CN113867347A - Robot path planning method, device, management system and computer storage medium - Google Patents

Robot path planning method, device, management system and computer storage medium Download PDF

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Publication number
CN113867347A
CN113867347A CN202111124278.6A CN202111124278A CN113867347A CN 113867347 A CN113867347 A CN 113867347A CN 202111124278 A CN202111124278 A CN 202111124278A CN 113867347 A CN113867347 A CN 113867347A
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China
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robot
obstacle
path
obstacle avoidance
tracking
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CN202111124278.6A
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Chinese (zh)
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冉东来
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Shenzhen Youibot Robotics Technology Co ltd
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Shenzhen Youibot Robotics Technology Co ltd
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Priority to CN202111124278.6A priority Critical patent/CN113867347A/en
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
    • G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
    • G05D1/0251—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle

Abstract

The application discloses a robot path planning method, which comprises the following steps: acquiring a tracking path; controlling the robot to travel according to the tracking path; if an obstacle is detected on the tracking path in the moving process of the robot, acquiring obstacle information of the obstacle; generating an obstacle avoidance path according to the obstacle information, wherein the end point of the obstacle avoidance path is arranged on the tracking path; and controlling the robot to move from the current position to a tracking path according to the obstacle avoidance path. The application also provides a robot path planning device, a management system and a computer readable storage medium. The robot can flexibly avoid obstacles, and the interference of obstacle avoidance of the robot to multi-machine scheduling is avoided.

Description

Robot path planning method, device, management system and computer storage medium
Technical Field
The present disclosure relates to the field of robot technologies, and in particular, to a method, an apparatus, a management system, and a computer storage medium for planning a path of a robot.
Background
At present, the path planning of the robot mainly comprises two methods, namely fixed path navigation and autonomous navigation. The fixed path navigation is to determine a path traveled by the robot in advance, so that the robot travels based on the preset path, the flexibility of the fixed path navigation is poor, the robot can only travel according to the preset path strictly, and the robot stops traveling and gives an alarm when encountering an obstacle; autonomous navigation means that a robot can acquire laser radar data in real time, autonomously plan a path from a current position to a target position, and dynamically adjust a traveling path according to the laser radar data, where the traveling path of the robot has a great uncertainty in autonomous navigation, for example, in an environment where multiple robots exist at the same time, the robot in an autonomous navigation state not only needs to travel from the current position to the target position, avoid obstacles that may be encountered during traveling, but also needs to prevent obstacles that hinder the traveling of other robots in the same environment, and thus autonomous navigation may bring difficulty to multi-robot scheduling.
Disclosure of Invention
The application provides a robot path planning method, a device, a management system and a computer storage medium, which are used for solving the problems that the existing robot path planning method is lack of obstacle avoiding capability and poor in flexibility.
In a first aspect, the present application provides a robot path planning method, including:
acquiring a tracking path;
controlling the robot to travel according to the tracking path;
if an obstacle is detected on the tracking path in the moving process of the robot, acquiring obstacle information of the obstacle;
generating an obstacle avoidance path according to the obstacle information, wherein the end point of the obstacle avoidance path is arranged on the tracking path;
and controlling the robot to move from the current position to a tracking path according to the obstacle avoidance path.
In a second aspect, the present application further provides a robot path planning apparatus, which includes a processor and a memory; the memory is used for storing a computer program; the processor is configured to execute the computer program and to implement the robot path planning method as described above when executing the computer program.
In a third aspect, the present application further provides a management system, where the management system includes a plurality of robots and the robot path planning apparatus described above.
In a fourth aspect, the present application also provides a computer-readable storage medium, which, when executed by one or more processors, causes the one or more processors to perform the steps of the robot path planning method as described above.
