Humanoid robot swarm path planning methods, systems, devices and storage media

Abstract

The present application discloses a humanoid robot cluster path planning method, system, device and storage medium, wherein the method: obtains the current distance between any two humanoid robots on the initial path, the first distance of the previous acquisition cycle of the current distance, the first path redundancy of any one humanoid robot to the target point and the first priority of the current task, the second path redundancy of another humanoid robot to the target point and the second priority of the current task; determines whether any two humanoid robots are in the first state according to the current distance and the first distance; when any two humanoid robots are in the first state, determines a humanoid robot that needs to re-plan the path according to the first priority, the first path redundancy, the second priority and the second path redundancy and performs path planning. The present application can be widely used in the field of robotics technology.

Description

Technical Field

[0001] The present application relates to the field of robot control technology, and in particular to a humanoid robot cluster path planning method, system, device and storage medium. Background Art

[0002] With the rapid development of robotics, humanoid robots, as a highly flexible and adaptable form of robot, have shown great potential in many fields, including service industry, healthcare, education, and home assistance. In these application scenarios, humanoid robots not only need to be able to imitate human movements, but also need to have the ability of autonomous navigation and path planning to cope with complex and dynamic environments.

[0003] Path planning is one of the core technologies for autonomous navigation of humanoid robots. Effective path planning requires not only that the robot can find the optimal path in a static environment, but also that it must have the ability to adjust the path in real time in a dynamic environment. The robot needs to deal with various challenges, such as avoiding static obstacles (such as furniture, walls) and dynamic obstacles (such as pedestrians, moving objects). In addition, path planning also needs to take into account the robot's motion constraints, mission objectives, and changes in the environment.

[0004] In the related art, when improving the path planning problem of humanoid robot clusters, there are many challenges such as local congestion and deadlock of humanoid robot operation, high real-time and dynamic requirements, high computational complexity, and difficulty in multi-robot coordination. Therefore, there are still technical problems that need to be solved in the related art. Summary of the invention

[0005] The purpose of this application is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0006] To this end, one purpose of an embodiment of the present application is to provide a humanoid robot cluster path planning method, system, device and storage medium, which can improve local congestion and deadlock in robot operation and improve the smoothness of robot cluster operation.

[0007] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present application include: a humanoid robot cluster path planning method, including: obtaining the current distance between any two humanoid robots on the initial path, the first distance of the current distance in a previous acquisition cycle, the first path redundancy of any one of the two humanoid robots to the target point and the first priority of the current task, the second path redundancy of the other of the two humanoid robots to the target point and the second priority of the current task;

[0008] Determining whether any two of the humanoid robots are in a first state according to the current distance and the first distance;

[0009] When any two of the humanoid robots are in the first state, one of the humanoid robots that needs to replan its path is determined and path planning is performed according to the first priority, the first path redundancy, the second priority, and the second path redundancy.

[0010] In addition, the humanoid robot cluster path planning method according to the above embodiment of the present invention may also have the following additional technical features:

[0011] Furthermore, in the embodiment of the present application, the first state includes a state in which the two humanoid robots are approaching each other and a state in which the two humanoid robots are in conflict.

[0012] Furthermore, in an embodiment of the present application, determining whether the any two humanoid robots are in the first state based on the current distance and the first distance specifically includes: when the current distance is less than or equal to a preset threshold, and the current distance is less than the first distance, and the current distance is greater than a constant 0, determining that the any two humanoid robots are in a state where the two humanoid robots are approaching each other; when the current distance is less than or equal to a preset threshold, and the current distance is less than the first distance, and the current distance is equal to a constant 0, determining that the any two humanoid robots are in a state where the two humanoid robots are in conflict.

