CN113733086B - Travel method, device and equipment of robot and storage medium - Google Patents
Travel method, device and equipment of robot and storage medium Download PDFInfo
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- CN113733086B CN113733086B CN202111015374.7A CN202111015374A CN113733086B CN 113733086 B CN113733086 B CN 113733086B CN 202111015374 A CN202111015374 A CN 202111015374A CN 113733086 B CN113733086 B CN 113733086B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
- B25J9/1697—Vision controlled systems
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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Abstract
The embodiment of the invention discloses a travel method, a travel device, travel equipment and travel storage media for a robot. The method comprises the following steps: before a target robot reaches a target road section in a target travel path, acquiring current image data of the target road section acquired by an image acquisition device; determining whether the target road section meets a path optimization condition according to the current image data; if yes, planning a new target travel path for the target robot, so that the target robot avoids the target road section. The embodiment of the invention solves the problem of congestion caused by external factors in the traveling process of the target robot, and achieves the purposes of saving time and improving efficiency in the working process of the target robot.
Description
Technical Field
The embodiment of the invention relates to an automatic control technology, in particular to a travel method, a travel device, travel equipment and a storage medium of a robot.
Background
Currently, the service robot industry is in an emerging development stage, and the market growth kinetic energy of the service robot is remarkable under the drive of factors such as demand diversification.
In an actual working scene of a robot, a target path is usually planned in advance for the robot and tasks are executed according to planned path information, a large amount of people flow in a certain road section possibly exists in the process of robot operation to cause road congestion, at the moment, the robot has to select to wait in situ, and when the traffic of people does not influence the traffic of the robot, the corresponding tasks are continuously executed.
Although the robot selects to wait in place when the road is congested to avoid situations such as collision, in reality, there may be a plurality of candidate road segments that can lead to the destination, and the robot cannot select other road segments to go to the destination when the road is congested, thus greatly wasting the time of the robot to execute tasks and reducing the working efficiency.
Disclosure of Invention
The invention provides a travel method, a travel device, travel equipment and travel storage medium for a robot, so that time is saved and efficiency is improved in the working process of a target robot.
In a first aspect, an embodiment of the present invention provides a travel method for a robot, including:
before a target robot reaches a target road section in a target travel path, acquiring current image data of the target road section acquired by an image acquisition device;
determining whether the target road section meets a path optimization condition according to the current image data;
if yes, planning a new target travel path for the target robot, so that the target robot avoids the target road section.
In a second aspect, an embodiment of the present invention further provides a travel device of a robot, where the device includes:
the image data acquisition module is used for acquiring current image data of a target road section acquired by the image acquisition device before the target robot reaches the target road section in the target travel path;
the optimization condition determining module is used for determining whether the target road section meets the path optimization condition according to the current image data;
and the path planning module is used for planning a new target travel path for the target robot when the target road section meets the path optimization condition so that the target robot avoids the target road section.
In a third aspect, an embodiment of the present invention further provides a travel device of a robot, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, where the processor implements the travel method of the robot according to any one of the embodiments of the present invention when executing the program.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, where the program when executed by a processor implements a travel method of a robot according to any one of the embodiments of the present invention.
According to the embodiment of the invention, before a target robot reaches a target road section in a target travel path, current image data of the target road section acquired by an image acquisition device is acquired; and determining whether the target road section meets the path optimization condition according to the current image data, and if so, planning a new target travel path for the target robot to enable the target robot to avoid the target road section. The technical scheme solves the problem of congestion caused by external factors in the traveling process of the target robot, and achieves the purposes of saving time and improving efficiency in the working process of the target robot.
