Method and apparatus for path planning of a robot

Abstract

The application discloses a path planning method and device of a robot, the path planning method comprises the following steps: performing global path planning based on a path map in a moving range of the robot to obtain a global path of the robot from a current position to a target position; performing local path planning based on the global path and the current position of the robot to obtain a local path; recording information of the local path in the path map; and performing global path planning based on the path map after the information is recorded to update the global path. The application can solve the problem that the matching degree of the global planning path and the local planning path is too small in the prior art.

Description

Technical Field

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

[0002] Currently, planning algorithms for mobile robots primarily involve global planning and local planning. Global planning generates the robot's global path, while local planning generates local trajectories, enabling specific robot motion, including tracking and obstacle avoidance. However, global planning and local planning are typically independent, leading to a poor match between the global and local planned paths, causing the robot to sway. Summary of the Invention

[0003] The present application provides a robot path planning method and device to solve the problem in the prior art that the global planned path and the local planned path have a low matching degree.

[0004] In order to solve the above technical problems, the present application proposes a robot path planning method, including: performing global path planning based on a path map within the robot's moving range to obtain the global path of the robot from the current position to the target position; performing local path planning based on the global path and the current position of the robot to obtain the local path; recording the information of the local path in the path map; performing global path planning based on the path map after recording the information to update the global path.

[0005] In one embodiment, performing global path planning includes: performing global path planning in a first cycle;

[0006] Performing local path planning includes: performing local path planning in a second cycle.

[0007] In one embodiment, the first period is greater than or equal to the second period.

[0008] In one embodiment, the path map is a grid map, and global path planning is performed based on the path map after recording information, including: setting the grid cost value of the grid corresponding to the local path on the grid map to a first preset value, and the grid cost value of the plannable grid on the grid map to a second preset value; the first preset value is less than the second preset value; and taking the path with the smallest sum of the grid cost values ​​on the grid map as the global path.

[0009] In one embodiment, the generation method further includes: performing global path planning based on the path map after recording the information, and further includes: setting the grid cost value of the grid corresponding to the updated global path to a second preset value.

[0010] In one embodiment, local path planning is performed based on the global path and the current position of the robot, including: performing local path planning based on the global path and environmental perception information of the robot at the current position.

[0011] In one embodiment, the environmental perception information of the robot at the current location includes environmental obstacles of the robot at the current location.

[0012] In one embodiment, the path map is a topological map; performing global path planning based on the path map after recording information includes:

[0013] Search for the topological point closest to the local path in the topological map;

[0014] Taking the topology closest to the local path as the starting point, re-perform global planning to update the global path of the robot from the starting point to the target position.

[0015] In order to solve the above technical problems, the present application proposes a path planning system, which includes a global planning module and a local planning module; the global planning module is used to perform global planning based on the path map within the robot's moving range to obtain the global path of the robot from the current position to the target position; the local planning module is used to perform local planning based on the global path and the current position of the robot to obtain the local path; the information of the local path is recorded in the path map; the global planning module is further used to perform global path planning based on the path map after recording the information to update the global path.

[0016] In order to solve the above technical problems, the present application also proposes an electronic device, which includes a processor and a memory, the memory is used to store computer programs, and the processor is used to execute the computer programs to implement the above method.

[0017] In order to solve the above technical problems, the present application also proposes a computer program product, which includes computer program instructions, and the computer program instructions enable a computer to implement the above method.

[0018] In order to solve the above technical problems, the present application also proposes a computer-readable storage medium, which stores a program and / or instructions, and the program and / or instructions are used to be executed to implement the above method.