[0013] Further, in an embodiment of the present application, determining a humanoid robot that needs to re-plan its path and performing path planning based on the first priority, the first path redundancy, the second priority, and the second path redundancy specifically includes: determining the first planning priority of any one of the humanoid robots based on the first priority and the first path redundancy; determining the second planning priority of another humanoid robot based on the second priority and the second path redundancy; determining a humanoid robot that needs to re-plan its path and performing path planning based on the first planning priority and the second planning priority.

[0014] Further, in an embodiment of the present application, determining the first planning priority of any humanoid robot according to the first priority and the first path redundancy specifically includes: inputting the first priority and the first path redundancy into a planning priority determination equation to determine the first planning priority of any humanoid robot; wherein the planning priority determination equation is:

[0015] ;

[0016] in, is the first planning priority, Pi is the first priority, is the first path redundancy; , are two weight coefficients, + =1.

[0017] Further, in the embodiment of the present application, determining any humanoid robot that needs to replan its path and performing path planning according to the first planning priority and the second planning priority specifically includes:

[0018] A humanoid robot having a lower planning priority among the first planning priority and the second planning priority is determined as a humanoid robot that needs to replan a path, and path planning is performed.

[0019] Furthermore, in an embodiment of the present application, the first path redundancy is obtained by the following steps: obtaining the initial planned path number of the humanoid robot; when the number of planned paths is 1, taking the length of the initial planned path of the humanoid robot as the first path redundancy; when the number of planned paths is greater than 1, taking the initial planned path number as the first path redundancy.

[0020] On the other hand, the embodiment of the present application also provides a humanoid robot cluster path planning system, including:

[0021] An acquisition unit, used for acquiring a current distance between any two humanoid robots on an initial path, a first distance of the current distance in a previous acquisition cycle, a first path redundancy from any one of the two humanoid robots to a target point and a first priority of a current task, and a second path redundancy from the other of the two humanoid robots to a target point and a second priority of the current task;

[0022] a first processing unit, configured to determine whether any two of the humanoid robots are in a first state according to the current distance and the first distance;

[0023] The second processing unit is used to determine one of the humanoid robots that needs to replan its path and perform path planning based on the first priority, the first path redundancy, the second priority and the second path redundancy when any two of the humanoid robots are in the first state.

[0024] On the other hand, the present application also provides a humanoid robot cluster path planning device, comprising:

[0025] at least one processor;

[0026] at least one memory for storing at least one program;

[0027] When the at least one program is executed by the at least one processor, the at least one processor implements a humanoid robot cluster path planning method as described in any one of the invention contents.

[0028] In addition, the present application also provides a computer-readable storage medium, which stores processor-executable instructions, and the processor-executable instructions are used to execute a humanoid robot cluster path planning method as described in any of the above items when executed by the processor.

[0029] The advantages and benefits of the present application will be partially given in the following description, and partially become apparent from the following description, or be understood through the practice of the present application:

[0030] The present application can obtain the current distance between any two humanoid robots on the initial path, the first distance of the current distance in the previous acquisition cycle, the first path redundancy from any one of the humanoid robots to the target point and the first priority of the current task, the second path redundancy from another humanoid robot to the target point and the second priority of the current task; determine whether any two humanoid robots are in the first state based on the current distance and the first distance; when any two humanoid robots are in the first state, determine the humanoid robot that needs to re-plan the path and perform path planning based on the first priority, the first path redundancy, the second priority and the second path redundancy. The present application determines one of the two humanoid robots that needs to re-plan the route through the path redundancy from the robot to the target point and the priority of the robot task and performs path planning for it. The present application can improve local congestion and deadlock in robot operation and can improve the smoothness of the operation of the robot cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A schematic diagram of the steps of a humanoid robot cluster path planning method in a specific embodiment of the present invention;

[0032] Figure 2 A flowchart of a robot initial path planning module planning a path in a specific embodiment of the present invention;

[0033] Figure 3 A flowchart of a path dynamic adjustment module dynamically adjusting a path in a specific embodiment of the present invention;