Drawings
Fig. 1 is a flowchart of a travel method of a robot in a first embodiment of the present invention;
fig. 2A is a flowchart of a travel method of a robot in a second embodiment of the present invention;
fig. 2B is a schematic diagram of labeling points in a travel method of a robot in a second embodiment of the present invention;
fig. 2C is a schematic diagram illustrating an implementation of a travel method of a robot in a second embodiment of the present invention;
fig. 3 is a flowchart of a travel method of a robot in a third embodiment of the present invention;
fig. 4 is a schematic structural view of a travel device of a robot in a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a travel device of a robot in a fifth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of a travel method of a robot according to an embodiment of the present invention, where the embodiment is applicable to a travel situation of a robot in a complex scene of human factors such as a restaurant, and the method may be performed by a travel device of the robot, and the device may be implemented in a software and/or hardware manner. As shown in fig. 1, the method specifically includes the following steps:
s110, acquiring current image data of a target road section acquired by an image acquisition device before the target robot reaches the target road section in a target travel path.
The target travel path refers to a travel path used by the target robot in the moving process of the scene, and can be obtained by planning the path of the target robot in advance according to travel requirements of the target robot, such as a starting point position and an end point position. Different candidate road segments can be included in the scene to which the robot belongs, and the target road segment refers to the candidate road segment belonging to the target travel path. The candidate road segments may be located within a collection area of an image collector, which may be a camera.
The number of the image collectors can be one or more, and can be correspondingly increased or decreased according to actual requirements. The image acquisition area of the image acquisition device can cover a single candidate road section, can also cover at least two candidate road sections, and can be set in advance by related technicians.
The current image data of the target link may be acquired by an image acquisition device covering the target link before the target robot reaches the target link in the target travel path.
S120, determining whether the target road section meets a path optimization condition according to the current image data.
The path optimization condition may be that a target road section is congested, a driving direction of a target robot collides with a scene environment factor, and the like. Taking a restaurant scene as an example of the target robot, if an obstacle blocking the movement of the target robot is placed on the target road section, it may be determined that the target road section satisfies the path optimization condition. The obstacle may be a static obstacle or a dynamic obstacle, which is not limited in this embodiment of the present application, for example, a child dining chair, an environmental user walking, checking out, and collecting a dining table, and the like.
Specifically, the current image data may be identified, and whether the target road section satisfies the path optimization condition may be determined according to the image identification result. Taking the path optimization condition as an example of congestion, determining whether the target road section is congested according to the acquired current image data; if the target road section is congested, a path optimization condition is met; otherwise, the path optimization condition is not satisfied.
And S130, if the travel path is met, planning a new target travel path for the target robot, so that the target robot avoids the target road section.
If the target road section meets the path optimization condition, a new target travel path is planned for the target robot, so that the target robot avoids the target road section. In the process of planning a new target travel path, the current position of the target robot can be used as a new starting position, and path planning can be performed according to the new starting position and the new ending position. The path planning method is not particularly limited in the embodiment of the application. It should be noted that, if the target road section does not meet the optimization condition, the target robot may continue to use the target travel path, without re-planning, i.e., the target robot may enter the target road section.
For example, whether the candidate road section is congested is determined by acquiring the current traffic of the candidate road section, the congestion degree of the candidate road section is determined according to the current traffic of the candidate road section or the current average speed of other robots on the candidate road section, and the congestion time of the candidate road section is determined according to the congestion degree of the candidate road section and the association relation between the historical congestion time and the historical congestion time. And determining the total time consumption of the congested road section according to the superposition of the conventional time consumption and the congested time consumption of the candidate road section, wherein the total time consumption of the uncongested road section is the conventional time consumption. The target travel path is a superposition of the total time consumption of the shortest candidate road sections.
Before the target robot reaches a target road section in a target travel path, acquiring current image data of the target road section through an image acquisition device, and determining whether the target road section meets a path optimization condition according to the current image data of the target road section; if yes, a new target travel path is planned for the target robot, so that the target robot avoids the target road section, namely, under the condition that the abnormality occurs in the target road section through the current image data of the target road section, the new target travel path is planned for the target robot before the target robot reaches the target road section, and the target robot is controlled to avoid the abnormal target road section, so that the target road section is prevented from interfering with the travel of the target robot. And whether the target road section meets the path optimization condition is judged through the image data, so that the judgment accuracy is improved.
