch from the normal local navigation mode to the fine local navigation mode;
s43, under the fine local navigation mode, acquiring current environment information, and judging whether the obstacle path section is passable or not based on the environment information;
and S44, if the judgment result is that the robot can pass, controlling the self-moving robot to move to the far end of the obstacle path section along the obstacle path section.
That is, the steps above first control the traveling from the mobile robot to the proximal end of the obstacle path segment by the normal local navigation, completing the traveling of the first local navigation path. After reaching the obstacle path segment, switching to a fine local navigation mode to deal with fine navigation of the obstacle path segment, and improving the successful passing possibility of the navigation of the obstacle path segment.
Fig. 5 and 6 show fine local navigation of two different scenes. By scanning the surrounding information in the fine local navigation mode, it is identified whether the current obstacle path segment is passable. In fig. 5, the obstacle path segment is completely closed, and the current obstacle path segment is determined to be unable to pass through, so that the self-control mobile robot gives up navigation of the obstacle path segment. The obstacle path section shown in fig. 6 is a step obstacle, and the self-moving robot can be controlled to pass through the obstacle path section according to the determination result of the environmental information of S43. Specifically, in order to ensure the passing efficiency, an acceleration passing mode, that is, acceleration obstacle crossing navigation is adopted.
That is, in the fine local navigation mode, current environmental information is acquired from the mobile robot, whether the obstacle path segment is passable is determined based on the environmental information, and if not, navigation of the obstacle path segment is directly abandoned. For example, if a closed door is detected and the door cannot pass, the door is directly abandoned. When the result is judged to be passable through the environmental information analysis, the self-moving robot is directly controlled to move to the far end of the obstacle path section along the obstacle path section.
In an embodiment, after the self-mobile robot moves to the near end of the obstacle path section according to the first local navigation path, the self-mobile robot performs path planning navigation by taking the current position as a starting point and taking the far end of the obstacle path section as a terminal, so as to obtain a navigation path passing through the obstacle path section, and the self-mobile robot is controlled to walk along the navigation path to pass through the obstacle path section and reach the target communication domain.
In an embodiment, the self-moving robot analyzes the type of the obstacle through the current environmental information, and when the type of the obstacle is a traversable, such as a low step, the self-moving robot is controlled to try a path segment through the obstacle.
In an embodiment, step S44, that is, after the step of controlling the self-moving robot to move along the obstacle path section to the distal end of the obstacle path section, further includes:
s441, controlling the self-mobile robot to switch from the fine local navigation mode to the normal local navigation mode;
s442, controlling the self-moving robot to move from the current position to the target point in a common local navigation mode.
That is, after passing through the obstacle path segment, the self-mobile robot has reached the target communication domain, and the self-mobile robot automatically switches the local fine navigation mode to the normal local navigation mode, and the walking of the second local navigation path is completed by adopting the normal local navigation mode.
The above-described "obstacle path segment with a possibility of passing" includes various types. In different working scenarios, the identification of obstacle path segments also differs due to the different types of obstacles. In one scenario, the target point is located in an area that has been travelled through during the execution of the current work task, that is, the area of the target point has arrived, in which case the obstacle path segment may be identified from the historical track map.
Thus, in one embodiment, "identify obstacle path segments with a possibility of passing" in step S20 specifically includes:
if the target point is positioned in a region which has been driven in the execution process of the current work task, acquiring a track map of the self-moving robot for executing the current work task, and determining the obstacle path section based on the track map; wherein the obstacle path segment is located on the trajectory map.
If the area of the target point is not reached in the current task execution process, but is reached in the previous task execution process. For example, in the case of the cleaning robot, the cleaning robot does not reach the master bedroom during the cleaning operation, but the master bedroom is reached based on the past history trajectory map, and if the target point is located in the master bedroom, the obstacle route section having the possibility of passing can be identified based on the history trajectory map.