[0034] Figure 4 A flowchart of a multi-robot coordination and collision avoidance module for realizing collision avoidance in a specific embodiment of the present invention;

[0035] Figure 5 A flowchart of a task allocation and scheduling module performing task allocation and scheduling in a specific embodiment of the present invention;

[0036] Figure 6 A schematic flow chart of a humanoid robot cluster path planning method in another specific embodiment of the present invention;

[0037] Figure 7 It is a structural schematic diagram of a humanoid robot cluster path planning system in a specific embodiment of the present invention;

[0038] Figure 8 It is a structural schematic diagram of a humanoid robot cluster path planning device in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0039] The embodiments of the present invention are described in detail below with reference to the accompanying drawings to illustrate the principles and processes of the humanoid robot cluster path planning method, system, device and storage medium in the embodiments of the present invention.

[0040] Reference Figure 1 This embodiment provides a humanoid robot cluster path planning method. The method may include steps S101 to S103.

[0041] S101, obtaining the current distance between any two humanoid robots on the initial path, the first distance of the current distance in the previous acquisition cycle, the first path redundancy from any humanoid robot to the target point and the first priority of the current task, the second path redundancy from another humanoid robot to the target point and the second priority of the current task.

[0042] It is understandable that the initial path can be the path calculated by a predetermined algorithm at the beginning, and as the surrounding environment changes, any two humanoid robots in the initial path will undergo dynamic path changes. The current distance can be the distance between any two robots in the same collection cycle. Each robot can be provided with a sensor, and each sensor has a number of fixed collection cycles. The time of the collection cycle allows the sensor to successfully collect data. The time difference between each two adjacent collection cycles of the same robot can be a fixed time difference. The priority and path redundancy can be obtained from the robot's processor or database.

[0043] In some feasible embodiments of the present application, the processor may first establish a wired or wireless connection with the robot. After the connection is established, the processor may obtain from the robot's processor or database the current distance between any two humanoid robots on the initial path, the first distance of the previous acquisition cycle before the current distance, the first path redundancy from any humanoid robot to the target point and the first priority of the current task, the second path redundancy from another humanoid robot to the target point and the second priority of the current task.

[0044] S102: Determine whether any two humanoid robots are in a first state according to the current distance and the first distance.

[0045] In this embodiment, after obtaining the current distance and the first distance, the processor can determine whether any two humanoid robots are in the first state. The first state can be used to indicate that any two robots are in a state of approaching each other or in a state of conflict.

[0046] S103: When any two humanoid robots are in the first state, a humanoid robot that needs to re-plan its path is determined according to the first priority, the first path redundancy, the second priority, and the second path redundancy, and path planning is performed.

[0047] In some feasible embodiments of the present application, when any two humanoid robots are in a state of approaching each other or in a conflicting motion state, the processor can determine a humanoid robot that needs to re-plan its path and perform path planning based on the first priority, the first path redundancy, the second priority, and the second path redundancy. Specifically, the path planning in this embodiment can be to avoid the robot that needs to re-plan its path so that the other humanoid robot passes first.

[0048] In summary, this embodiment can obtain the current distance between any two humanoid robots on the initial path, the first distance of the previous acquisition cycle from the current distance, the first path redundancy from any humanoid robot to the target point and the first priority of the current task, the second path redundancy from another humanoid robot to the target point and the second priority of the current task. According to the current distance and the first distance, it is determined whether any two humanoid robots are in the first state. When any two humanoid robots are in the first state, according to the first priority, the first path redundancy, the second priority and the second path redundancy, a humanoid robot that needs to re-plan the path is determined and path planning is performed. This embodiment determines one of the two humanoid robots that needs to re-plan the route through the path redundancy from the robot to the target point and the priority of the robot task and performs path planning for it. This embodiment can improve local congestion and deadlock in robot operation, and can improve the smoothness of the operation of the robot cluster.

