Local obstacle avoidance path planning method, system and device and storage medium

Abstract

The invention discloses a local obstacle avoidance path planning method, system and device and a storage medium, and the method comprises the following steps: S1, constructing a path search tree, judging whether there is an obstacle in the path search process, and generating a path if there is no obstacle; and S2, if the obstacles exist, adopting a beetle antennae expansion strategy to avoid the obstacles, adding expansion nodes avoiding the obstacles into the path search tree until the whole path search is completed, and generating a path. Obstacles can be bypassed in advance, and the original Connect thought is improved to help the two path search trees to quickly establish connection. The probability of invalid expansion in the non-target direction is reduced, the connection efficiency under the barrier-free path condition is also improved, and meanwhile the calculation cost of equal step length expansion of the RRT-Connect algorithm between path search trees is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of path planning and relates to a local obstacle avoidance path planning method, system, equipment and storage medium. Background Art

[0002] Industrial robots have become an important part of modern manufacturing due to their high precision, stability and efficiency. They are widely used in assembly, welding, handling and other fields, greatly improving the level of production automation, reducing labor costs, and promoting the rapid development of intelligent manufacturing. Industrial robot path planning is an important field in robotics technology. It involves how to plan and optimize the path of robot movement so as to complete the task in the shortest time, safely and efficiently.

[0003] The Rapidly-exploring Random Tree (RRT) algorithm is a path planning method based on random sampling proposed by LaValle in 1998. The algorithm constructs a random tree structure, starts from the starting point, and expands toward the target area until a path connecting the starting point and the target point is found. It has the characteristics of rapid exploration, applicable to high-dimensional space and complex environment, and easy to implement. However, the RRT algorithm cannot guarantee to find the optimal path, and there may be problems of low search efficiency in narrow channels and dense obstacle environments. The Bidirectional Rapidly-exploring Random Trees (Bi-RRT) algorithm is an improved version of the RRT algorithm. Unlike the RRT algorithm that expands unidirectionally from the starting point to the target point, the Bidirectional RRT algorithm starts from the starting point and the target point at the same time, constructs two random trees until they meet, and has the characteristics of improving search efficiency, reducing search space, and being applicable to symmetric environments. The Rapidly-exploring Random Tree Connect (RRT-Connect) algorithm is a further improvement of the bidirectional RRT algorithm, which aims to improve the efficiency of path planning. The algorithm starts from the starting point and the target point at the same time, builds two random trees, and tries to connect them at each step. If the connection is successful, a path is found; if it fails, the random tree continues to be expanded. The RRT-Connect algorithm improves search efficiency and is applicable to various environments and constraints, but it still has the defect of falling into local optimal values. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a local obstacle avoidance path planning method, system, device and storage medium, which can bypass obstacles in advance and improve the original Connect idea to help two path search trees quickly establish a connection.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A local obstacle avoidance path planning method includes the following processes: S1, build a path search tree to determine whether there are obstacles during the path search process. If not, generate a path; S2, if there is an obstacle, the beetle whisker extension strategy is used to avoid the obstacle, and the extension node that avoids the obstacle is added to the path search tree until the entire path search is completed and the path is generated.

[0006] Preferably, in S1, the process of constructing the path search tree is as follows: starting from the starting point x start Build a path search starting tree for the root node T start , with the target point x goal Build a path search target tree for the root node T goal .

[0007] Furthermore, the path search process is as follows: Generate sampling points in the workspace x rand , find the tree T start Middle distance x rand The most recent x near1 , find the tree T goal Middle distance x rand The most recent x near2 , x rand represents the randomly generated nodes in each iteration, x near1 Indicates the starting tree x rand The nearest point, x near2 Indicates the target tree x rand The nearest point.

[0008] Further, judge x near1 Directly expand to x near2 Whether it encounters an obstacle, if no collision occurs, then connect x near1 and x near2 And generate a line from x start arrivex goal Path.

[0009] Furthermore, in S2, by x near1 , x near2 and x rand Three points determine the expansion plane. x near1 Two longhorn beetle whiskers are generated at x near2 Generate two beetle whiskers at each location, generate an extension node on each beetle whisker, determine whether the path collides with an obstacle, and add the nodes that have not collided to their corresponding path search trees.