In the robot path planning method described in the present application, the robot can acquire obstacle information when detecting an obstacle, and adjust a path of travel according to the obstacle information, so that the robot bypasses the obstacle and returns to a tracking path. The robot can adjust the traveling path when detecting the obstacle in the traveling process, and flexibly bypasses the obstacle on the tracking path.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a block diagram of a management system provided in accordance with an embodiment of the present application;
fig. 2 is a schematic flowchart of a robot path planning method according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating steps provided in one embodiment of the present application for determining obstacle information;
FIG. 4 is a schematic diagram of a density clustering algorithm provided in one embodiment of the present application;
fig. 5 is a flowchart illustrating steps of determining an obstacle avoidance path according to an embodiment of the present application;
FIG. 6 is a schematic flow chart diagram illustrating the steps for determining an expansion region provided by one embodiment of the present application;
fig. 7 is a flowchart illustrating steps for traveling according to an obstacle avoidance path according to an embodiment of the present application;
fig. 8 is a schematic block diagram of a structure of a robot path planning apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The embodiment of the application provides a robot path planning method, a device, a management system and a computer storage medium, so as to improve the flexibility of avoiding obstacles of a robot.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a block diagram illustrating a management system according to an embodiment of the present application.
Specifically, the management system comprises a plurality of robots and a robot path planning device. In practical application, a user presets a tracking path of the robot in a management system, the robot is controlled to travel according to the tracking path, if the robot detects an obstacle on the tracking path in the traveling process, obstacle information is acquired, an obstacle avoiding path is generated according to the obstacle information, the end point of the obstacle avoiding path is arranged on the tracking path, and the robot travels from the current position to the tracking path according to the obstacle avoiding path, so that the robot can flexibly avoid the obstacle in the traveling path.
For example, in an environment where a plurality of robots are present at the same time, the robots can be prevented from traveling and interfering with each other, and the order of multi-robot work can be ensured.
As shown in fig. 1, in the management system corresponding to the present application, the management system 100 includes a plurality of robots, such as a first robot 101, a second robot 102, … …, an nth robot 10N, and a robot path planning apparatus 10, where the robot path planning apparatus 10 may be disposed on at least one of the robots, or may be a separate device other than the robot.
The robot path planning device 10 controls the robot to travel according to the preset tracking path by acquiring the preset tracking path, and when the robot detects an obstacle on the tracking path, generates an obstacle avoidance path according to obstacle information to achieve the effect of flexibly avoiding the obstacle by the robot. The terminal point of the obstacle avoidance path is arranged on the tracking path, and the plurality of robots are prevented from generating interference due to obstacle avoidance.
Referring to fig. 2, fig. 2 is a schematic flow chart of a robot path planning method according to an embodiment of the present disclosure.
Step S201, acquiring a tracking path.
In an environment where tasks need to be performed by robots, multiple robots may be present simultaneously. For example, when a robot is used to transport materials in a warehouse or a production shop, a plurality of robots performing tasks of transporting materials may be simultaneously present in the warehouse or the production shop. In order to ensure that a plurality of robots do not interfere with each other when performing tasks in the same environment, a tracking path that each robot travels when performing tasks may be planned in advance and introduced to the corresponding robot.
In practical applications, when the robot is required to perform a task, a user may operate on the relevant system to determine a tracking path of the robot on the relevant system, so that the robot can travel according to a preset tracking path.
For example, the tracking path may be represented in various types of maps, such as a Metric Map (Metric Map), a Topological Map (Topological Map), or a Semantic Map (Semantic Map), and is not limited herein.
The tracking path of the robot can be preset, so that the running of each robot can be ensured not to generate interference in the environment where a plurality of robots exist simultaneously, and the orderly operation of a plurality of robots can be ensured.
Step S202, controlling the robot to move according to the tracking path;
in practical application, a user operates on a relevant system, the tracking path of each robot is determined, and the tracking path is guided into the corresponding robot according to the operation of the user on the relevant system.
The tracking path determines a traveling route of the robot for executing the task, and the robot is controlled to travel according to the traveling route of the robot determined by the tracking path.
The robot is controlled to move according to the preset tracking path, so that the robots are ensured not to generate interference in an environment where a plurality of robots exist simultaneously, mutual obstruction and even collision among the robots are avoided, and the orderly operation of multiple robots is ensured.
Step S203, if an obstacle is detected on the tracking path in the moving process of the robot, acquiring obstacle information of the obstacle.
The robot is equipped with at least one detection device, and the presence or absence of an obstacle on the tracking path can be detected by the at least one detection device. If the robot detects an obstacle on the tracking path through the at least one detection device in the process of traveling, acquiring obstacle information of the obstacle through the at least one detection device.
In particular, the at least one detection device is at least one lidar. Of course, the present invention is not limited to this, and other devices may be used to detect an obstacle and obtain obstacle information of the obstacle. For example, the at least one detection device may also be a 3D camera.
