Mobile robot path planning method, mobile robot and storage medium

Abstract

The present invention discloses a path planning method for a mobile robot, a mobile robot, and a storage medium. The method comprises: offline establishment of a sparse directed connected graph based on a probability map; and online path search based on the sparse directed connected graph to plan the optimal path for the mobile robot. Embodiments of the present invention utilize the sparse directed connected graph for path search, improving the speed and efficiency of path search compared to traditional methods by establishing a sparse directed connected graph offline based on a probability map and performing online path search based on the sparse directed connected graph to plan the optimal path for the mobile robot.

Description

Technical Field

[0001] The present invention relates to the field of mobile robots, and in particular to a path planning method for a mobile robot, a mobile robot, and a storage medium. Background Art

[0002] Mobile robots are a very important part of the robotics field, and the path planning algorithm of mobile robots is one of the key links in realizing autonomous navigation of robots.

[0003] A mobile robot's path planning algorithm typically refers to the process of planning a route from a given location on a map to another specified location. In the field of mobile robotics, the most common form of map is the probabilistic map.

[0004] However, due to the large amount of data in probability maps, the calculation time of traditional path search algorithms (such as the A* algorithm) is very high, the search speed is very slow, and the search efficiency is low. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a path planning method for a mobile robot, a mobile robot, and a storage medium, which utilize a sparse directed connected graph for path search, thereby improving the speed and efficiency of path search.

[0006] The technical solutions adopted by the present invention to solve the above technical problems are as follows:

[0007] According to one aspect of an embodiment of the present invention, a path planning method for a mobile robot is provided, the method comprising:

[0008] Offline construction of sparse directed connected graph based on probabilistic map;

[0009] A path search is performed online based on the sparse directed connected graph to plan an optimal path for the mobile robot.

[0010] In one possible design, the offline establishment of a sparse directed connected graph based on a probability map includes:

[0011] Sampling the posture of the mobile robot on the probability map to obtain all sampling points;

[0012] Perform collision detection on all sampling points, remove the sampling points that collide, and obtain all retained sampling points;

[0013] Using mobile robot kinematics and collision detection, connectivity detection is performed on all retained sampling points to obtain a directed connected graph;

[0014] The directed connected graph is thinned to obtain a sparse directed connected graph.

[0015] In a possible design, performing collision detection on all sampling points, eliminating sampling points that collide, and obtaining all retained sampling points includes:

[0016] Determine the geometric center of the mobile robot and establish the coordinate system of the mobile robot body;

[0017] Based on the coordinate system of the mobile robot body, the outline of the mobile robot is measured and mapped, and the geometric outline of the mobile robot is obtained according to the outline simulation of the mobile robot;

[0018] Applying the mobile robot poses at all the sampling points to the geometric profile of the mobile robot;

[0019] Sampling is performed in two-dimensional space within the geometric outline of the mobile robot to obtain all sampling points within the geometric outline of the mobile robot;

[0020] Each sampling point within the geometric outline of the mobile robot is matched with the corresponding probability map to check whether there is an obstacle at the probability map corresponding to the sampling point; if there is an obstacle, it is considered that a collision is detected and the sampling point is removed; otherwise, there is no obstacle and the sampling point is retained; repeat this step to obtain all retained sampling points.

[0021] In one possible design, the method utilizes the kinematics and collision detection of the mobile robot to perform connectivity detection on all retained sampling points to obtain a directed connected graph; including:

[0022] Determine the kinematic equations of the mobile robot;

[0023] Create a directed graph and add all retained sampling points as nodes to the directed graph;

[0024] For each node in the directed graph, determining the connectivity between the node and other surrounding nodes to obtain a directed connected graph;

[0025] The directed connected graph is thinned to obtain a sparse directed connected graph.