[0049] Further, in some feasible embodiments of the present application, the first state may include a state in which the two humanoid robots are approaching each other and a state in which the two humanoid robots are in conflict.

[0050] Furthermore, the step of determining whether any two humanoid robots are in the first state according to the current distance and the first distance may specifically include step S201 or step S202.

[0051] S201. When the current distance is less than or equal to a preset threshold, and the current distance is less than the first distance, and the current distance is greater than a constant 0, it is determined that any two humanoid robots are in a state where the two humanoid robots are approaching each other.

[0052] S202: When the current distance is less than or equal to a preset threshold, and the current distance is less than the first distance, and the current distance is equal to a constant 0, it is determined that any two humanoid robots are in a conflict state.

[0053] In some feasible embodiments of the present application, when the current distance is less than or equal to the preset threshold, and the current distance is less than the first distance, and the current distance is greater than the constant 0, the processor can determine that any two humanoid robots are in a state where the two humanoid robots are approaching each other. When the current distance is less than or equal to the preset threshold, and the current distance is less than the first distance, and the current distance is equal to the constant 0, the processor can determine that any two humanoid robots are in a state where the two humanoid robots are in conflict.

[0054] Furthermore, the step of determining a humanoid robot that needs to re-plan its path and performing path planning according to the first priority, the first path redundancy, the second priority and the second path redundancy may specifically include steps S301 to S303.

[0055] S301. Determine a first planning priority of any humanoid robot according to a first priority and a first path redundancy.

[0056] S302: Determine a second planning priority of another humanoid robot according to the second priority and the second path redundancy.

[0057] S303: According to the first planning priority and the second planning priority, determine a humanoid robot that needs to replan its path and perform path planning.

[0058] In some feasible embodiments of the present application, the processor can determine the first planning priority of any one humanoid robot according to the first priority and the first path redundancy, and determine the second planning priority of another humanoid robot according to the second priority and the second path redundancy. According to the first planning priority and the second planning priority corresponding to the two humanoid robots, the processor can finally determine the humanoid robot that needs to replan the path and perform path planning.

[0059] Further, in some feasible embodiments of the present application, the step of determining the first planning priority of any humanoid robot according to the first priority and the first path redundancy may specifically include:

[0060] The first priority and the first path redundancy are input into the planning priority determination equation to determine the first planning priority of any humanoid robot. The planning priority determination equation is:

[0061] .

[0062] in, is the first planning priority, Pi is the first priority, is the first path redundancy. , are two weight coefficients, + =1.

[0063] Further, in some feasible embodiments of the present application, according to the first planning priority and the second planning priority, the step of determining any humanoid robot that needs to replan its path and performing path planning may specifically include:

[0064] A humanoid robot having a lower planning priority among the first planning priority and the second planning priority is determined as a humanoid robot that needs to replan a path, and path planning is performed.

[0065] Further, the first path redundancy is obtained through step S401 and step S402, or through step S401 and step S403.

[0066] S401, obtaining the number of initial planned paths of the humanoid robot.

[0067] S402: When the number of planned paths is 1, the length of the initial planned path of the humanoid robot is used as the first path redundancy.

[0068] S403: When the number of planned paths is greater than 1, the number of initial planned paths is used as the first path redundancy.

[0069] In some feasible embodiments of the present application, the processor may obtain the number of initial planned paths of the humanoid robot. When the number of planned paths is 1, the processor may use the length of the initial planned path of the humanoid robot as the first path redundancy. When the number of planned paths is greater than 1, the processor may use the number of initial planned paths as the first path redundancy.

[0070] The specific implementation principle of this application is described below with reference to the accompanying drawings:

[0071] Reference Figure 2-Figure 6 This embodiment can improve the local congestion and deadlock problems in the path planning of humanoid robot clusters and improve the coordination and autonomy of robots in complex environments. The method of this embodiment can be burned into each functional module through a program.