According to the embodiment of the invention, before a target robot reaches a target road section in a target travel path, current image data of the target road section acquired by an image acquisition device is acquired; and determining whether the target road section meets the path optimization condition according to the current image data, and if so, planning a new target travel path for the target robot to enable the target robot to avoid the target road section. The technical scheme solves the problem of congestion caused by external factors in the traveling process of the target robot, and achieves the purposes of saving time and improving efficiency in the working process of the target robot.
In an optional embodiment, the determining whether the target road segment meets a path optimization condition according to the current image data includes: determining action posture information of an environmental user in the target road section according to the current image data; and determining whether the target road section meets a path optimization condition according to the action gesture data.
Specifically, motion gesture information of an environmental user in the current image data is identified, user intention is judged according to the motion gesture information, and whether the running directions of the environmental user and the target robot conflict is determined according to the user intention. Still taking a restaurant scene as an example, the action gesture information of the environmental user can be actions, gestures and the like of the environmental user, and the user intention can be checkout at a cash register, material taking at a material desk, dining table collecting or ground cleaning and the like. The road section occupation time of the user can be determined according to the user intention, and if the road section occupation time of the user is greater than the time threshold, the path optimization condition is determined to be met; otherwise, determining that the path optimization condition is not satisfied.
The relationship between the intention of the user and the occupied duration of the road section can be obtained through a large amount of data statistics and through a machine learning mode, and can also be set manually through a preset standard, wherein the preset standard can be the duration of the estimated occupation according to the intention of the user according to experience. For example, the user's checkout counter, which is obtained by machine learning or manual setting, takes 10 seconds, and the table is picked up or the floor is cleaned for 20 seconds. The time length threshold value can be determined according to the current people flow and the historical trip data.
And determining the action gesture information of the environmental user in the target road section through the current image data and determining the intention of the user according to the action gesture data, so as to judge whether the target road section meets the path optimization condition, improve the accuracy of the path optimization judgment mode and improve the working efficiency of the robot.
Example two
The second embodiment of the present invention provides a travel method of a robot based on the first embodiment. Fig. 2A is a flowchart of a travel method of a robot according to a second embodiment of the present invention. Referring to fig. 2A, the method specifically includes the steps of:
s210, when a target robot reaches a marking point position associated with a target road section in a target travel path, acquiring current image data of the target road section acquired by an image acquisition device; wherein the annotation point location belongs to an adjacent road segment preceding the target road segment.
Optionally, by adding a labeling point to the target road section in the target travel path, judging whether the target road section meets the path optimization condition at the labeling point. The marking point position associated with the target road section belongs to the adjacent road section before the target road section, but does not belong to the target road section, that is, the target robot reaches the marking point position associated with the target road section before entering the target road section. The marking points of the target road sections belong to adjacent road sections before the target road sections, and the marking points of different target road sections can be the same or different. Referring to fig. 2B, the labeling points of the target link OA and the target link OB are the same, and are both labeling point 1.
When the target robot runs to the marked point of the target road section, the judgment of whether the path optimization condition is met is triggered, and the current road condition of the target road section can be prejudged in advance, so that whether the target robot continues to use the target road section is judged.
In the moving process of the target robot using the target travel path, whether the target robot reaches the marking point of the target road section can be detected in real time, and when the target robot reaches the marking point of the target road section, the current image data of the target road section acquired by the image acquisition device is acquired and used for determining whether path optimization is carried out or not according to the current image data of the target road section. The current image data can be obtained through direct interaction with the image collector, and also can be obtained through a cloud server or a local server.
S220, determining whether the target road section meets a path optimization condition according to the current image data;
and S230, if the travel path is met, planning a new target travel path for the target robot, so that the target robot avoids the target road section.