[0010] Further, the distance between the extended node and another search tree is calculated. If the distance length is less than the set threshold and there is no collision on the path, a path is generated. Otherwise, it is determined whether the number of searches is less than the set upper limit. If so, return to S1. Otherwise, the step ends and the search fails.

[0011] Preferably, after the path is generated, a path shortening strategy is used to optimize the path.

[0012] A local obstacle avoidance path planning system, comprising: The path search tree module is used to build a path search tree to determine whether there are obstacles during the path search process. If not, a path is generated; The beetle whisker module is used to avoid obstacles by adopting the beetle whisker extension strategy if there are obstacles, and add the extension nodes for avoiding obstacles to the path search tree until the entire path search is completed and the path is generated.

[0013] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the local obstacle avoidance path planning method when executing the computer program.

[0014] A computer-readable storage medium stores a computer program, which implements the steps of the local obstacle avoidance path planning method when executed by a processor.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The obstacle avoidance path planning algorithm proposed in this method is inspired by the antenna mechanism of longhorn beetles, which effectively solves the problem that the path search tree gets stuck when approaching obstacles. By simulating the behavior of longhorn beetle antennae sensing odors, two expansion directions are generated in the algorithm. Due to the existence of a certain angle between the expansion directions, the expansion nodes are expanded to both sides, which can bypass obstacles to a certain extent and increase the effective sampling rate. At the same time, the difference in expansion directions is conducive to opening up the search space and helping to find an open area to connect the target point. The essence of Connect is to approach the target point, but the gradual expansion mechanism of the same step length not only increases the computational cost, but also may have the risk of approaching obstacles. By introducing longhorn beetle antennae, it helps the expansion nodes find a more open area, improves the Connect idea, and directly judges whether there is an obstacle between the nearest node and the target point. If there is, the expansion connection is not performed. The application of this strategy not only reduces the probability of invalid expansion in non-target directions, but also improves the connection efficiency under obstacle-free path conditions, and reduces the computational cost of the RRT-Connect algorithm in equal step expansion between path search trees. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the local obstacle avoidance path planning method based on RRT-Connect optimized by longicorn whiskers of the present invention; Figure 2 A schematic diagram of an extended surface constructed for the beetle whisker algorithm of the present invention; Figure 3 A schematic diagram of the expansion direction of the beetle whisker algorithm of the present invention; Figure 4 It is a schematic diagram of the Rodrigo rotation of the present invention; Figure 5 A schematic diagram of an extended node is constructed for the beetle whisker algorithm of the present invention; Figure 6 It is a schematic diagram of the obstacle avoidance method of the beetle whisker algorithm of the present invention; Figure 7 It is a schematic diagram of the Connect connection of the beetle whisker algorithm of the present invention; Figure 8 This is a schematic diagram of the path shortening method of the beetle whisker algorithm of the present invention. DETAILED DESCRIPTION

[0017] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0018] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs. The terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or can communicate with each other; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. The term "and / or" used in this article includes any and all combinations of one or more related listed items. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0020] The disclosure below provides many different embodiments or examples to realize different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplicity and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides various specific examples of processes and materials, but those of ordinary skill in the art can be aware of the application of other processes and / or the use of other materials.

[0021] like Figure 1As shown, the present invention provides an industrial robot local obstacle avoidance path planning method based on the improved RRT-Connect algorithm to solve the problem of how to plan a better path for the industrial robot in a complex environment based on the RRT-Connect algorithm.

[0022] The RRT-Connect algorithm improves the problems of long search time and falling into local optimality of the basic RRT algorithm. However, due to its rapid expansion idea, the path search tree easily approaches obstacles, and the effective expansion direction is limited. More sampling is required to jump out of the obstacle area, which increases the number of sampling and calculation costs. At the same time, the extended connection idea of ​​Connect is essentially to quickly approach the target point, but it will fall into local optimality when there are obstacles. This method can bypass obstacles in advance by introducing the idea of ​​the longicorn beard search algorithm, and improves the original Connect idea to help the two path search trees quickly establish connections.