[0072] The following describes in detail the processing details of each functional module:

[0073] 1. The process executed by the robot's initial path planning module is S11 and S12.

[0074] S11. Initialize the path through the OCra* algorithm: The OCra* (Optimal Cooperative Replanning A*) algorithm is a multi-robot path planning algorithm that can generate the optimal path while considering the dynamic environment and multi-robot coordination. During initialization, each robot obtains its starting and ending positions and establishes a preliminary path from the starting point to the end point.

[0075] S12. Path generation: The OCra* algorithm is used to calculate the initial path of each robot. The path tries to avoid static obstacles and the initial positions of other robots in a global range to ensure the global optimality of the path.

[0076] 2. The process executed by the path dynamic adjustment module is S21 and S22.

[0077] S21. Real-time environmental perception: Each robot is equipped with sensors to perceive the dynamic changes of the surrounding environment in real time, including the position changes of obstacles and other robots.

[0078] S22. Path Adjustment: Dynamically adjust the robot's path based on real-time environmental information. The OCra* algorithm recalculates the path during dynamic adjustment to ensure that the robot can avoid new obstacles and adjust the path to avoid potential collisions.

[0079] 3. The processes executed by the multi-robot coordination and collision avoidance module are S31 and S32.

[0080] S31. Push and rotate algorithm application: When multiple robots conflict or approach each other on the path, the push and rotate algorithm is used for coordination. The algorithm uses push and rotate operations to allow one robot to temporarily give up the passage so that other robots can pass smoothly, thus avoiding congestion and deadlock.

[0081] S32. Conflict detection and resolution: The system continuously monitors potential conflict points on the robot path. When a conflict is detected, the push and rotate algorithm is automatically called to adjust and coordinate the path.

[0082] 4. The process of task allocation and scheduling module execution is S41 and S42

[0083] S41. Task allocation: According to the robot's position and task priority, reasonably allocate tasks to ensure that each robot has a clear task goal.

[0084] S42.Task Scheduling: During task execution, dynamically adjust task allocation and scheduling strategies to cope with environmental changes and task priority adjustments.

[0085] 5. The processes executed by the system integration and optimization module are S51 and S52.

[0086] S51. Path optimization: Combining the advantages of OCra* and push and rotate algorithms, comprehensively optimize path planning to ensure the optimality and coordination of the path.

[0087] S52. Algorithm integration: Seamlessly integrate OCra* and push and rotate algorithms into the robot control system, and achieve efficient path planning and coordination through modular design.

[0088] In other embodiments, the method of the present application is used to perform path planning for a humanoid robot cluster in a warehousing and logistics environment.

[0089] 1. Experimental Environment

[0090] In a simulated warehouse logistics environment, which contains multiple shelves, narrow aisles, and randomly placed obstacles, multiple humanoid robots are deployed in the environment to perform handling tasks.

[0091] 2. Experimental Procedure

[0092] Step a1: Initial path planning. Each robot determines the starting point and end point according to the task requirements. The OCra* algorithm is used to generate an initial path for each robot. The path tries to avoid static obstacles and the initial positions of other robots in the global scope to ensure the global optimality of the path.

[0093] Step a2: Dynamic path adjustment. During the path execution process, the robot senses the changes in the environment in real time through sensors. The path is adjusted dynamically based on the real-time environmental information. The OCra* algorithm recalculates the path during the adjustment process to ensure that the robot can avoid new obstacles and adjust the path to avoid potential collisions.

[0094] Step a3: Multi-robot coordination and collision avoidance. The system continuously monitors potential conflict points on the robot paths.

[0095] When a conflict is detected, the push and rotation algorithm is called to coordinate. Through the push and rotation operations, one robot temporarily gives up the channel so that other robots can pass smoothly, avoiding congestion and deadlock.

[0096] Step a4: Task allocation and scheduling. According to the robot's position and task priority, the tasks are reasonably allocated to ensure that each robot has a clear task goal. During the task execution process, the task allocation and scheduling strategies are dynamically adjusted to cope with environmental changes and task priority adjustments.