Referring to fig. 2C, in a specific example, the starting position of the target robot is an O point, the end position is a point No. 1, and the candidate travel path may include a candidate link AB and a candidate link BD, and may further include a candidate link AC and a candidate link CD. The marking point location 1 may be a marking point location associated with the candidate road segment AB and the candidate road segment AC, and the marking point location 2 may be a marking point location associated with the candidate road segment CD; moreover, the image collectors associated with different candidate segments may be the same or different, and the collection area of the image collectors can cover all candidate segments. Taking the example that the target travel path comprises a candidate road segment AC and a candidate road segment CD, when the target robot starts from a starting position O and reaches a labeling point 1, that is, before the target road segment AC is reached, current image data of the target road segment AC acquired by an image acquisition device is acquired, whether the target road segment AC meets a path optimization condition is determined according to the current image data, if the path optimization condition is met, a new target travel path is planned for the target robot, so that the target robot avoids the target road segment AC, for example, the new target travel path is re-planned and can comprise a candidate road segment AB and a candidate road segment BD, and the candidate road segment AB and the candidate road segment BD are used as new target road segments. If the path optimization condition is not met, the target robot continues to advance along the current target road section AC until the next marking point 2 is reached, and whether the target road section CD meets the path optimization condition is continuously judged.
In an alternative embodiment, the acquiring the current image data of the target road segment acquired by the image acquirer includes: acquiring a current image part corresponding to the target road section acquired by the image acquisition unit; the image collector fixedly collects image data of a preset area, and the preset area comprises a target road section.
The position and/or the acquisition area of the image collector can be a fixed preset position and/or a preset area, and the image collector can be specifically arranged at the fixed preset position according to the requirement, and the angle direction is fixed; the preset area can be set in advance according to the requirement, and the image collector is fixed at a preset position corresponding to the preset area. And acquiring image data of at least one candidate road section in a preset area through an image acquisition device at a preset position, wherein the position of the candidate road section in the preset area is relatively fixed. The preset position and the preset area can be adjusted manually according to requirements, the preset area comprises at least one candidate road section, and the candidate road section comprises a target road section.
The method comprises the steps of collecting a current image of a preset area through an image collector, obtaining a current image part corresponding to a target road section from the current image of the preset area, and determining current image data of the target road section according to the obtained current image part of the target road section. The method for acquiring the current image data of the target road section in a targeted manner is realized by acquiring the current image part corresponding to the target road section and determining the current image data of the target road section, so that the acquisition efficiency of the current image data of the target road section is improved.
In an alternative embodiment, the area information of the target road section in the image data is marked in advance according to the image data of the image collector, and the corresponding image data is extracted according to the area information and is used as the current image data of the target road section. The image data acquired by the image acquisition unit comprises image data of at least one candidate road section, wherein the candidate road section comprises a target road section. The information of the area where the target road section is located in the image data can be marked in advance according to the image data of at least one candidate road section in the image collector. And extracting the image data of the target road section according to the marked area information of the target road section, and taking the image data of the target road section as the current image data of the target road section. According to the scheme, the current image data of the target road section is determined by marking the information of the area where the target road section is located in the image data in advance, so that the accuracy of the current image data is improved.
According to the method, when the target robot reaches the marking point position associated with the target road section in the target travel path, the current image data of the target road section collected by the image collector is obtained, so that the image data of the target road section can be obtained in advance at a specific position before the target road section is reached, and preparation work is carried out for judging whether the target road section meets the path optimization condition better or not.
Example III
In the third embodiment of the present invention, an additional step is performed on the first embodiment, and fig. 3 is a flowchart of a travel method of the robot provided in the third embodiment of the present invention. The embodiment is applicable to the travel situation of the robot in the scene of the congested road section, the method can be executed by a travel device of the robot, the device can be realized in a software and/or hardware mode, and the method specifically comprises the following steps:
s310, acquiring current image data of a target road section acquired by an image acquisition device before the target robot reaches the target road section in a target travel path.