[0023] The path search steps of the RRT-Connect algorithm based on longhorn beard optimization are as follows: (1) x start Build a path search tree for the root node T start ,by x goal Build a path search tree for the root node T goal .

[0024] (2) Generate sampling points in the workspace x rand , find the tree T start Middle distance x rand The most recent x near1 , find the tree T goal Middle distance x rand The most recent x near2 .

[0025] (3) Judgment x near1 Directly expand to x near2 Whether it encounters an obstacle, if no collision occurs, then connect x near1 and x near2 And generate a line from x start arrive x goalIf the initial path is , go to step (7); otherwise, go to step (4).

[0026] (4) By x near1 , x near2 and x rand Three points determine the expansion plane. x near1 Two longhorn beetle whiskers are generated at x near2 Generate two longicorn whiskers at each location, generate an extension node on each longicorn whisker, determine whether the path collides with an obstacle, and add the nodes that have not collided to the corresponding path search tree; (5) Calculate the distance between the extended node and the other search tree. If the distance length is less than the set threshold and there is no collision on the path, generate the initial path and go to step (7). Otherwise, go to step (6).

[0027] (6) If the number of searches is less than the set upper limit, return to step (2); otherwise, end the step and the search fails.

[0028] (7) Shorten and optimize the initial path to find x start Connect x goal The trajectory path.

[0029] in: x start Indicates the starting point, x goal represents the target point, T start Represents the path search tree starting from the starting point, T goal Represents a path search tree starting from the target point. x rand represents the randomly generated nodes in each iteration, x near1 Indicates the starting tree x rand The nearest point, x near2 Indicates the target tree x rand The nearest point.

[0030] In step (4), the beetle beard expansion strategy includes the following process: 1) Determine the expansion plane In the traditional RRT series of algorithms, the nearest node of each path search tree will expand toward the target point, and the expansion direction is the nearest target point, and there is no need to determine the expansion plane. The RRT-Connect algorithm based on beetle whisker optimization uses two beetle whiskers to determine the expansion direction, so it is necessary to consider the plane problem where the expansion direction is located.

[0031] like Figure 2 As shown, first generate a random sampling point x rand , and then in x start Search the tree for the path to the root node T start Calculate the nearest node x near1 , in x goal Search the tree for the path to the root node T goal Calculate the nearest node x near2 , calculated by the formula x near1 point to x rand Vector , by calculating x near1 Point to x near2 Vector , by calculating x near1 , x near2 and x rand The unit normal vector corresponding to the plane where the three points are located .

[0032] (1) (2) (3) Assumptions =( a,b,c )and x near1 =( x 0, y 0, z 0), then the final point method indicates the point x near1 and perpendicular to the vector The plane, i.e. the node x near1 Extension surface β .

[0033] (4) 2) Determine the expansion direction Compared with the traditional algorithm that only generates one expansion node each time, during the iteration process of the RRT-Connect algorithm based on beetle beard optimization, one node will determine two expansion directions within the expansion plane and further expand two new nodes according to the expansion directions.

[0034] like Figure 3 As shown, in x start The path search tree for the root node is x near point to x rand is the initial direction, and deflects to both sides in the expansion plane θ Angle, generate the left beard and the right beard of the longhorn beetle, which are the nodes x near The expansion direction is calculated using the Rodriguez rotation formula, as shown in the following formula: (5) The vector can be obtained by calculating the Rodriguez rotation formula Around a unit vector Rotation θ The new vector after the angle This calculation uses the rotation axis and rotation angle θ Determine the rotation matrix, parameterizing the angle and direction of the rotation. Figure 4 Rotate the schematic for Rodrigo.

[0035] According to the Rodriguez rotation formula, the unit normal vector As the rotation axis, the vector Rotate to the left θ The expansion direction is obtained after the angle , the vector Rotate in the same direction -θ The expansion direction is obtained after the angle .

[0036] (6) (7) 3) Expand new nodes like Figure 5 As shown, from the nearest node x near Initially, the step length is λ , generate a new extension node along the left beard of the longhorn x left , generate a new extension node along the right beard of the longhornx right .