[0097] Step a5: System integration and optimization. Comprehensively optimize path planning, combining the advantages of OCra* and push and rotate algorithms to ensure the optimality and coordination of the path.

[0098] Seamlessly integrate OCra* and push and rotate algorithms into the robot control system, achieving efficient path planning and coordination through modular design.

[0099] Furthermore, the push and rotate algorithm may specifically include a conflict and proximity judgment algorithm and a push and rotate operation.

[0100] In the process of multi-robot path planning, conflict and proximity judgment is a key step. By using the conflict and proximity judgment algorithm to judge the position relationship of multiple robots on the path, the system can coordinate operations in a timely manner.

[0101] The specific conflict judgment algorithm is as follows:

[0102] Set up robot and Respectively in time The location is and , then the conflict judgment condition is:

[0103]

[0104] The algorithm steps include steps c1-c3.

[0105] c1. Calculate current position: time step Location and .

[0106] c2. Compare positions: Determine whether the two robots occupy the same position at the same time step, that is, whether .

[0107] c3. Mark conflict: If the conditions are met, mark it as a conflict; otherwise continue.

[0108] The proximity judgment algorithm is as follows:

[0109] Set up robot and In time The location is and , the safe distance between robots is , then the condition for proximity judgment is:

[0110]

[0111] The algorithm steps include steps d1-d3.

[0112] d1. Calculate distance: Calculate robot and At time step The Euclidean distance between .

[0113] d2. Compare distance: Compare the calculated distance with the preset safety distance Make a comparison.

[0114] d3.Mark close: if the distance is less than or equal to and If it is a positive number, mark it as close; otherwise continue.

[0115] Push and Rotate Operations: After determining conflicts or proximity situations, the Push and Rotate algorithm resolves these problems through Push and Rotate operations.

[0116] Push operation: Push operation aims to temporarily move one of the robots to a nearby free position so that other robots can pass through. Specifically, select the push object: Usually select the robot with less impact to push, and the criteria may include the path redundancy of the robot to the target point or the priority of the current task. Find free position: Search for free positions around the robot and make sure that this position does not conflict with the paths of other robots.

[0117] Execute Push: Move the selected robots to The robot moves to the next position, records the action and notifies other robots to update their path planning.

[0118] Furthermore, push object selection formula

[0119] First define the push object scoring function , this function is for each robot A comprehensive score is calculated, and robots with lower scores are more suitable for being pushed.

[0120]

[0121] in:

[0122] It's a robot The first planning priority (the lower the easier to push).

[0123] It's a robot The path redundancy to the target point can be defined as the robot The number of alternative paths to choose from, or the length of the shortest path from the starting point to the destination.

[0124] It's a robot The higher the value, the higher the priority of the task. If the priority is lower, the robot is more likely to be pushed.

[0125] and is a weight parameter used to adjust the influence of path redundancy and task priority in the comprehensive score. You can adjust these weights according to the specific situation to balance different factors.

[0126] Path redundancy It can be calculated as follows:

[0127] If the robot has an alternate path: define = ,in is the number of valid paths that robot iii can choose.

[0128] If the robot does not have an alternative path: the length of the main path can be used as redundancy, e.g. = ,in is the length of the main path.

[0129] Task Priority

[0130] Task Priority Usually defined based on the urgency of the task, its impact on the system as a whole, or the time it takes to complete the task. For all robots, calculate ,choose The smallest robot is used as the push object.

[0131] Specifically, if there are any two robots with weights =0.7, =0.3, and the path redundancy of robot 1 =5, task priority =2Robot 2's path redundancy =2, task priority =5. Then their scores are:

[0132]

[0133] .