In some embodiments, it is determined that the robot detects the obstacle if the distance between the robot and the obstacle is less than a preset threshold. The distance between the robot and the obstacle is the minimum distance between the peripheral outline of the robot close to the obstacle and the outline of the obstacle.
The preset threshold value is preset according to actual requirements. For example, the preset threshold may be set to 1 meter. And if the detection device detects the obstacle within a range of 1 m away from the robot on the tracking path, judging that the robot detects the obstacle.
For example, the distance between the robot and the obstacle may be acquired by at least one detection device mounted on the robot. For example, when the detection device is a laser radar, the laser radar can emit laser light, and the distance between the robot and the obstacle is determined according to the time difference between the emitted laser light and the reflected laser light.
In some embodiments, if it is determined that the robot detects an obstacle, that is, an object whose distance from the robot is less than a preset threshold value is detected on the tracking path, the robot is controlled to decelerate. In some embodiments, the robot returns to normal speed after receiving and moving along a new obstacle avoidance path.
Whether obstacles exist in a certain range on a tracking path is detected through the detection device, and when the obstacles exist in the certain range on the tracking path, the robot is controlled to decelerate, so that the robot and the obstacles can be prevented from colliding, and the robot is prevented from being damaged.
For example, since the obstacle information is determined by processing point cloud data obtained by scanning an obstacle with a laser radar, when determining the obstacle information, please refer to fig. 3, and fig. 3 is a flowchart illustrating a step of determining the obstacle information according to an embodiment of the present application.
S301, acquiring point cloud data of the obstacle through the laser radar;
and S302, determining the obstacle information according to the point cloud data based on a density clustering algorithm.
The point cloud data (point cloud data) is a set of vectors in a three-dimensional coordinate system, and the laser radar can emit laser and acquire the point cloud data according to the laser reflected by an object. And the point cloud data returned by the laser radar scanning the obstacle is processed, so that the obstacle information of the obstacle can be determined, and the robot can plan an obstacle avoidance path according to the obstacle information.
A Density Clustering algorithm, such as a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Clustering algorithm, may cluster the point cloud data according to the Density of the point cloud data distribution to obtain a Clustering result, where the Clustering result is a point cloud data set used for representing an obstacle. Compared with other clustering algorithms, the density clustering algorithm does not need to determine the number and the shape of the obstacles in advance, can effectively filter noise in data, avoids the clustering result from being influenced by abnormal data, and can more accurately cluster point cloud data.
Specifically, the density clustering algorithm is based on preset parameters: maximum radii (Eps) and minimum points (minPts), aggregating point cloud data. For example, X ═ { X for a point cloud data set1，x2，……，xNAnd one point cloud data in the point cloud data is the neighborhood of the point cloud data, wherein the distance between the point cloud data and the point cloud data is less than the maximum radius. If the number of elements included in the neighborhood of the point cloud data is larger than the minimum point, the point cloud data is a core point; if the number of elements included in the neighborhood of the point cloud data is not more than the minimum point, but the neighborhood of the point cloud data includes a core point, the point cloud data is a boundary point; otherwise, the point cloud data is a noise point. If X, y belongs to X, X is a core point and y is located in the neighborhood of X, y is directly reached from X density; if p is1，p2，……，pmIs epsilon X (wherein m is more than or equal to 2) and satisfies pi+1Is from piDirect density, i is 1, 2 … …, m-1, then pmIs from p1The density can be reached. The point cloud data set C clustered into the same class (cluster) should satisfy: for X, y ∈ X, if X ∈ C, and y is reachable from X density, then y ∈ C.
Referring to fig. 4, fig. 4 is a schematic diagram of a density clustering algorithm according to an embodiment of the present application. As shown in fig. 4, 40 represents a neighborhood of the point cloud data 401. If the minimum point is set to have a value of 8 in the embodiment of fig. 3, the number of objects included in the neighborhood 40 of the point cloud data 401 is greater than 8, and the point cloud data 401 is a core point.
In some embodiments, after the clustering result is obtained by the density clustering algorithm, the obstacle contour is determined according to the clustering result based on the convex hull algorithm.