S320, determining whether the target road section meets the path optimization condition according to the current image data.
And S330, if the travel path is met, planning a new target travel path for the target robot, so that the target robot avoids the target road section.
S340, determining whether abnormal congestion occurs in the target road section according to the current travel data of the target robot in the target road section.
The current travel data may include a travel track of the target robot on the target road section, a current travel duration, a current travel speed, and the like, and the abnormal congestion refers to difficulty in traveling of the target robot on the target road section, for example, the travel speed is less than a speed threshold.
In an alternative embodiment, determining whether the target road segment is abnormally congested according to current travel data of the target robot on the target road segment includes: if the current running time of the target robot on the target road section is longer than the running time threshold of the target road section, determining that the target road section is congested, and starting timing; after the timing reaches the waiting time, if the current running speed of the target robot on the target road section is smaller than the speed threshold value of the target road section, determining that abnormal congestion occurs on the target road section.
Specifically, the driving duration threshold value may be determined according to a historical average speed, when the current driving duration of the target robot in the target road section is longer than the driving duration threshold value of the target road section, it is determined that congestion occurs in the target road section, the target robot starts waiting for timing, the timing time may be set manually in advance, and the timing purpose is to detect whether abnormal congestion occurs in the road section. After the timing reaches the preset waiting time, if the current running speed of the target robot on the target road section is smaller than the speed threshold value of the target road section, determining that abnormal congestion occurs on the target road section. The method has the advantages that the congestion condition of the current target road section can be accurately judged, the target robot responds differently to the situation that the target road section is congested and the situation that the target robot responds abnormally is congested, the problem that the robot is difficult to travel due to the fact that the road condition of the current road section cannot be judged in the traveling process of the congested road section is solved, and the traveling efficiency of the robot is improved.
Optionally, after the timing reaches the waiting duration, if the current running speed of the target robot on the target road section is smaller than the speed threshold of the target road section, determining that abnormal congestion occurs on the target road section. The speed threshold of the target road section is determined according to the average running speed of the historical robot in the target road section in the same period of history and the historical congestion probability of the target road section.
Specifically, the speed threshold may be set manually based on the average running speed and the historical congestion probability, where the historical congestion probability is the probability that the historical contemporaneous robot congests on the road section in the same period, and the historical congestion probability may be obtained based on statistics of historical data. When the historical congestion probability is large, the speed threshold may be set to a value less affected by the average running speed, i.e., the speed threshold is set to be small. When the historical congestion probability is small, the speed threshold may be set to a value that is greatly affected by the average running speed, i.e., the speed threshold is set to be large, and is set in accordance with the average running speed. The method has the advantages that the speed threshold can be determined by combining the average running speed and the historical congestion probability of the target road section, and the accuracy of setting the speed threshold is improved.
And S350, if abnormal congestion occurs in the target road section, controlling the target robot to return to the adjacent marking point according to the target travel path.
If abnormal congestion of the target road section is detected, the target robot is controlled to fall back to the adjacent marking point according to the target travel path. It should be noted that, if the target robot fails to return to the labeling point, it is determined that an accident occurs and a warning is triggered to the fact that the robot may be jammed or jammed, and manual intervention is required.
In a specific example, the target robot is congested when the target robot does not reach the next marking point in the driving process of the target road section, and waits for timing in situ at the moment, and if the road section is smooth before the timing is finished, the target robot continues to use the target travel path. And if the timing is finished and the current speed is smaller than the speed threshold value of the target road section, determining that the target road section is abnormally congested, and returning the target robot to the adjacent marking point. If the target robot fails to return to the adjacent marking point position, the accident situation of the target robot is determined to need manual intervention processing.
S360, acquiring candidate travel paths of adjacent marked points, and selecting a new target travel path for the target robot from the candidate travel paths except the target travel path.