[0134] In addition, refer to Figure 7 ,and Figure 1 Corresponding to the method, a humanoid robot cluster path planning system is also provided in an embodiment of the present application. The system may include: an acquisition unit 1001, a first processing unit 1002 and a second processing unit 1003. The acquisition unit 1001 can be used to obtain the current distance between any two humanoid robots on the initial path, the first distance of the current distance in the previous acquisition cycle, the first path redundancy from any humanoid robot to the target point and the first priority of the current task, the second path redundancy from another humanoid robot to the target point and the second priority of the current task. The first processing unit 1002 can be used to determine whether any two humanoid robots are in the first state based on the current distance and the first distance. The second processing unit 1003 can be used to determine a humanoid robot that needs to re-plan the path and perform path planning based on the first priority, the first path redundancy, the second priority and the second path redundancy when any two humanoid robots are in the first state.

[0135] It should be noted that the acquisition unit can be any integrated circuit unit or microprocessor unit obtained by integrating a chip having a processing function and its peripheral circuits through existing integration technology. The first processing unit and the second processing unit can also be any integrated circuit module or microprocessor module obtained by integrating a chip having a processing function and its peripheral circuits through existing integration technology. The first processing unit and the second processing unit can also include one or more memories. One or more memories can be used to store the specific algorithm used for route planning in this application.

[0136] The acquisition unit 1001 can transmit the acquired data to the processor through a wired or wireless connection with the processor of the first processing unit 1002. The processor of the first processing unit 1002 can process the data through an internal chip and finally obtain the search result. The specific device connection method and device setting of the acquisition unit 1001 and the first processing unit 1002 and the first processing unit 1002 and the second processing unit 1003 are not limited.

[0137] It should be noted that the contents of the above-mentioned humanoid robot cluster path planning method embodiment are all applicable to this humanoid robot cluster path planning system embodiment. The functions specifically implemented by this humanoid robot cluster path planning system embodiment are the same as those of the above-mentioned humanoid robot cluster path planning method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned humanoid robot cluster path planning method embodiment.

[0138] and Figure 1 Corresponding to the method, the embodiment of the present application also provides a humanoid robot cluster path planning device, the specific structure of which can be referred to Figure 8 ,include:

[0139] at least one processor 1011;

[0140] At least one memory 1012, used to store at least one program;

[0141] When the at least one program is executed by the at least one processor, the at least one processor implements the humanoid robot cluster path planning method.

[0142] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0143] and Figure 1 Corresponding to the method, an embodiment of the present application further provides a computer-readable storage medium, which stores processor-executable instructions, and the processor-executable instructions are used to execute the humanoid robot cluster path planning method when executed by the processor.

[0144] The contents of the above-mentioned humanoid robot cluster path planning method embodiment are all applicable to the present storage medium embodiment. The functions specifically implemented by the present storage medium embodiment are the same as those of the above-mentioned humanoid robot cluster path planning method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned humanoid robot cluster path planning method embodiment.

[0145] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the application is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are expected, wherein the order of various operations is changed and the sub-operation described as a part of a larger operation is performed independently.

[0146] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present application. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional techniques of the engineer. Therefore, those skilled in the art can implement the present application set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the attached claims and their equivalents.

[0147] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several programs to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0148] The logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable programs for implementing logical functions, and may be embodied in any computer-readable medium for use by a program execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch and execute a program from a program execution system, device or apparatus), or in conjunction with such program execution system, device or apparatus. For purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by a program execution system, device or apparatus, or in conjunction with such program execution system, device or apparatus.