[0019] After planning a local path based on the global path and the current position of the robot, this application can record the information of the local path on the path map, and perform global path planning based on the path map after recording the information to update the global path. In this way, the local planning results are introduced into the global planning algorithm as an important reference indicator for global path planning, thereby achieving almost consistency in the geometric shapes of the global path and the local trajectory, and solving problems such as robot swaying motion and even falling into local dead zones caused by separation and inconsistency between the global path and the local trajectory. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0021] Figure 1 This is a flow chart of an embodiment of a path planning method for a robot of the present application;

[0022] Figure 2 This is a schematic diagram of the global path replacement update;

[0023] Figure 3 It is a schematic diagram of global path replanning in the path planning method of the robot of the present application;

[0024] Figure 4 This is a structural diagram of an embodiment of an electronic device of the present application;

[0025] Figure 5 It is a structural diagram of an embodiment of the computer program product of the present application;

[0026] Figure 6 It is a structural diagram of an embodiment of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0027] Below with reference to the accompanying drawings in the embodiment of the present application, the technical solutions in the embodiment of the present application are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. In addition, all examples described herein are mainly and clearly intended to be used for teaching purposes, to assist readers in understanding the principles of the present application and the concepts provided by the inventors, so as to deepen the field, and all examples should not be interpreted as being limited to the examples and conditions of such specific elaborations. That is, based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work premise belong to the scope of protection of this application. In addition, unless otherwise specified (for example, "or in addition" or "or in an alternative"), the term "or" as used herein refers to non-exclusive "or" (that is, "and / or"). Moreover, the various embodiments described herein are not necessarily mutually exclusive, because some embodiments can be combined with one or more other embodiments to form new embodiments.

[0028] This application is used to perform path planning for a robot, such as planning the cleaning path of a cleaning robot, or planning the moving path of an air purification robot.

[0029] See also Figure 1 , Figure 1 This is a flow chart of an embodiment of the robot path planning method of the present application. The robot path planning method of this embodiment includes the following steps. It should be noted that the following step numbers are used only to simplify the description and are not intended to limit the order in which the steps are executed. The steps of this embodiment can be executed in any order without violating the technical principles of this application.

[0030] S11: Perform global path planning based on the path map within the robot's moving range to obtain the global path of the robot from the current position to the target position.

[0031] Global path planning can be performed based on the path map within the robot's moving range to obtain the global path from the robot's current position to the target position, so that the robot can move forward to the target position according to the global path.

[0032] The movement range may be user-defined or pre-set. When no user instruction to limit the movement range is received, the corresponding area of ​​the robot's path map serves as the robot's movement range. Upon receiving a user instruction to limit the movement range, a path map within the restricted movement range may be extracted from the robot's path map. This allows global path planning to be performed in step S11 based on the path map within the robot's movement range.

[0033] Optionally, global path planning may be performed using Dijkstra algorithm, A* algorithm, D* algorithm, LPA* algorithm, or D*lite algorithm.

[0034] Optionally, before performing global path planning, the target position may be determined first, so as to plan a global path for the robot from the current position to the target position.

[0035] The target location can be the endpoint location contained in an operation instruction, such as the endpoint location in a driving instruction received by an unmanned vehicle. For example, if an air purification robot senses that the concentration of air pollutants in a certain area has reached a preset threshold, it will generate an air purification instruction. The endpoint location contained in the air purification instruction is the location where the concentration of air pollutants has reached the preset threshold. In other application scenarios, the target location can also be a target location pre-set within the robot, such as the cleaning robot's daily stopping point.

[0036] In addition, global path planning is to plan a path for the robot in a known environment. The accuracy of path planning depends on the accuracy of environment acquisition. Therefore, more accurate environmental information can be obtained before global path planning so that the planned global path is more accurate. This can avoid the situation where the actual generated local path is significantly different from the global path due to inaccurate global path.

[0037] S12: Based on the global path and the current position of the robot, local path planning is performed to obtain the local path.

[0038] After obtaining the global path of the robot from its current position to its target position based on the above steps, local path planning can be performed based on the global path and the current position of the robot to obtain the local path.