Thus, in another embodiment, "identify obstacle path segments with a possibility of passage" in step S20 specifically includes:
acquiring a historical track map stored in a historical cleaning process, and determining the obstacle path section through the historical track map; wherein the obstacle path segment is located on the historical track map.
The "history track map" is a track map stored in the previous working process.
In another embodiment, "identify obstacle path segment with a possibility of passing" in step S20 specifically includes:
if the target point is located in an area which is not driven by the current work task, constructing the obstacle path section with the preset attribute through SLAM map and AI intelligent identification technology.
Specifically, in an embodiment, the step of constructing the obstacle path segment with the predetermined attribute through SLAM map and/or AI intelligent recognition technology includes:
identifying whether the area where the target point is located is communicated with the current area or not through the SLAM map;
and under the condition that the area where the identification target point is located is communicated with the current area, scanning surrounding environment information through an AI intelligent identification technology, and identifying the obstacle path section with the possibility of passing based on the surrounding environment information.
Specifically, the area where the SLAM map displays the target point is communicable with the current area, and further, the attribute of the obstacle path segment is identified by an AI intelligent identification technology, for example, the area identified as "gate" by AI is identified, and the area identified as "gate" is constructed as the obstacle path segment. AI may also identify many other predetermined attribute obstructions that are not completely unvented, such as sliding doors, moving tables and chairs, etc. In this embodiment, the attribute of the obstacle may be analyzed by using the AI identification technology, and when the attribute meets the preset attribute, the path where the obstacle is located may be constructed as an obstacle path segment, so as to establish a navigation path, and improve the working efficiency of the self-mobile robot.
In the embodiment of the cleaning robot, the cleaning robot can meet special areas, such as carpet areas, during working, virtual walls are arranged in a mode of marking the positions of the special areas, and special cleaning modes are adopted during normal cleaning to avoid entering the marked areas. In another case, the cleaning robot is controlled to not cross the special areas, such as the door passing steps and the sliding door sliding rails, before the cleaning of the current area is completed, and then the cleaning robot is controlled to cross the special obstacle area after the cleaning of the current area is completed.
In order to cope with the above-mentioned special marked obstacle path segment situation. In one embodiment, "identify obstacle path segment with traffic possibility" in step S20 specifically includes:
acquiring position coordinates of the obstacle with marked attribute; wherein the obstacle comprises one or more of a sliding door guide rail area, a carpet area and a passing door step;
and determining the obstacle path section according to the position coordinates of the obstacle with the marked attribute.
The above-mentioned "obstacle marked with an attribute" is an "obstacle" that can pass through from the mobile robot, and these obstacles are marked with an attribute in normal operation, so that in the above-mentioned method steps, by acquiring the position coordinates of the obstacle marked with an attribute, it is possible to construct an obstacle path segment using these obstacles marked with an attribute.
The path planning method based on the obstacle mark provided by the invention is oriented to PTP path planning of a fine scene and the design of a navigation scheme. The existing PTP path planning and navigation technology generally shows that navigation collision increases, arrival capacity in a narrow space, escaping capacity and the like are poor when facing a cleaning task in a complex scene. According to the path planning scheme capable of supporting fine navigation and crossing the connected domain, the arrival rate of the self-moving robot to a narrow space is improved, and meanwhile, the escaping capability of the self-moving robot in the narrow space and the navigation passing performance of the self-moving robot in a fine scene are enhanced.
Referring to fig. 7, the present invention further provides a path planning system 100 based on the obstacle markers, including:
a navigation module 102 for planning an unobstructed navigation path from a current starting point to a target point of the mobile robot;
an obstacle path identifying module 104, communicatively connected to the navigation module 102, for identifying an obstacle path segment having a possibility of passing if an obstacle-free navigation path cannot be obtained;
a navigation path planning module 106, communicatively connected to the obstacle path identifying module 104, for constructing a navigation path from the starting point to the target point based on the obstacle path segment;
the control module 108 is communicatively connected to the navigation path planning module 106 and is configured to control the self-moving robot to move to the target point according to the navigation path.