[0026] In a possible design, for each node in the directed graph, determining the connectivity between the node and other surrounding nodes to obtain a directed connected graph includes:

[0027] Select two nodes;

[0028] Substitute the positions of the two selected nodes into the kinematic equations of the mobile robot and solve them to obtain the solution;

[0029] Drawing a path of the mobile robot in two-dimensional space according to the solution result;

[0030] Perform uniform sampling on the path to obtain all sampling points;

[0031] Performing collision detection on each sample point, and if no collision is detected, creating a directed edge between the two selected nodes in the directed graph;

[0032] Repeat the above steps to detect mutually reachable nodes from all nodes, and add directed edges between these detected nodes in the directed graph to obtain a directed connected graph.

[0033] In one possible design, the step of sparsifying the directed connected graph to obtain a sparse directed connected graph includes:

[0034] Determine whether a sampling point in the directed connected graph is an empty point, and obtain all non-empty points and all empty points in the directed connected graph;

[0035] All empty points and directed edges connected to the empty points are removed from the directed connected graph;

[0036] The connectivity between all non-empty points in the directed connected graph is detected to obtain a sparse directed connected graph.

[0037] In one possible design, detecting the connectivity between all non-empty points in the directed connected graph to obtain a sparse directed connected graph includes:

[0038] Select a non-empty point from all non-empty points, use the A* algorithm to search on the directed connected graph whether the non-empty point can reach other non-empty points, and if the non-empty point can reach another non-empty point, add a directed edge between the two non-empty points;

[0039] Repeat the above steps to detect mutually reachable non-empty points from all non-empty points, and add directed edges between these detected non-empty points to the directed connected graph to obtain a sparse directed connected graph.

[0040] In one possible design, the online path search based on the sparse directed connected graph to plan the optimal path for the mobile robot includes:

[0041] Given a starting point and an end point, connectivity detection is performed with nodes in a certain area on the sparse directed connected graph respectively;

[0042] If connectivity exists, adding the starting point and the end point to the sparse directed connected graph;

[0043] The optimal path of the mobile robot is planned by searching from the starting point and the end point respectively using a preset algorithm.

[0044] According to another aspect of an embodiment of the present invention, a mobile robot is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of a mobile robot path planning method provided in an embodiment of the present invention.

[0045] According to another aspect of an embodiment of the present invention, a storage medium is provided, characterized in that a path planning method program for a mobile robot is stored on the storage medium, and when the path planning method program for a mobile robot is executed by a processor, the steps of the path planning method for a mobile robot provided in an embodiment of the present invention are implemented.

[0046] Compared to related technologies, embodiments of the present invention provide a path planning method for a mobile robot, a mobile robot, and a storage medium. The method comprises: offline creation of a sparse directed connected graph based on a probabilistic map; and online path search based on the sparse directed connected graph to plan the optimal path for the mobile robot. By utilizing the sparse directed connected graph for path search, the embodiments of the present invention improve the speed and efficiency of path search compared to traditional methods (such as the A* algorithm) by offline creation of a sparse directed connected graph based on a probabilistic map and online path search based on the sparse directed connected graph to plan the optimal path for the mobile robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic flow chart of a path planning method for a mobile robot provided in an embodiment of the present invention.

[0048] Figure 2 A schematic diagram of a process for offline establishing a sparse directed connected graph based on a probability map in a path planning method for a mobile robot provided by an embodiment of the present invention.

[0049] Figure 3 In a path planning method for a mobile robot provided by an embodiment of the present invention, collision detection is performed on all sampling points, sampling points where collision occurs are eliminated, and a flow chart of all retained sampling points is obtained.

[0050] Figure 4 In a mobile robot path planning method provided by an embodiment of the present invention, the kinematics and collision detection of the mobile robot are utilized to perform connectivity detection on all retained sampling points to obtain a flow chart of a directed connectivity graph.

[0051] Figure 5 A schematic flow chart of a mobile robot path planning method providing an embodiment of the present invention is provided for thinning the directed connected graph to obtain a sparse directed connected graph.

[0052] Figure 6 A schematic diagram of a process for performing online path search based on the sparse directed connected graph and planning the optimal path for the mobile robot in a path planning method for a mobile robot provided by an embodiment of the present invention.