Specifically, since the clustering result is a point cloud data set used for representing the obstacle, the convex hull algorithm can determine a maximum convex polygon from the point cloud data in the clustering result, and the convex polygon is used for representing the outline of the obstacle. For example, the convex Hull algorithm may be a Jarvis step algorithm (Jarvis March), an Incremental algorithm (Incremental Method), a fast convex Hull algorithm (Quick Hull), a Divide and Conquer algorithm (Divide and Conquer), a Graham scanning algorithm (Graham Scan), a monotonic Chain algorithm (Monotone Chain), and the like, which can determine the maximum convex polygon according to the point cloud data, and details are not repeated herein.
And S204, generating an obstacle avoidance path according to the obstacle information, wherein the end point of the obstacle avoidance path is arranged on the tracking path.
The terminal point of the obstacle avoidance path is arranged on the tracking path, and the robot can return to the preset tracking path after passing around the obstacle, namely after the robot finishes traveling from the current position to the tracking path according to the obstacle avoidance path, and continue traveling according to the tracking path, so that the interference of obstacle avoidance on the multi-machine scheduling of the robot is avoided.
Since the obstacle avoidance path is generated according to the obstacle information, please refer to fig. 5 when the obstacle avoidance path is generated, and fig. 5 is a flowchart illustrating a step of generating the obstacle avoidance path according to an embodiment of the present application.
Step S501, determining the end point of the obstacle avoidance path according to the obstacle information and the tracking path;
and S502, determining the obstacle avoidance path according to the end point and the current position of the robot.
After the obstacle information is acquired, determining the end point of the obstacle avoidance path according to the acquired obstacle information and a preset tracking path. The terminal point of the obstacle avoidance path is located on the obstacle avoidance path, so that the robot can bypass the obstacle in the process of traveling from the current position to the terminal point and finally return to the tracking path, and the interference of the obstacle avoidance of the robot on the multi-machine operation is prevented. And determining an obstacle avoidance route of the robot for avoiding the obstacle according to the terminal, the current position of the robot and the obstacle information.
For example, since the obstacle avoidance path is determined according to the current position of the robot and the end point of the obstacle avoidance path, when the obstacle avoidance path is determined, please refer to fig. 6, and fig. 6 is a flowchart illustrating a step of determining the obstacle avoidance path according to an embodiment of the present application.
S601, determining an expansion area of the obstacle according to the obstacle information and preset expansion parameters;
step S602, determining the obstacle avoidance path according to the obstacle expansion area and the tracking path, wherein the robot is not overlapped with the expansion area on the obstacle avoidance path.
And determining an obstacle expansion area according to the obstacle outline determined in the step S302 and a preset expansion parameter. The obstacle inflation area is used to simulate the area occupied by the obstacle in the tracking path.
For example, when an obstacle is detected, the point cloud data acquired by the detection device can determine the obstacle profile of the obstacle in the direction perpendicular to the robot, but cannot determine the obstacle length in the direction parallel to the robot. Therefore, an expansion parameter is preset, and the expansion parameter is used for simulating the extending length of the obstacle outline in the direction close to the robot and the direction far away from the robot, so that the expansion area of the obstacle is simulated.
The expansion parameter is set according to actual requirements, for example, the expansion parameter may be set to 0.6 meter, and then according to the obstacle information determined in step S302, the obstacle profile is extended by 0.6 meter in the direction approaching the robot and the direction away from the robot, respectively, so as to simulate the area occupied by the obstacle.
In some preferred embodiments, the value of the expansion parameter is smaller than a preset threshold value used by the robot to judge whether an obstacle is detected, so as to ensure the safety of robot driving.
The robot can be prevented from colliding with the obstacle by simulating the obstacle expansion area, and the end point of the obstacle avoiding route of the robot is determined by the obstacle outline so as to plan the obstacle avoiding route according to the obstacle expansion area.
In some embodiments, the determining the obstacle avoidance path according to the end point and the current position of the robot includes: and determining the obstacle avoidance path according to the terminal point and the current position of the robot based on a heuristic search algorithm.
A heuristic search algorithm, such as the a-algorithm, can search for the best path from the current location to the destination based on the obstacle information.