[0037] First, the extension vector of the beetle whiskers is standardized as follows: (8) (9) Then, from x near Move forward along the unit vector λ , and get two extended nodes: (10) (11) Finally, the judgment node x near Whether the line connected to the expanded node collides with an obstacle. If no collision occurs, the node is successfully expanded and added to the path search tree, otherwise the current expanded node is discarded.

[0038] In the path search process of the RRT-Connect algorithm, the introduction of the Connect idea is intended to promote the rapid expansion of the random search tree to the target point, which is conducive to reducing the search time. However, if the algorithm encounters an obstacle during the expansion process, the nodes of the search tree will be close to the obstacle, and it may fall into frequent and inefficient expansion attempts in order to escape from the area around the obstacle. Although this mechanism speeds up the connection speed of the search tree, it also increases the time cost of invalid sampling.

[0039] The obstacle avoidance path planning algorithm proposed in this method is inspired by the antenna mechanism of the longhorn beetle, which effectively solves the problem that the path search tree gets stuck when approaching obstacles. By simulating the behavior of the antennae of the longhorn beetle to sense the smell, two expansion directions are generated in the algorithm. Since there is a certain angle between the expansion directions, the expansion nodes are expanded to both sides, which can bypass obstacles to a certain extent and increase the effective sampling rate. At the same time, the difference in expansion directions is conducive to opening up the search space and helping to find an open area to connect the target point. The essence of Connect is to approach the target point, but the gradual expansion mechanism of the same step length not only increases the computational cost, but also may have the risk of approaching obstacles. The introduction of the longhorn beetle's antennae helps the expansion node find a more open area, improves the Connect idea, and directly judges whether there is an obstacle between the nearest node and the target point. If there is, no expansion connection is performed.

[0040] Figure 6It shows that when there is an obstacle between the nearest node and the random sampling point, the beetle beard algorithm has a certain angle between its tentacles, so that the two expansion directions do not all point directly to the obstacle, and there is a higher probability that one of the expansion directions will not pass through the obstacle, thereby reducing the generation of invalid sampling points. Figure 7 As shown in the figure, when there are no obstacles between the nearest nodes of the two trees, the algorithm attempts to directly connect the two nodes with a certain probability. This is to optimize the rapid establishment of the path and improve the efficiency of the algorithm. The application of this strategy not only reduces the probability of invalid expansion in non-target directions, but also improves the connection efficiency under obstacle-free path conditions, while reducing the computational cost of the RRT-Connect algorithm in equal-step expansion between path search trees.

[0041] In step (7), the specific process of path shortening optimization is as follows: In the implementation of the RRT-Connect algorithm, the beetle whisker search strategy was integrated to improve the algorithm's ability to avoid obstacles and find a better path. However, the resulting path is often tortuous, indicating that there is room for further optimization and shortening of the path.

[0042] The path shortening strategy is used to optimize the path. The specific steps of the algorithm are as follows: (1) In the generated initial path, the nodes in the path are numbered in order to obtain the path sequence [ x 1, x 2, .. . , x n ]; (2) Traverse the nodes in the sequence one by one, starting with the current node x i Centered on λ is the search radius, and the neighbor nodes of the current node are searched; (3) When the node x i When the front neighbor node and the back neighbor node are both within the search range, the farthest front neighbor node is selected. x front and the farthest neighbor node x behind , if the node x front and nodes x behind If the line connecting the two nodes does not collide with obstacles, remove other nodes between the two nodes; (4) Update the path sequence and finally obtain a new path.

[0043] like Figure 8 As shown, the initial path sequence from the initial point to the target point is [ x 1,x 2, x 3, x 4, x 5, x 6, x 7], and the new path sequence is obtained by using the path shortening strategy [ x 1, x 2, x 3, x 4, x 5, x 6, x 7], the new path is reduced in length relative to the initial path.

[0044] The following are device embodiments of the present invention, which can be used to perform method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0045] In yet another embodiment of the present invention, a local obstacle avoidance path planning system is provided, which can be used to implement the above-mentioned local obstacle avoidance path planning method. Specifically, the local obstacle avoidance path planning system includes a path search tree module and a longicorn whisker module.