[0053] Figure 7 A schematic structural diagram of a mobile robot provided by an embodiment of the present invention.

[0054] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0055] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer and more understandable, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0056] In the subsequent description, suffixes such as "module," "component," or "unit" used to represent elements are only used to facilitate the description of the present invention and have no specific meaning. Therefore, "module," "component," or "unit" can be used interchangeably.

[0057] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0058] In one embodiment, Figure 1 As shown, the present invention provides a path planning method for a mobile robot based on a probability map, the method comprising:

[0059] S1. Establish a sparse directed connected graph offline based on the probability map.

[0060] S2. Perform path search online based on the sparse directed connected graph to plan the optimal path for the mobile robot.

[0061] In this embodiment, a sparse directed connected graph is established offline based on a probability map, and a path search is performed online based on the sparse directed connected graph to plan the optimal path for the mobile robot. Thus, the path search is performed using the sparse directed connected graph, which improves the speed and efficiency of the path search compared to traditional methods (such as the A* algorithm).

[0062] In one embodiment, Figure 2 As shown, in step S1, the offline establishment of a sparse directed connected graph based on the probability map includes:

[0063] S11. Sampling the posture of the mobile robot on the probability map to obtain all sampling points.

[0064] In this step, the probability map is already known, and the posture sampling of the mobile robot is performed on the known probability map. The sampling may be performed uniformly at fixed length and angle intervals, or randomly.

[0065] In this embodiment, to ensure effective sampling, a preset sampling method is used to sample the mobile robot's pose on the probability map, obtaining all sampling points. This preset sampling method combines the advantages of uniform sampling and random sampling. The sampling method is as follows: the probability map is divided into a number of S*S square areas. Within each square area, the mobile robot pose is randomly sampled N times (N is an integer). Each sampling generates a mobile robot pose, which includes the mobile robot's position and heading angle.

[0066] S12. Perform collision detection on all sampling points, remove sampling points where collision occurs, and obtain all retained sampling points.

[0067] In this step, collision detection is performed on all the sampling points obtained above, and the sampling points that collide are eliminated. Figure 3 As shown, specifically including:

[0068] S121. Determine the geometric center of the mobile robot and establish the coordinate system of the mobile robot body.

[0069] S122. Based on the mobile robot body coordinate system, map the outline of the mobile robot, and obtain the geometric outline of the mobile robot according to the outline simulation of the mobile robot.

[0070] Preferably, simulating the geometric outline of the mobile robot according to the outline of the mobile robot includes: using a circumscribed polygon or a circumscribed circle of the outline of the mobile robot to simulate the geometric outline of the mobile robot.

[0071] S123: Apply the postures of the mobile robot at all the sampling points to the geometric outline of the mobile robot.

[0072] S124. Perform uniform sampling or random sampling in two-dimensional space within the geometric outline of the mobile robot to obtain all sampling points within the geometric outline of the mobile robot.

[0073] S125. Each sampling point within the geometric outline of the mobile robot is matched to the corresponding probability map to check whether there is an obstacle at the probability map corresponding to the sampling point. If there is an obstacle, it is considered that a collision has been detected and the sampling point is discarded. Otherwise, if there is no obstacle, it is considered that no collision has been detected and the sampling point is retained.

[0074] Repeat step S125 to obtain all retained sampling points.

[0075] S13. Using the mobile robot kinematics and collision detection, perform connectivity detection on all retained sampling points to obtain a directed connectivity graph.

[0076] A sampling point is a posture of the mobile robot, and the connectivity between the sampling points indicates whether the mobile robot can move from one posture to another.

[0077] In this step, the connectivity between all the retained sampling points is checked. When making the connectivity judgment, it is necessary to check the kinematics and collision of the mobile robot. Figure 4 As shown, the specific method is:

[0078] S131. Determine the kinematic equations of the mobile robot.