Specifically, the idea of the heuristic search algorithm is to determine an optimal path among paths from the current position to the end point based on a preset evaluation function f (n) ═ g (n) + h (n). Where n denotes any one position on the path from the current position to the end point, f (n) denotes a minimum distance estimate from the current position to the end point via the position n, g (n) denotes a minimum distance from the current position to the position n, and h (n) denotes a minimum estimated distance of the path from the position n to the end point. And traversing the path from the current position to the terminal point according to the evaluation function, and determining the path with the minimum estimated value f (n) as an obstacle avoidance path, thereby achieving the technical effect of determining the obstacle avoidance path with the shortest distance for the robot.
And S205, controlling the robot to move from the current position to a tracking path according to the obstacle avoidance path.
The terminal point of the obstacle avoidance path is arranged on the tracking path, so that the robot can return to the preset tracking path after traveling according to the obstacle avoidance path, and continue to travel according to the tracking path, thereby avoiding the interference of obstacle avoidance on the multi-machine scheduling of the robot.
In some embodiments, the robot needs to continue to perform obstacle detection during the process of traveling according to the obstacle avoidance path, so as to prevent a new obstacle from being encountered in the obstacle avoidance path, or the obstacle avoidance path planning needs to be performed again when the obstacle area simulated according to the expansion parameters is smaller than the area actually occupied by the obstacle. Therefore, when the robot travels according to the obstacle avoidance path, please refer to fig. 7, and fig. 7 is a flowchart illustrating steps of traveling according to the obstacle avoidance path according to an embodiment of the present application.
S701, in the process that the robot travels according to the obstacle avoidance path, obstacle detection is carried out;
step S702, if an obstacle is detected in the process that the robot travels according to the current obstacle avoidance path, a new obstacle avoidance path is generated.
For example, since the obstacle avoidance path in steps S601 to S602 is determined according to the obstacle expansion area, and the obstacle expansion area is simulated according to the preset expansion parameter, in order to prevent the obstacle expansion area simulated according to the expansion parameter from not truly reflecting the area occupied by the obstacle, so that the robot collides with the obstacle, or prevent the robot from detecting a new obstacle during the process of traveling according to the obstacle avoidance path, the robot still needs to perform obstacle detection by the detection device during the process of traveling according to the obstacle avoidance path.
If the robot detects an obstacle on the obstacle avoidance path in the process of traveling according to the current obstacle avoidance path, that is, an object whose distance from the robot is smaller than a preset threshold value is detected on the obstacle avoidance path of the robot, the operations of the steps S301 to S302, the steps S501 to S502 and the steps S601 to 602 are executed again, a new obstacle avoidance path is generated, and the robot travels according to the new obstacle avoidance path. The end point of the new obstacle avoidance path is on the previous obstacle avoidance path or on the tracking path.
As can be understood, in the process of traveling according to the new obstacle avoidance path, the operations of steps S701-S702 are executed in a loop until the robot does not detect an obstacle and returns to the preset tracking path; or until the robot completes the process of traveling according to the tracking path.
In the above-described robot path planning method, apparatus, system, and storage medium, the robot may acquire obstacle information when detecting an obstacle, and adjust a tracking path of travel according to the obstacle information, so that the robot bypasses the obstacle. The robot can flexibly bypass the obstacle by adjusting the traveling path in the traveling process, and meanwhile, the interference on multi-machine scheduling is avoided.
As shown in fig. 8, fig. 8 is a schematic block diagram of a structure of a robot path planning apparatus according to an embodiment of the present application.
The robot path planning apparatus 10 includes a memory 11 and a processor 12, where the processor 11 and the memory 12 are connected by a system bus 13, and the memory 11 may include a nonvolatile storage medium and an internal memory.
The non-volatile storage medium may store a computer program. The computer program comprises program instructions which, when executed, cause the processor 11 to perform any of the methods for robot path planning.
The processor 12 is used for providing computing and control capability, and supporting the operation of the whole cloud server.
The internal memory provides an environment for the execution of a computer program on a non-volatile storage medium, which, when executed by the processor, causes the processor to perform any of the robot path planning methods.
Those skilled in the art will appreciate that the configuration shown in fig. 8 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation on the terminal to which the present application is applied, and that a particular terminal may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It should be understood that the Processor 12 may be a Central Processing Unit (CPU), and that the Processor 12 may be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. The general purpose processor 12 may be a microprocessor or the processor may be any conventional processor or the like.