[0149] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0150] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable program execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0151] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0152] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0153] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the described embodiments. Technical personnel familiar with the field may make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A humanoid robot cluster path planning method, characterized in that: The following steps are involved: Obtaining the current distance between any two humanoid robots on the initial path, the first distance of the current distance in a previous acquisition cycle, the first path redundancy of any one of the two humanoid robots to the target point and the first priority of the current task, and the second path redundancy of the other of the two humanoid robots to the target point and the second priority of the current task; Determining whether any two of the humanoid robots are in a first state according to the current distance and the first distance; When any two of the humanoid robots are in the first state, one of the humanoid robots that needs to replan its path is determined and path planning is performed according to the first priority, the first path redundancy, the second priority and the second path redundancy; wherein the first state includes a state where the two humanoid robots are approaching each other and a state where the two humanoid robots are in conflict; the first path redundancy is obtained by the following steps: Obtain the number of initial planned paths of the humanoid robot; When the number of planned paths is 1, the length of the initial planned path of the humanoid robot is used as the first path redundancy; When the number of planned paths is greater than 1, the number of initial planned paths is used as the first path redundancy.

2. According to claim 1, a humanoid robot cluster path planning method is characterized in that: The determining, according to the current distance and the first distance, whether the two humanoid robots are in the first state specifically includes: When the current distance is less than or equal to a preset threshold, and the current distance is less than the first distance, and the current distance is greater than a constant 0, it is determined that the two humanoid robots are in a state where the two humanoid robots are approaching each other; When the current distance is less than or equal to a preset threshold, and the current distance is less than the first distance, and the current distance is equal to a constant 0, it is determined that the arbitrary two humanoid robots are in a conflict state.

3. The humanoid robot cluster path planning method according to claim 1, characterized in that: The determining a humanoid robot that needs to re-plan a path and performing path planning according to the first priority, the first path redundancy, the second priority, and the second path redundancy specifically includes: Determining a first planning priority of any one of the humanoid robots according to the first priority and the first path redundancy; Determining a second planning priority of another of the humanoid robots according to the second priority and the second path redundancy; According to the first planning priority and the second planning priority, a humanoid robot that needs to replan its path is determined and path planning is performed.

4. A humanoid robot cluster path planning method according to claim 3, characterized in that: The determining the first planning priority of any humanoid robot according to the first priority and the first path redundancy specifically includes: The first priority and the first path redundancy are input into a planning priority determination equation to determine the first planning priority of any humanoid robot; wherein the planning priority determination equation is: ； in, The first planning priority, P i As the first priority, is the first path redundancy; , are two weight coefficients, + =1.

5. The humanoid robot cluster path planning method according to claim 3, characterized in that: The determining, according to the first planning priority and the second planning priority, any humanoid robot that needs to replan its path and performing path planning specifically includes: A humanoid robot having a lower planning priority among the first planning priority and the second planning priority is determined as a humanoid robot that needs to replan a path, and path planning is performed.

6. A humanoid robot cluster path planning system, characterized in that: include: An acquisition unit, used for acquiring a current distance between any two humanoid robots on an initial path, a first distance of the current distance in a previous acquisition cycle, a first path redundancy from any one of the two humanoid robots to a target point and a first priority of a current task, and a second path redundancy from the other of the two humanoid robots to a target point and a second priority of the current task; a first processing unit, configured to determine whether any two of the humanoid robots are in a first state according to the current distance and the first distance; a second processing unit, configured to determine a humanoid robot that needs to replan its path and perform path planning according to the first priority, the first path redundancy, the second priority, and the second path redundancy when any two of the humanoid robots are in the first state; wherein the first state includes a state in which the two humanoid robots are approaching each other and a state in which the two humanoid robots are in conflict; The first path redundancy is obtained by the following steps: obtaining the initial planned path number of the humanoid robot; when the planned path number is 1, taking the length of the initial planned path of the humanoid robot as the first path redundancy; when the planned path number is greater than 1, taking the initial planned path number as the first path redundancy.

7. A humanoid robot cluster path planning device, characterized in that include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the humanoid robot cluster path planning method as described in any one of claims 1-5.

8. A computer-readable storage medium storing instructions executable by a processor, characterized in that: The processor-executable instructions are used to execute a humanoid robot cluster path planning method as described in any one of claims 1-5 when executed by the processor.

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