[0079] The kinematic equations for a mobile robot describe how it moves from one position to another within a given timeframe, controlled by a control variable. These equations are determined based on the robot's kinematic type (e.g., differential drive or omnidirectional) and fixed parameters (e.g., wheel circumference or wheel spacing).

[0080] S132: Create a directed graph, and add all the retained sampling points as nodes to the directed graph.

[0081] S133: For each node in the directed graph, determine the connectivity between the node and other surrounding nodes to obtain a directed connected graph. The specific method is:

[0082] S1331. Select two nodes, which are respectively recorded as node a and node b.

[0083] S1332. Substitute the position and posture of node a and node b into the kinematic equation of the mobile robot to solve and obtain a solution.

[0084] S1333. Draw a path of the mobile robot in two-dimensional space according to the solution result.

[0085] S1334. Perform uniform sampling on the path to obtain all sampling points.

[0086] S1335: Perform collision detection on each sampling point as described in step S12. If no collision is detected, it is considered that the mobile robot can reach node b from node a, and a directed edge is created between node a and node b in the directed graph.

[0087] Repeat steps S1331-S1335 to detect mutually reachable nodes from all nodes, and add directed edges between these detected nodes in the directed graph to obtain a directed connected graph.

[0088] S14. Sparse the directed connected graph to obtain a sparse directed connected graph.

[0089] In this step, the directed connected graph is thinned by selectively removing nodes from the directed connected graph. Figure 5 As shown, the specific method is:

[0090] S141 , determining whether a sampling point in the directed connected graph is an empty point, and obtaining all non-empty points and all empty points in the directed connected graph.

[0091] If there is no obstacle within a radius of R meters at the spatial position corresponding to a sampling point in the directed connected graph, the sampling point is considered to be an empty point. In this way, all non-empty points and all empty points in the directed connected graph are determined.

[0092] S142: Remove all empty points and directed edges connected to the empty points from the directed connected graph.

[0093] Empty points do not play a big role in path search, and eliminating empty points can significantly improve the search speed.

[0094] S143 , detecting the connectivity between all non-empty points in the directed connected graph to obtain a sparse directed connected graph.

[0095] The specific method is as follows: select a non-empty point from all non-empty points, use the A* algorithm to search the directed connected graph to see if the non-empty point can reach other non-empty points. If the non-empty point can reach another non-empty point, add a directed edge between the two non-empty points. If the non-empty point cannot reach the other non-empty point, remove the non-empty point. For example, if non-empty point a can reach non-empty point b, add a directed edge between non-empty point a and non-empty point b.

[0096] Repeat step S143 to detect mutually reachable non-empty points from all non-empty points, and add directed edges between these detected non-empty points in the directed connected graph to obtain a sparse directed connected graph.

[0097] In one embodiment, Figure 6 As shown, in step S2, the online path search is performed based on the sparse directed connected graph to plan the optimal path of the mobile robot, including:

[0098] S21. Given a starting point and an end point, connectivity detection is performed with nodes in a certain area on the sparse directed connected graph.

[0099] In this step, a connectivity check is performed on a starting point and an end point with nodes within a certain area on the sparse directed connected graph. The method is:

[0100] The starting point and the end point are respectively regarded as nodes in the sparse directed connected graph, and the method described in step S133 is used to detect the connectivity between the node and other nodes within a radius R.

[0101] S22: If connectivity exists, the starting point and the end point are added to the sparse directed connected graph and the subsequent S23 operation is performed. Otherwise, the search fails and the search ends.

[0102] In this step, based on the connectivity test result of step S21, it is determined whether the starting point and the end point need to be added to the sparse directed connected graph. If the starting point and the end point both have connectivity with the nodes in the sparse directed connected graph, the starting point and the end point are added to the sparse directed connected graph. If the starting point and the end point do not have connectivity with the nodes in the sparse directed connected graph, the starting point and the end point are determined to be unreachable, and there is no path from the starting point to the end point. The search is determined to have failed, and the entire search process ends.