[0046] Among them, the path search tree module is used to construct a path search tree to determine whether there are obstacles during the path search process. If not, a path is generated.

[0047] The beetle whisker module is used to avoid obstacles if there are any. The beetle whisker extension strategy is used to avoid the obstacles. The extension nodes that avoid the obstacles are added to the path search tree until the entire path search is completed and the path is generated.

[0048] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the local obstacle avoidance path planning method, including: S1, constructing a path search tree, judging whether there is an obstacle during the path search process, and if not, generating a path; S2, if there is an obstacle, using the beetle beard extension strategy to avoid the obstacle, adding the extension node that avoids the obstacle to the path search tree, until the entire path search is completed, and the path is generated.

[0049] In another embodiment, the present invention further provides a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0050] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the local obstacle avoidance path planning method in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and the following steps are executed: S1, constructing a path search tree to determine whether there are obstacles during the path search process, and if not, generating a path; S2, if there are obstacles, adopting the beetle whisker extension strategy to avoid the obstacles, and adding the extension nodes that avoid the obstacles to the path search tree until the entire path search is completed and the path is generated.

[0051] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0052] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0053] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0054] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0055] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0056] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0057] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0058] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0059] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

[0060] It should be understood that the above description is for illustration and not for limitation. Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of this patent should not be determined with reference to the above description, but rather with reference to the preceding claims and the full scope of equivalents to which such claims are entitled. For the purpose of comprehensiveness, all articles and references, including disclosures of patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the preceding claims is not intended to be a waiver of such subject matter, nor should it be considered that the applicant has not considered such subject matter to be part of the disclosed inventive subject matter.

Claims

1. A local obstacle avoidance path planning method, characterized in that: The process includes: S1, build a path search tree to determine whether there are obstacles during the path search process. If not, generate a path; S2, if there is an obstacle, the beetle whisker extension strategy is used to avoid the obstacle, and the extension node that avoids the obstacle is added to the path search tree until the entire path search is completed and the path is generated.

2. The local obstacle avoidance path planning method according to claim 1, characterized in that: In S1, the process of constructing the path search tree is as follows: starting from the starting point x start Build a path search starting tree for the root node T start , with the target point x goal Build a path search target tree for the root node T goal .

3. The local obstacle avoidance path planning method according to claim 2, characterized in that: The path search process is: Generate sampling points in the workspace x rand , find the tree T start Middle distance x rand The most recent x near1 , find the tree T goal Middle distance x rand The most recent x near2 , x rand represents the randomly generated nodes in each iteration, x near1 Indicates the starting tree x rand The nearest point, x near2 Indicates the target tree x rand The nearest point.

4. The local obstacle avoidance path planning method according to claim 3, characterized in that: judge x near1 Directly expand to x near2 Whether it encounters an obstacle, if no collision occurs, then connect x near1 and x near2 And generate a line from x start arrive x goal Path.

5. The local obstacle avoidance path planning method according to claim 3, characterized in that: In S2, through x near1 , x near2 and x rand Three points determine the expansion plane. x near1 Two longhorn beetle whiskers are generated at x near2 Generate two beetle whiskers at each location, generate an extension node on each beetle whisker, determine whether the path collides with an obstacle, and add the nodes that have not collided to their corresponding path search trees.

6. The local obstacle avoidance path planning method according to claim 5, characterized in that: Calculate the distance between the extended node and another search tree. If the distance length is less than the set threshold and there is no collision on the path, generate a path. Otherwise, determine whether the number of searches is less than the set upper limit. If so, return to S1. Otherwise, end the step and the search fails.

7. The local obstacle avoidance path planning method according to claim 1, characterized in that: After the path is generated, the path shortening strategy is used to optimize the path.

8. A local obstacle avoidance path planning system, characterized in that: include: The path search tree module is used to construct a path search tree to determine whether there are obstacles during the path search process. If not, a path is generated; The beetle whisker module is used to avoid obstacles by adopting the beetle whisker extension strategy if there are obstacles, and add the extension nodes for avoiding obstacles to the path search tree until the entire path search is completed and the path is generated.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the local obstacle avoidance path planning method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the local obstacle avoidance path planning method as described in any one of claims 1 to 7 are implemented.

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