The obstacle marking-based path planning system 100 in this embodiment corresponds to the above-mentioned obstacle marking-based path planning method, and the functions of each module in the obstacle marking-based path planning system 100 in this embodiment are described in detail in the corresponding method embodiments, which are not described herein.
The invention also provides a self-moving robot for automatically walking and working in a working area, comprising: a body; and the controller is arranged on the machine body.
Wherein, the controller is used for:
acquiring an unobstructed navigation path from a current starting point to a target point of the mobile robot;
identifying an obstacle path segment having a likelihood of passing when the navigation path is not available without an obstacle;
constructing a navigation path from the starting point to the target point according to the identified obstacle path segment;
and controlling the self-moving robot to move to the target point according to the navigation path.
Similarly, the controller is used for implementing the path planning method based on the obstacle mark, and specific content can refer to the description of the cleaning control method, which is not repeated here.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, server, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), servers and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent that the embodiments described above are merely some, but not all, embodiments of the invention. Based on the embodiments of the present invention, those skilled in the art may make other different changes or modifications without making any creative effort, which shall fall within the protection scope of the present invention.
Claims (10)
1. A path planning method based on obstacle markers, for a self-moving robot, the method comprising:
planning an unobstructed navigation path from a current starting point to a target point of the self-moving robot;
identifying an obstacle path segment having a likelihood of passing if the navigation path is not available without an obstacle;
constructing a navigation path from the starting point to the target point according to the identified obstacle path segment;
and controlling the self-moving robot to move towards the target point according to the navigation path.
2. The obstacle marker-based path planning method of claim 1, wherein the constructing a navigation path from the starting point to the target point from the identified obstacle path segment, comprises:
adopting a common local navigation mode, planning a first local navigation path from the starting point to the near end of the obstacle path section, and planning a second local navigation path from the far end of the obstacle path section to the target point;
and sequentially connecting the first local navigation path, the obstacle path section and the second local navigation path end to construct the navigation path.
3. The obstacle-marking-based path planning method of claim 2, wherein the controlling the self-moving robot to move toward the target point in accordance with the navigation path comprises:
in a normal local navigation mode, controlling the self-moving robot to move to the near end of the obstacle path section according to the first local navigation path;
when the self-moving robot moves to the near end of the obstacle path section, controlling the self-moving robot to switch from the normal local navigation mode to a fine local navigation mode;
under the fine local navigation mode, current environment information is acquired, and whether the obstacle path section is passable is judged based on the environment information;
and if the judgment result is that the robot can pass, controlling the self-moving robot to move to the far end of the obstacle path section along the obstacle path section.
4. A path planning method based on obstacle markers according to claim 3, characterized in that said controlling said self-moving robot after moving along said obstacle path segment to the distal end of said obstacle path segment comprises:
controlling the self-mobile robot to switch from the fine local navigation mode to the normal local navigation mode;
and controlling the self-moving robot to move from the current position to the target point in a common local navigation mode.
5. The obstacle marker-based path planning method as claimed in any one of claims 1-4, wherein said identifying obstacle path segments having a pass potential comprises:
if the target point is positioned in a region which has been driven in the execution process of the current work task, acquiring a track map of the self-moving robot for executing the current work task, and determining the obstacle path section based on the track map; wherein the obstacle path segment is located on the trajectory map.
6. The obstacle marker-based path planning method as claimed in any one of claims 1-4, wherein said identifying obstacle path segments having a pass potential comprises:
if the target point is positioned in an area which is not driven by the current work task, constructing the obstacle path section with the preset attribute; or,
acquiring a historical track map stored in a historical cleaning process, and determining the obstacle path section through the historical track map; wherein the obstacle path segment is located on the historical track map.