And after the target robot returns to the adjacent marking point due to abnormal congestion of the target road section, selecting a new target travel path for the target robot from the candidate travel paths except the target travel path. Specifically, the travel time of the candidate travel path is selected from the history planning record, the selected travel time is the least as a new target travel path, the target robot does not need to be re-planned, the travel time of the target robot is saved, and the working efficiency of the robot is improved.
In an alternative embodiment, the method further comprises: planning at least one candidate travel path for the target robot; selecting a target travel path for the target robot from the candidate travel paths according to the historical traffic state of the candidate travel road sections in the candidate travel paths in the same historical period; the historical traffic state is determined according to historical image data of the candidate travel road sections in the same period of the history.
Specifically, at least one candidate travel path may be planned for the target robot according to the link to which the starting position of the target robot belongs, the link to which the target position belongs, the connection relationship between different candidate travel links, attribute information of each candidate travel link, and the like. Wherein the attribute information may include at least one of link position information or link condition information, etc.
By way of example, the traffic state of the target robot in each time period of the candidate travel section may be acquired in real time, and the acquired traffic state data may be stored in a database, a cloud server, or a local server of the target robot.
The history synchronization may be a period of time that is before and periodically regular with the travel time of the target robot. For example, if the travel time is a period of 10 to 12 am on monday, the history contemporaneous may be a period of 10 to 12 am on other dates to which monday belongs. The historical traffic state can be a congestion state or a clear state, and specifically can be determined according to historical image data of the candidate travel road sections in the same period of history. For example, historical people traffic data for a candidate travel segment that is contemporaneous in history may be determined from the historical image data. And selecting a target travel path for the target robot from the candidate travel paths according to the historical people flow data. By means of the mode that the candidate travel road sections in the candidate travel paths are in the history passing state in the same history period, the target travel paths are selected for the target robots from the candidate travel paths, accurate determination of the target travel paths is achieved, and therefore travel efficiency of the target robots is improved.
According to the technical scheme, the problem that the target robot cannot cope with the congestion situation in the running process is solved by judging whether the target robot has abnormal congestion in the running process of each road section and re-planning the route when the target robot has abnormal congestion, and the work efficiency of the target robot is improved.
Example IV
Fig. 4 is a schematic structural diagram of a travel device of a robot according to a fourth embodiment of the present invention. The travel device of the robot provided by the embodiment of the invention can execute the travel method of the robot provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. The device can be realized in a software and/or hardware mode, as shown in fig. 4, and the travel device of the robot specifically comprises: an image data acquisition module 410, an optimization condition determination module 420, and a path planning module 430.
The image data acquisition module 410 is configured to acquire current image data of a target road segment acquired by the image acquisition device before the target robot reaches the target road segment in the target travel path;
an optimization condition determining module 420, configured to determine, according to the current image data, whether the target road segment meets a path optimization condition;
and the path planning module 430 is configured to plan a new target travel path for the target robot when the target road section meets a path optimization condition, so that the target robot avoids the target road section.
Optionally, the image data acquisition module 410 is specifically configured to:
when a target robot reaches a marking point position associated with a target road section in a target travel path, acquiring current image data of the target road section acquired by an image acquisition device; wherein the annotation point location belongs to an adjacent road segment preceding the target road segment.
Optionally, the optimization condition determining module 420 includes:
a gesture information determining unit, configured to determine action gesture information of an environmental user in the target road section according to the current image data;
and the optimization condition determining unit is used for determining whether the target road section meets the path optimization condition according to the action gesture data.
Optionally, the image data acquisition module 410 is specifically configured to:
acquiring a current image part corresponding to the target road section acquired by the image acquisition unit; the image collector fixedly collects image data of a preset area, and the preset area comprises the target road section.
Optionally, the image data acquisition module 410 is specifically configured to:
and marking the area information of the target road section in the image data in advance according to the image data of the image collector, and extracting the corresponding image data according to the area information, wherein the image data is used as the current image data of the target road section.