[0103] S23. Using a preset algorithm to search from the starting point and the end point respectively, to plan an optimal path for the mobile robot; wherein the preset algorithm includes at least one of the following algorithms: a bidirectional A* algorithm and a Dijkstra algorithm.

[0104] In this embodiment, the bidirectional A* algorithm is used as an example to perform the search. The search process of the Dijkstra algorithm is similar to the search process of the bidirectional A* algorithm.

[0105] In this step, a bidirectional A* algorithm is used to complete the search from the starting point to the end point using the sparse directed connected graph.

[0106] The bidirectional A* algorithm is an improvement on the A* algorithm and has better speed for general scenarios. The specific method is: search according to the bidirectional A* algorithm from the starting point and the end point respectively, wherein the search process from the starting point is along the forward direction of the sparse directed connected graph, and the search process from the end point is along the reverse direction of the sparse directed connected graph. The nodes visited during the search process from the starting point are all marked as "from the starting point", and the nodes visited during the search process from the end point are all marked as "from the end point". If a node marked as "from the end point" is reached during the search process from the starting point, or a node marked as "from the starting point" is reached during the search process from the end point, the search is successful. After the search is completed, the starting point and the end point, as well as the directed edges connected to them, are removed from the sparse directed connected graph, and the optimal path for the mobile robot is finally planned.

[0107] In addition, an embodiment of the present invention further provides a mobile robot, such as Figure 7 As shown, it includes: a memory, a processor, and one or more computer programs stored in the memory and executable on the processor. When the one or more computer programs are executed by the processor, the following steps of a path planning method for a mobile robot based on a probability map provided by an embodiment of the present invention are implemented:

[0108] S1. Establish a sparse directed connected graph offline based on the probability map.

[0109] S2. Perform path search online based on the sparse directed connected graph to plan the optimal path for the mobile robot.

[0110] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by the processor 901. The processor 901 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be performed by hardware integrated logic circuits or software instructions within the processor 901. The processor 901 may be a general-purpose processor, a DSP, or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The processor 901 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software modules may be located in a storage medium located in the memory 902. The processor 901 reads information from the memory 902 and, in conjunction with its hardware, completes the steps of the above method.

[0111] It can be understood that the memory 902 in the embodiment of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device; the volatile memory can be random access memory (RAM), by way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), etc. Memory), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), Direct Rambus Random Access Memory (DRRAM).The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0112] It should be noted that the above-mentioned mobile robot embodiment and method embodiment belong to the same concept, and their specific implementation process is detailed in the method embodiment, and the technical features in the method embodiment are correspondingly applicable in the mobile robot embodiment, which will not be repeated here.

[0113] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a program of a path planning method for a mobile robot based on a probability map is stored. When the path planning method for a mobile robot based on a probability map is executed by a processor, the following steps of the path planning method for a mobile robot based on a probability map provided in an embodiment of the present invention are implemented:

[0114] S1. Establish a sparse directed connected graph offline based on the probability map.

[0115] S2. Perform path search online based on the sparse directed connected graph to plan the optimal path for the mobile robot.

[0116] It should be noted that the embodiment of the path planning method for a mobile robot based on a probability map on the above-mentioned computer-readable storage medium and the method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment, and the technical features in the method embodiment are correspondingly applicable in the embodiment of the above-mentioned computer-readable storage medium, which will not be repeated here.