[0039] Alternatively, the robot can be controlled to sense the environment around its current location, obtain information about the robot's surrounding environment, and then perform local path planning based on the global path and the robot's environmental perception information at its current location to obtain a local path. This can enable the robot to have good obstacle avoidance capabilities. Alternatively, the robot can be controlled to sense the surrounding environment through its own sensors (such as radar sensors or ultrasonic sensors, etc.).

[0040] The robot's environmental perception information at its current location may include environmental obstacles at the robot's current location. Thus, based on the robot's environmental perception information at its current location, it is determined whether there are obstacles on the global path. If so, local path planning can be performed to avoid the obstacles. If no obstacles are present, the robot can be controlled to travel directly along the global path. In other alternative embodiments, after obtaining the global path, local path planning can be performed based on the robot's environmental perception information at its current location and the global path to determine the optimal local path, regardless of whether there are obstacles on the global path.

[0041] Optionally, when planning a local path, a target point of the local path can be selected on the global path. For example, the last point on the global path that is located in the robot's perception area (or local map) along the direction from the current position to the target position can be used as the target point of the local path; then, based on the robot's environmental perception information at the current position, a local path from the current position to the target point of the local path is locally planned.

[0042] The specific algorithm of the local path planning is not limited, and may be, for example, the DWA algorithm, the VFH* algorithm, the Dijkstra algorithm, the A* algorithm, or the D* algorithm.

[0043] S13: Record the information of the local route in the route map.

[0044] After local path planning is performed based on the global path and the current position of the robot to obtain the local path, the information of the local path can be recorded on the path map so that the global path planning can be re-performed based on the path map after recording the information. In this way, the local planning results are introduced into the global planning algorithm as an important reference indicator for global path planning, achieving almost consistency in the geometric shapes of the global path and the local trajectory, and solving problems such as robot swaying motion or even falling into local dead zones caused by separation and inconsistency between the global path and the local trajectory.

[0045] In one feasible method, the path map can be a grid map, and the cost of the grid point where the local path is located on the path map can be changed so that the cost of the grid point where the local path is located on the path map is different from the cost of other passable grid points. In this way, the information of the local path is recorded on the path map.

[0046] In another implementation, the path map may be a topological map, and multiple points corresponding to the local paths in the topological map may be connected to obtain local path lines, thereby recording the information of the local paths on the path map.

[0047] S14: Perform global path planning based on the path map after recording the information to update the global path.

[0048] After recording the information of the local path on the path map, the global path planning can be re-performed based on the path map after recording the information to update the global path. In this way, the local planning results are introduced into the global planning algorithm as an important reference indicator for global path planning, achieving almost consistency in the geometric shapes of the global path and the local trajectory, and solving problems such as robot swaying motion or even falling into local dead zones caused by separation and inconsistency between the global path and the local trajectory.

[0049] Optionally, the "global path planning based on the path map after recording the information" of the present application means: based on the path map after recording the local path information, reusing the global path algorithm to perform global path planning to recalculate the updated global path. That is, different from "directly replacing the path of the corresponding area in the global path with the local path", step S14 of the present application is directly based on the local path and uses the global path planning algorithm to recalculate the updated global path. In this way, even if the target point of the local path is not on the global path, or if Figure 2 The local path shown is very different from the global path. This application can also re-plan a more reasonable global path based on the local path (such as Figure 3 The replanned global path shown in the figure can avoid the problem of "directly replacing the path of the corresponding area in the global path with the local path" in the solution of making the local path, which is increasingly different from the global path, closer to the global path. Figure 2 The path that the robot actually walks is too long and the robot has problems with swaying motion.

[0050] Optionally, step S12 may be performed in the second cycle, and step S14 may be performed in the first cycle.

