Wheel-legged robot path planning method, medium and product

Abstract

The invention discloses a wheel-legged robot path planning method, a medium and a product, and relates to the field of robot control, and the method comprises the steps: obtaining environment point cloud information, an initial point and a target point of a wheel-legged robot; filtering the environmental point cloud information, and constructing a global grid map based on the filtered environmental point cloud information; performing path planning on the wheel-legged robot based on the global grid map, the initial point and the target point by adopting an A * algorithm and a cost function to obtain a wheel-legged robot path; the cost function is used for calculating the cost of nodes expanded by the A * algorithm, the cost function is a leg type motion cost function, a wheel type motion cost function or a wheel-leg composite motion cost function, the rationality and smoothness of path generation can be guaranteed, and the difficulty of rear-end path tracking control is reduced.

Description

Technical Field

[0001] The present invention relates to the field of robot control, and particularly to a path planning method, medium and product for a wheel-legged robot. Background Art

[0002] The wheel-legged robot has multi-modal motion capabilities, can achieve navigation in complex environments, meet the needs of diversified tasks, and as an important part of realizing autonomous capabilities, robot path planning has broad development space. The motion, exploration and planning of the robot in the 3D space terrain are extremely challenging: Firstly, for the robot itself, the robot needs to protect itself from collisions, damages or excessive stresses, and the path planning algorithm needs to consider the size, shape and dynamic characteristics of the wheel-legged robot to ensure that the robot can move safely; Secondly, for the complex environment, the robot needs to avoid collisions with obstacles, walls or other objects in the environment and calculate the best path to pass through them; Finally, for the selection of the motion mode of the wheel-legged robot, the wheel-legged robot has wheeled, legged and wheel-leg composite motion modes, and a suitable motion mode should be selected during path tracking control.

[0003] The path planning algorithm needs to generate an appropriate path according to the task requirements, so there is an urgent need for a technology to achieve reasonable path planning. Summary of the Invention

[0004] The purpose of the present invention is to provide a path planning method, medium and product for a wheel-legged robot, which can ensure the rationality and smoothness of path generation and reduce the difficulty of subsequent path tracking control.

[0005] To achieve the above purpose, the present invention provides the following solutions.

[0006] A path planning method for a wheel-legged robot includes the following steps.

[0007] Obtain the environmental point cloud information, initial point and target point of the wheel-legged robot.

[0008] Filter the environmental point cloud information and construct a global grid map based on the filtered environmental point cloud information.

[0009] Use the A* algorithm and cost function to perform path planning for the wheel-legged robot based on the global grid map, initial point and target point to obtain the path of the wheel-legged robot; the cost function is used to calculate the cost of the nodes expanded by the A* algorithm.

[0010] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements the above-mentioned path planning method for a wheel-legged robot.

[0011] A computer program product includes a computer program which, when executed by a processor, implements the above-mentioned path planning method for a wheel-legged robot.

[0012] The present invention discloses a path planning method, medium and product for a wheel-legged robot. First, a global grid map is constructed; the A* algorithm is used to expand nodes starting from the initial point, and then a cost function is used to calculate the cost of each node expanded by the A* algorithm. Nodes are selected with the aim of minimizing the cost to obtain the path of the wheel-legged robot. The present invention can ensure the rationality and smoothness of path generation and reduce the difficulty of subsequent path tracking control. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0014] Figure 1 It is a schematic flowchart of the path planning method for the wheel-legged robot provided by the present invention.

[0015] Figure 2 It is a schematic diagram of the wheel-legged robot provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0017] The object of the present invention is to provide a path planning method, medium and product for a wheel-legged robot, aiming to ensure the rationality and smoothness of path generation and reduce the difficulty of subsequent path tracking control.

[0018] To make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0019] The present invention uses lidar to scan the environment to obtain environmental point cloud information; according to the passability of the wheel-legged robot, the passability of the environmental point cloud information is filtered, and a global grid map is generated based on the filtered environmental point cloud information; a target point is set, and a cost function is defined for the motion efficiency and safety of the wheel-legged robot; the downward projection A* algorithm is used to generate an optimal global path; the global path is optimized considering the path time factor and path smoothness. The present invention can handle terrains such as steps and slopes in an indoor complex environment, and by selecting a suitable terrain, the generation of the best path of the wheel-legged robot in the indoor complex environment is realized.