Wherein, in an embodiment, the memory has stored therein a computer program which, when executed by the processor 12, causes the processor to carry out the steps of:
acquiring a tracking path;
controlling the robot to travel according to the tracking path;
if an obstacle is detected on the tracking path in the moving process of the robot, acquiring obstacle information of the obstacle;
generating an obstacle avoidance path according to the obstacle information, wherein the end point of the obstacle avoidance path is arranged on the tracking path;
and controlling the robot to move from the current position to a tracking path according to the obstacle avoidance path.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
detecting an obstacle in the process that the robot travels according to the obstacle avoidance path;
and if the robot detects the obstacle in the process of traveling according to the current obstacle avoidance path, generating a new obstacle avoidance path.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring point cloud data of the obstacle through the laser radar;
and determining the obstacle information according to the point cloud data based on a density clustering algorithm.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
determining the end point of the obstacle avoidance path according to the obstacle information and the tracking path;
and determining the obstacle avoidance path according to the terminal and the current position of the robot.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
determining an expansion area of the obstacle according to the obstacle information and preset expansion parameters;
and determining the obstacle avoidance path according to the obstacle expansion area and the tracking path, wherein the robot is not overlapped with the expansion area on the obstacle avoidance path.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and determining the obstacle avoidance path according to the terminal point and the current position of the robot based on a heuristic search algorithm.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and if the distance between the robot and the obstacle is smaller than a preset threshold value, judging that the robot detects the obstacle.
It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working process of the data processing described above may refer to the corresponding process in the foregoing embodiment of the robot path planning method, and details are not described herein again.
Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, where the computer program includes program instructions, and a method implemented when the program instructions are executed may refer to various embodiments of the data processing method of the present application.
The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device.
It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments. While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A method of robot path planning, the method comprising:
acquiring a tracking path;
controlling the robot to travel according to the tracking path;
if an obstacle is detected on the tracking path in the moving process of the robot, acquiring obstacle information of the obstacle;
generating an obstacle avoidance path according to the obstacle information, wherein the end point of the obstacle avoidance path is arranged on the tracking path;
and controlling the robot to move from the current position to a tracking path according to the obstacle avoidance path.
2. The method for planning a robot path according to claim 1, wherein the robot proceeds from a current position to a tracking path according to the obstacle avoidance path, and includes:
detecting an obstacle in the process that the robot travels according to the obstacle avoidance path;
and if the robot detects the obstacle in the process of traveling according to the current obstacle avoidance path, generating a new obstacle avoidance path.
3. A robot path planning method according to claim 1 or 2, characterized in that the robot carries a lidar;
the acquiring of the obstacle information of the obstacle includes:
acquiring point cloud data of the obstacle through the laser radar;
and determining the obstacle information according to the point cloud data based on a density clustering algorithm.
4. The robot path planning method according to claim 3, wherein generating an obstacle avoidance path according to the obstacle information includes:
determining the end point of the obstacle avoidance path according to the obstacle information and the tracking path;
and determining the obstacle avoidance path according to the terminal and the current position of the robot.
5. The robot path planning method according to claim 4, wherein generating an obstacle avoidance path according to the obstacle information includes:
determining an expansion area of the obstacle according to the obstacle information and preset expansion parameters;
and determining the obstacle avoidance path according to the obstacle expansion area and the tracking path, wherein the robot is not overlapped with the expansion area on the obstacle avoidance path.
6. The robot path planning method according to claim 4, wherein the determining the obstacle avoidance path according to the end point and the current position of the robot includes:
and determining the obstacle avoidance path according to the terminal point and the current position of the robot based on a heuristic search algorithm.
7. A robot path planning method according to any of claims 4-6, characterized in that the detection of an obstacle when the robot is travelling according to the tracking path comprises:
and if the distance between the robot and the obstacle is smaller than a preset threshold value, judging that the robot detects the obstacle.
8. A robot path planning apparatus, comprising a processor and a memory; the memory is used for storing a computer program; the processor is for executing the computer program and when executing the computer program implementing the robot path planning method according to any of claims 1 to 7.
9. A management system, characterized in that the management system comprises a number of robots and a robot path planning apparatus according to claim 8.
10. A computer-readable storage medium, which, when executed by one or more processors, causes the one or more processors to perform the steps of the robot path planning method according to any one of claims 1 to 7.
CN202111124278.6A 2021-09-24 2021-09-24 Robot path planning method, device, management system and computer storage medium Pending CN113867347A (en)

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