Optionally, the apparatus further comprises:
the abnormal congestion determining module is used for determining whether abnormal congestion occurs in the target road section according to the current travel data of the target robot in the target road section;
the travel path returning module is used for determining that abnormal congestion occurs in the target road section, if the abnormal congestion position has no marking point, the target robot is controlled to return to an adjacent marking point according to the target travel path;
the target path acquisition module is used for acquiring candidate travel paths of adjacent marked points and selecting a new target travel path for the target robot from the candidate travel paths except the target travel paths.
Optionally, the abnormal congestion determining module includes:
the timing unit is started, and is used for determining that the target road section is congested and starting timing if the current running time of the target robot on the target road section is longer than the running time threshold of the target road section;
and the abnormal congestion determining unit is used for determining that abnormal congestion occurs in the target road section if the current running speed of the target robot in the target road section is smaller than the speed threshold value of the target road section after the timing reaches the waiting time.
Optionally, the speed threshold of the target road section is determined according to the average running speed of the historical robot in the target road section in the same period of history and the historical congestion probability of the target road section.
Optionally, the apparatus further comprises:
the candidate travel path planning module is used for planning at least one candidate travel path for the target robot;
the target travel path selection module is used for selecting a target travel path for the target robot from the candidate travel paths according to the historical traffic state of the candidate travel road sections in the candidate travel paths in the same historical period; the historical traffic state is determined according to historical image data of the candidate travel road sections in the same period of the history.
According to the embodiment of the invention, before a target robot reaches a target road section in a target travel path, current image data of the target road section acquired by an image acquisition device is acquired; and determining whether the target road section meets the path optimization condition according to the current image data, and if so, planning a new target travel path for the target robot to enable the target robot to avoid the target road section. The technical scheme solves the problem of congestion caused by external factors in the traveling process of the target robot, and achieves the purposes of saving time and improving efficiency in the working process of the target robot.
Example five
Fig. 5 is a schematic diagram of a travel structure of a robot according to a fifth embodiment of the present invention. The travel device of a robot is an electronic device and fig. 5 shows a block diagram of an exemplary electronic device 500 suitable for use in implementing embodiments of the present invention. The electronic device 500 shown in fig. 5 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 5, the electronic device 500 is embodied in the form of a general purpose computing device. The components of electronic device 500 may include, but are not limited to: one or more processors or processing units 501, a system memory 502, and a bus 503 that connects the various system components (including the system memory 502 and processing units 501).
The system memory 502 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 504 and/or cache memory 505. Electronic device 500 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 506 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard disk drive"). Although not shown in fig. 5, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 503 through one or more data medium interfaces. Memory 502 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 508 having a set (at least one) of program modules 507 may be stored, for example, in memory 502, such program modules 507 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 507 typically perform the functions and/or methods of the described embodiments of the invention.
The electronic device 500 may also communicate with one or more external devices 509 (e.g., keyboard, pointing device, display 510, etc.), one or more devices that enable a user to interact with the electronic device 500, and/or any device (e.g., network card, modem, etc.) that enables the electronic device 500 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 511. Also, electronic device 500 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 512. As shown in fig. 5, the network adapter 512 communicates with other modules of the electronic device 500 over the bus 503. It should be appreciated that although not shown in fig. 5, other hardware and/or software modules may be used in connection with electronic device 500, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 501 executes various functional applications and data processing by running a program stored in the system memory 502, for example, implements a method of a robot provided by an embodiment of the present invention.