[0051] Among them, the first cycle can be greater than the second cycle, that is, step S14 can be executed once after executing step S12 several times, for example, the first cycle is 1s (that is, the operating frequency of global path planning is 1Hz) or 10s (that is, the operating frequency of global path planning is 0.1Hz), and the second cycle is 0.1s (that is, the operating frequency of local path planning is 10Hz) or 0.05s (that is, the operating frequency of local path planning is 20Hz). Exemplarily, the first cycle is 1s and the second cycle is 0.1s, that is, after executing step S11, after each 10 executions of local path planning, the previous local path planning information (for example, the previous 10 local path planning information) is recorded on the path map, and then global path planning is performed based on the path map after recording the local path information until the robot reaches the target position. Among them, after each execution of local path planning, the robot can be controlled to travel according to the current local path planning result.

[0052] In other embodiments, the first cycle may also be equal to the second cycle. That is, after determining the local path based on step S12, the local path information is directly recorded in the path map; then, global path planning is performed based on the path map after recording the information; after the robot travels to the target point of the local path according to the local path, local path planning can be performed based on the updated global path planning and the robot's current position to obtain the current local path, and then the global path can be updated based on the current local path, and the execution is repeated until the robot reaches the target position. That is, after executing step S11, the present application can iteratively execute the steps of "performing local path planning based on the global path and the robot's current position to obtain the local path", "recording the local path information in the path map", "performing global path planning based on the path map after recording the information to update the global path", and "controlling the robot to move according to the local path" until the robot reaches the target position.

[0053] In one implementation, the path map is a grid map, and global path planning can be performed based on the grid map after recording local path information. Specifically, during global path planning in steps S11 and S14, the grid cost values ​​can be calculated using the grid map, and the path with the minimum sum of the grid cost values ​​on the grid map is determined as the global path. The grid cost value of a plannable grid (i.e., a traversable grid) on the grid map is a second preset value. Furthermore, the second preset value for each plannable grid can be the larger of a fixed value and an obstacle distance cost for each plannable grid, where the obstacle distance cost is the cost of the distance between the grid and the obstacle. Thus, the obstacle distance cost is positively correlated with the distance between the grid and the obstacle, i.e., the closer the grid is to the obstacle, the greater the obstacle distance cost. Thus, the obstacle distance cost can be used to plan a shorter path that avoids collisions with obstacles. The fixed value can also be set based on actual conditions and is not limited here. For example, it can be 1 or 10. In other alternative embodiments, the second preset value can be equal to the fixed value.

[0054] Furthermore, if Figure 3As shown, in step S14, the grid cost value of the grid corresponding to the local path obtained in step S12 on the grid map can be set to a first preset value, wherein the first preset value is less than the second preset value. This can reduce the sum of the grid cost values ​​of the path containing the grid points on the local path, thereby making the replanned global path closer to the grid points containing the local path, so as to achieve a high degree of consistency between the global path and the local trajectory. Moreover, since the local trajectory of the robot reflects the robot's subsequent motion state, this prevents the robot from experiencing a significant change in motion state when tracking or avoiding obstacles, making the robot's motion smoother and more stable, and solving the problem of the robot's swaying and repetitive motion caused by a large deviation between the global path and the local trajectory. When the robot globally plans and generates a path, the local trajectory of the robot at the previous moment is taken into account. The first preset value can be set according to actual conditions and is not limited here. For example, it can be 0 or -2. In addition, the grid cost value (i.e., the third-generation value) of the grid where the obstacle is located on the grid map will be greater than the second preset value to prevent the global path from including the grid where the obstacle is located, thereby achieving the purpose of obstacle avoidance for the robot. For example, the grid cost value (i.e., the third-generation value) of the grid where the obstacle is located on the grid map can be 100 or 200. In addition, the grid cost value of the grid where the unknown area is located on the grid map can be different from the second preset value, the first preset value, and the third-generation value, for example, it can be -1. In this way, the unknown area is distinguished from the rest of the area by a grid cost value that is different from the first preset value, the second preset value, and the third-generation value, thereby avoiding planning a path that includes the unknown area.

[0055] Optionally, after the global path is generated based on step S14, the grid cost value of the grid corresponding to the updated global path may be set to a second preset value.