7. The obstacle marker-based path planning method of claim 6, wherein said constructing the obstacle path segment of a predetermined attribute comprises:
identifying whether the area where the target point is located is communicated with the current area;
and in the case that the area where the identification target point is located is communicated with the current area, scanning surrounding environment information, and identifying the obstacle path section with the possibility of passing based on the surrounding environment information.
8. The obstacle marker-based path planning method as claimed in any one of claims 1-4, wherein said identifying obstacle path segments having a pass potential comprises:
acquiring position coordinates of the obstacle with marked attribute;
and determining the obstacle path section according to the position coordinates of the obstacle with the marked attribute.
9. A path planning system based on obstacle markers, comprising:
the navigation module is used for planning an accessible navigation path from the current starting point to the target point of the mobile robot;
the obstacle path identification module is in communication connection with the navigation module and is used for identifying an obstacle path section with a passing possibility under the condition that the navigation path without obstacle cannot be obtained;
the navigation path planning module is in communication connection with the obstacle path identification module and is used for constructing a navigation path from the starting point to the target point based on the obstacle path segment;
and the control module is in communication connection with the navigation path planning module and is used for controlling the self-moving robot to move towards the target point according to the navigation path.
10. A self-moving robot for automatically walking and working in a work area, comprising:
a body;
the controller is arranged on the machine body;
wherein the controller is configured to:
acquiring an unobstructed navigation path from a current starting point to a target point of the self-moving robot;
identifying an obstacle path segment having a likelihood of passing when the navigation path is not available without an obstacle;
constructing a navigation path from the starting point to the target point according to the identified obstacle path segment;
and controlling the self-moving robot to move towards the target point according to the navigation path.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202111681899.4A CN116414118A (en) | 2021-12-31 | 2021-12-31 | Path planning method and system based on obstacle marks and self-moving robot |
| PCT/CN2022/132723 WO2023124621A1 (en) | 2021-12-31 | 2022-11-18 | Path planning method and system based on obstacle marker, and self-moving robot |
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| CN202111681899.4A CN116414118A (en) | 2021-12-31 | 2021-12-31 | Path planning method and system based on obstacle marks and self-moving robot |
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| JP5112666B2 (en) * | 2006-09-11 | 2013-01-09 | 株式会社日立製作所 | Mobile device |
| CN103092204B (en) * | 2013-01-18 | 2016-04-13 | 浙江大学 | A kind of Robotic Dynamic paths planning method of mixing |
| CN104808671B (en) * | 2015-05-19 | 2017-03-15 | 东南大学 | A kind of robot path planning method under domestic environment |
| CN108415428B (en) * | 2018-02-11 | 2021-04-30 | 弗徕威智能机器人科技（上海）有限公司 | Global path optimization method of mobile robot |
| CN110375756B (en) * | 2018-11-07 | 2021-12-31 | 北京京东尚科信息技术有限公司 | Navigation method, navigation device and computer readable storage medium |
| CN110716559B (en) * | 2019-11-22 | 2022-07-29 | 河北工业大学 | Comprehensive control method for shopping mall and supermarket goods picking robot |
| CN112050810B (en) * | 2019-12-23 | 2022-09-27 | 华北电力大学（保定） | Indoor positioning navigation method and system based on computer vision |
| CN111142526B (en) * | 2019-12-30 | 2022-07-12 | 科沃斯机器人股份有限公司 | Obstacle crossing and operation method, equipment and storage medium |
| CN111142542A (en) * | 2020-01-15 | 2020-05-12 | 苏州晨本智能科技有限公司 | Omnidirectional mobile robot autonomous navigation system based on VFH local path planning method |
| CN111562788A (en) * | 2020-06-04 | 2020-08-21 | 哈尔滨理工大学 | Path planning and obstacle avoidance method for indoor intelligent cleaning robot |
| CN113108796B (en) * | 2021-04-19 | 2023-10-20 | 北京有竹居网络技术有限公司 | Navigation method, navigation device, storage medium and navigation equipment |
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