[0117] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0118] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0119] Through the description of the above embodiments, those skilled in the art will clearly understand that the above-mentioned embodiments and methods can be implemented by software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, or optical disk) and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0120] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A path planning method for a mobile robot, characterized in that: The method comprises: S1. Offline establishment of a sparse directed connected graph based on the probability map, including: S11. Sampling the posture of the mobile robot on the probability map to obtain all sampling points; S12, performing collision detection on all sampling points, eliminating sampling points where collision occurs, and obtaining all retained sampling points; S13. Using the kinematics and collision detection of the mobile robot, a connectivity test is performed on all the retained sampling points to obtain a directed connectivity graph, including: S131. Determining the kinematic equation of the mobile robot; S132. Creating a directed graph and adding all the retained sampling points as nodes to the directed graph; S133. For each node in the directed graph, determining the connectivity between the node and other surrounding nodes to obtain a directed connectivity graph, including: S1331. Selecting two nodes; S1332. Substituting the postures of the two selected nodes into the kinematic equation of the mobile robot respectively. , and obtaining a solution result; S1333, drawing a path of the mobile robot in two-dimensional space according to the solution result; S1334, uniformly sampling on the path to obtain all sampling points; S1335, performing a collision check on each sampling point, and if no collision is detected, creating a directed edge between the two selected nodes in the directed graph; repeating steps S1331-S1335, detecting mutually reachable nodes from all nodes, and adding directed edges between these detected nodes in the directed graph to obtain a directed connected graph; S14, performing sparse processing on the directed connected graph to obtain a sparse directed connected graph; S2. Perform path search online based on the sparse directed connected graph to plan the optimal path for the mobile robot.

2. The method according to claim 1, wherein The step S12 of performing collision detection on all sampling points, eliminating sampling points that collide, and obtaining all retained sampling points includes: S121. Determine the geometric center of the mobile robot and establish a coordinate system for the mobile robot body; S122. Mapping the outline of the mobile robot based on the mobile robot body coordinate system, and obtaining a geometric outline of the mobile robot according to the outline simulation of the mobile robot; S123, applying the postures of the mobile robot at all the sampling points to the geometric outline of the mobile robot; S124, performing sampling in two-dimensional space within the geometric outline of the mobile robot to obtain all sampling points within the geometric outline of the mobile robot; S125. Match each sampling point within the geometric outline of the mobile robot with the corresponding probability map to check whether there is an obstacle at the probability map corresponding to the sampling point; if there is an obstacle, it is considered that a collision is detected and the sampling point is removed; otherwise, there is no obstacle and the sampling point is retained; repeat step S125 to obtain all retained sampling points.

3. The method according to claim 1, wherein The step S14 of thinning the directed connected graph to obtain a sparse directed connected graph includes: S141. Determine whether a sampling point in the directed connected graph is an empty point, and obtain all non-empty points and all empty points in the directed connected graph; wherein, if there is no obstacle within a preset radius at a spatial position corresponding to a sampling point in the directed connected graph, the sampling point is an empty point; otherwise, the sampling point is a non-empty point; S142, all empty points and directed edges connected to the empty points are removed from the directed connected graph; S143 , detecting the connectivity between all non-empty points in the directed connected graph to obtain a sparse directed connected graph.

4. The method according to claim 3, wherein The step S143 of detecting the connectivity between all non-empty points in the directed connected graph to obtain a sparse directed connected graph includes: Select a non-empty point from all non-empty points, use the A* algorithm to search on the directed connected graph whether the non-empty point can reach other non-empty points, and if the non-empty point can reach another non-empty point, add a directed edge between the two non-empty points; Repeat the above steps to detect mutually reachable non-empty points from all non-empty points, and add directed edges between these detected non-empty points in the directed connected graph to obtain a sparse directed connected graph.

5. The method according to claim 1, wherein The step S2 of performing online path search based on the sparse directed connected graph to plan the optimal path of the mobile robot comprises: S21. Given a starting point and an end point, perform connectivity detection with nodes within a certain area on the sparse directed connected graph respectively; S22. If connectivity exists, adding the starting point and the end point to the sparse directed connected graph; S23. Using a preset algorithm to search from the starting point and the end point respectively, to plan an optimal path for the mobile robot.

6. A mobile robot, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of a path planning method for a mobile robot as claimed in any one of claims 1 to 5.

7. A storage medium, characterized in that: The storage medium stores a program for a path planning method for a mobile robot, and when the program for the path planning method for a mobile robot is executed by a processor, the steps of the path planning method for a mobile robot as described in any one of claims 1 to 5 are implemented.

Images