[0056] In another possible implementation, path planning can be performed based on a topological map. Specifically, in step S14, the topological point closest to the local path obtained in step S12 can be searched in the topological map; global planning is then re-performed starting from the topological point closest to the local path, i.e., path search is performed starting from the topological point closest to the local path to update the global path of the robot from the starting point to the target location.

[0057] For example, you can use the topological point closest to the local path as the starting point and perform a path search using the A* or D* algorithm to obtain a shortest traversable path, thus obtaining the global path. Alternatively, after obtaining the shortest traversable path, you can use the path formed by connecting the shortest traversable path with the local path as the global path.

[0058] Furthermore, in step S14, global path planning can be performed based on the path map (e.g., the grid map or topological map described above), the environmental perception information obtained in step S12, and the local path. Specifically, the path map can be updated based on the environmental perception information obtained in step S12, for example, information such as the obstacle position on the path map can be updated based on the obstacle information perceived in the environmental perception information; and in step S13, the information of the local path is recorded on the path map; thus, in step S14, global path planning is performed based on the path map after recording the information, that is, global path planning can be performed based on the environmental perception information obtained in step S12 and the local path. In this way, the global map can be updated using the environmental perception information obtained during the local path planning, thereby ensuring the accuracy and real-time performance of the global map and improving the efficiency of subsequent global path planning.

[0059] In this embodiment, after planning a local path based on the global path and the current position of the robot, the global path planning can be re-performed based on the local path to update the global path. In this way, the local planning results are introduced into the global planning algorithm as an important reference indicator for global path planning, thereby achieving almost uniform geometric shapes between the global path and the local trajectory, and solving problems such as the robot's swaying motion or even falling into a local dead zone caused by separation and inconsistency between the global path and the local trajectory.

[0060] The present application also provides a path planning system for the above-mentioned robot path planning method, wherein the path planning system includes a global planning module and a local planning module.

[0061] The global planning module is used to perform global planning based on the path map within the robot's moving range to obtain the robot's global path from the current position to the target position;

[0062] The local planning module is used to perform local planning based on the global path and the current position of the robot to obtain the local path; the local path information is recorded in the path map;

[0063] The global planning module is further used to perform global path planning based on the path map after recording the information to update the global path.

[0064] The global planning module and the local planning module are connected in communication and cooperate in iterative execution: based on the global path and the current position of the robot, local path planning is performed to obtain the local path; the information of the local path is recorded in the path map; the global path planning is performed based on the path map after recording the information to update the global path; the robot is controlled to move according to the local path; until the robot reaches the target position.

[0065] The global planning module is used to perform global path planning in the first cycle; the local planning module is used to perform local path planning in the second cycle.

[0066] The first period is greater than or equal to the second period.

[0067] The path map is a grid map; the global planning module is used to set the grid cost value of the grid corresponding to the local path on the grid map to a first preset value, and the grid cost value of the plannable grid on the grid map to a second preset value; the first preset value is less than the second preset value; and the path with the smallest sum of the grid cost values ​​on the grid map is used as the global path.

[0068] The global planning module is further configured to set the grid cost value of the grid corresponding to the updated global path to a second preset value.

[0069] Among them, the local planning module is used to perform local path planning based on the global path and the environmental perception information of the robot at its current position.

[0070] The environmental perception information of the robot at the current position includes environmental obstacles at the current position of the robot.

[0071] Among them, the path map is a topological map; the global planning module is used to search for the topological point closest to the local path in the topological map; using the topological point closest to the local path as the starting point, global planning is re-performed to update the global path of the robot from the starting point to the target position.

[0072] The hardware equipment for implementing the above robot path planning method can be found in Figure 4 , Figure 4 2 is a schematic diagram of the structure of an embodiment of the electronic device 200 of the present application. The electronic device 200 of the present application includes a processor 21, which is configured to execute a computer program to implement the method of any of the above embodiments of the present application and any non-conflicting combination thereof.