Example six
The sixth embodiment of the present invention further provides a storage medium containing computer executable instructions, on which a computer program is stored, which when executed by a processor, implements a travel method of a robot as provided in the embodiments of the present invention, including:
before a target robot reaches a target road section in a target travel path, acquiring current image data of the target road section acquired by an image acquisition device;
determining whether the target road section meets a path optimization condition according to the current image data;
if yes, planning a new target travel path for the target robot, so that the target robot avoids the target road section.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Claims (11)
1. A method of travel of a robot, comprising:
before a target robot reaches a target road section in a target travel path, acquiring current image data of the target road section acquired by an image acquisition device;
determining action posture information of an environmental user in the target road section according to the current image data;
judging the intention of a user according to the action gesture information;
determining the road section occupation time length of the user according to the user intention, and determining that the path optimization condition is met if the road section occupation time length of the user is greater than a time length threshold;
if yes, planning a new target travel path for the target robot, so that the target robot avoids the target road section.
2. The method of claim 1, wherein acquiring current image data of a target link acquired by an image collector before the target robot reaches the target link in a target travel path, comprises:
when a target robot reaches a marking point position associated with a target road section in a target travel path, acquiring current image data of the target road section acquired by an image acquisition device; wherein the annotation point location belongs to an adjacent road segment preceding the target road segment.
3. The method of claim 1, wherein the acquiring current image data of the target road segment acquired by an image acquirer comprises:
acquiring a current image part corresponding to the target road section acquired by the image acquisition unit; the image collector fixedly collects image data of a preset area, and the preset area comprises the target road section.
4. A method according to claim 3, further comprising:
and marking the area information of the target road section in the image data in advance according to the image data of the image collector, and extracting the corresponding image data according to the area information, wherein the image data is used as the current image data of the target road section.
5. The method as recited in claim 1, further comprising:
determining whether abnormal congestion occurs in a target road section according to current travel data of the target robot in the target road section;
if abnormal congestion occurs in the target road section, controlling the target robot to fall back to an adjacent marking point according to the target travel path;
and acquiring candidate travel paths of adjacent marked points, and selecting a new target travel path for the target robot from the candidate travel paths except the target travel path.
6. The method of claim 5, wherein determining whether the target link is abnormally congested based on current travel data of the target robot at the target link comprises:
if the current running time of the target robot on the target road section is longer than the running time threshold of the target road section, determining that the target road section is congested, and starting timing;
after the timing reaches the waiting time, if the current running speed of the target robot on the target road section is smaller than the speed threshold value of the target road section, determining that abnormal congestion occurs on the target road section.
7. The method of claim 6, wherein the speed threshold of the target link is determined based on an average travel speed of the historical robot at the target link and a historical congestion probability of the target link at the same time.
8. The method according to any one of claims 1-7, further comprising:
planning at least one candidate travel path for the target robot;
selecting a target travel path for the target robot from the candidate travel paths according to the historical traffic state of the candidate travel road sections in the candidate travel paths in the same historical period; the historical traffic state is determined according to historical image data of the candidate travel road sections in the same period of the history.
9. A travel device of a robot, comprising:
the image data acquisition module is used for acquiring current image data of a target road section acquired by the image acquisition device before the target robot reaches the target road section in the target travel path;
the optimization condition determining module is used for determining whether the target road section meets the path optimization condition according to the current image data;
the path planning module is used for planning a new target travel path for the target robot when the target road section meets the path optimization condition;
the optimization condition determining module is specifically configured to:
determining action posture information of an environmental user in the target road section according to the current image data;
judging the intention of a user according to the action gesture information;
and determining the road section occupation time length of the user according to the user intention, and determining that the path optimization condition is met if the road section occupation time length of the user is greater than a time length threshold.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of travel of the robot according to any of claims 1-8 when executing the program.
11. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a method of travelling a robot according to any one of claims 1-8.
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| CN106846846A (en) * | 2017-02-02 | 2017-06-13 | 南京交通职业技术学院 | A kind of robot system for dredging congestion |
| JP2019000940A (en) * | 2017-06-15 | 2019-01-10 | 株式会社日立製作所 | Robot control system, platform, robot, and method of controlling robot |
| US20210362337A1 (en) * | 2019-06-10 | 2021-11-25 | Lg Electronics Inc. | Intelligent robot device |
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