[0073] The processor 21 may also be referred to as a CPU (Central Processing Unit). The processor 21 may be an integrated circuit chip having signal processing capabilities. The processor 21 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The general-purpose processor may be a microprocessor, or the processor 21 may be any conventional processor.

[0074] The electronic device 200 may further include a memory 22 for storing computer programs required for the processor 21 to run.

[0075] See also Figure 5 , Figure 5 Schematic diagram of the structure of the computer program product in the embodiment of the present application. The computer program product 300 of the embodiment of the present application includes computer program instructions, which, when executed, implement the method provided by any embodiment of the above-mentioned method of the present application and any non-conflicting combination. Among them, the computer program instructions can form a program file and be stored in the above-mentioned computer program product 300 in the form of a software product, so that a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) executes all or part of the steps of the various embodiments of the present application. The aforementioned computer program product 300 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a computer, server, mobile phone, tablet and other devices.

[0076] Furthermore, if Figure 6 As shown, the present application also provides a computer-readable storage medium 400, and the computer-readable storage medium 400 of the embodiment of the present application stores a program and / or instruction 410, which is used to be executed to implement the method provided by any embodiment of the above-mentioned method of the present application and any non-conflicting combination. Among them, the program and / or instruction 410 can be formed into a program file and stored in the above-mentioned storage medium 400 in the form of a software product, so that a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) executes all or part of the steps of the various embodiments of the present application. The aforementioned storage medium 400 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a computer, server, mobile phone, tablet and other devices.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as 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 an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0078] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0079] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0080] The above is only an implementation method of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of this application, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A robot path planning method, characterized in that: The path planning method comprises: Performing global path planning based on a path map within the robot's moving range to obtain a global path from the robot's current position to a target position; Performing local path planning based on the global path and the current position of the robot to obtain a local path; Recording the information of the local route in the route map; Re-planning the global path based on the path map after recording the information of the local path to recalculate an updated global path; The performing global path planning comprises: performing the global path planning in a first cycle; The performing local path planning includes: performing the local path planning in a second cycle.

2. The path planning method according to claim 1, characterized in that: The first period is greater than or equal to the second period.

3. The path planning method according to claim 1, wherein: The path map is a grid map; and the global path planning based on the path map after recording the information includes: Setting the grid cost value of the grid corresponding to the local path on the grid map to a first preset value, and the grid cost value of the plannable grid on the grid map to a second preset value; the first preset value is smaller than the second preset value; The path with the smallest sum of grid cost values ​​on the grid map is used as the global path.

4. The path planning method according to claim 3, characterized in that: The performing of global path planning based on the path map after recording the information further includes: setting the grid cost value of the grid corresponding to the updated global path to the second preset value.

5. The path planning method according to claim 1, wherein: The performing of local path planning based on the global path and the current position of the robot includes: Local path planning is performed based on the global path and environmental perception information of the robot at its current position.

6. The path planning method according to claim 5, characterized in that: The environmental perception information of the robot at the current position includes environmental obstacles of the robot at the current position.

7. The path planning method according to claim 1, characterized in that: The path map is a topological map; performing global path planning based on the path map after recording information includes: Search for the topological point closest to the local path in the topological map; Taking the topology closest to the local path as the starting point, global planning is re-performed to update the global path of the robot from the starting point to the target position.

8. A path planning system, characterized in that: The path planning system includes: The global planning module is used to perform global planning based on the path map within the robot's movement range to obtain the robot's global path from the current position to the target position; A local planning module is used to perform local planning based on the global path and the current position of the robot to obtain a local path; and record information of the local path in the path map; The global planning module is further configured to re-plan the global path based on the path map after recording the information of the local path, so as to recalculate an updated global path; The global planning module is used to perform the global path planning in a first cycle; The local planning module is used to perform the local path planning in a second cycle.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The computer program product comprises computer program instructions, and the computer program instructions enable a computer to implement the method according to any one of claims 1 to 7.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program and / or instructions, and the program and / or instructions are configured to be executed to implement the method according to any one of claims 1 to 7.

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