[0020] Embodiment 1: As Figure 1 shown, a path planning method for a wheel-legged robot in this embodiment includes the following steps.

[0021] Step 101: Obtain the environmental point cloud information, initial point, and target point of the wheel-legged robot.

[0022] Use lidar to obtain the environmental point cloud information, initial point, and target point of the wheel-legged robot.

[0023] Step 102: Filter the environmental point cloud information and construct a global grid map based on the filtered environmental point cloud information.

[0024] Step 103: Use the A* algorithm and the cost function to perform path planning for the wheel-legged robot based on the global grid map, initial point, and target point to obtain the path of the wheel-legged robot. The cost function is used to calculate the cost of the nodes expanded by the A* algorithm.

[0025] The cost function is a legged motion cost function, a wheeled motion cost function, or a wheel-legged composite motion cost function.

[0026] Construct a path data set; the path data set includes an initial point and a target point.

[0027] Let the value of k be 1; take the initial point as the first node.

[0028] Use the A* algorithm to expand the k-th node based on the filtered global grid map to obtain multiple candidate nodes of the (k + 1)-th node.

[0029] Calculate the cost of each candidate node of the (k + 1)-th node based on the cost function to obtain a cost set.

[0030] Determine the passability index of each candidate node of the (k + 1)-th node.

[0031] Extract the features of each candidate node of the (k + 1)-th node to obtain the feature data of each candidate node of the (k + 1)-th node; the feature data includes roughness, slope, and step height.

[0032] Select the ground roughness, slope, and step height as terrain feature indicators, and gradually extract the features of the nodes within the range through the filtered global raster map. As Figure 2 shown, when the wheel-legged robot is at the k-th node , a rectangle with length and width corresponding to the dimensions of the wheel-legged robot is selected to analyze the node information within the range. For the points on the terrain plane, a cube with a side length of is used to enclose the point cloud in the three-dimensional space. Based on this point cloud set, the method of singular value decomposition is used to perform plane fitting on the point cloud set to obtain the unit orthogonal vectors , , is the three-dimensional real vector space. The three-dimensional coordinate system of the k-th node of the fitted plane can be composed of three unit vectors , as shown in the following formula.

[0033] .

[0034] .

[0035] .

[0036] Among them, points to the next raster, represents the three-dimensional vector pointing from the coordinate at the k position to the coordinate at the k + 1 position, represents the three-dimensional coordinate of the node at the k + 1 position. And , represents transpose of. represents the unit vector of the z-axis of the k-th node, which is a one-dimensional vector used to characterize in direction of projection. represents the unit vector of the x-axis of the k-th node; represents the unit vector of the y-axis of the k-th node; , R represents the rotation of the local plane coordinate system. t represents the translation of the local coordinate system on the plane, .

[0037] Calculate the passability index of each candidate node of the (k + 1)-th node based on the feature data of each candidate node of the k-th node, as shown in the following formula.

[0038] .

[0039] .

[0040] 。

[0041] represents the step height of the l -th nearby node within the matrix with length w and width centered at the -th candidate node of the N l w -th candidate node of the node;

[0042] Roughness characterizes the continuity of the terrain and can be defined as the existence of discontinuous planes in the terrain within a microscopic range. It can be quantified using the mean square deviation of the grid heights within a rectangular range. Therefore, the roughness of the

[0043] -th candidate node of the

[0044] node is given by the following formula. where is the passability index of the -th candidate node of the node; is the ratio of the slope; is the slope of the -th candidate node of the node; is the maximum slope value that the wheel-legged robot can pass through, determined by the robot's motion ability; is the ratio of the roughness; is the roughness of the -th candidate node of the node; is the maximum roughness value that the wheel-legged robot can pass through, determined by the robot's motion ability; is the ratio of the step height, and

[0045] The difficulty for the wheel-legged robot to pass through the terrain increases from 1 to 0. When , it is considered that the -th candidate node of the node is impassable. When the passability index of the

[0046] Determine the cost of each candidate node of the (k + 1)-th node based on the passability index of each candidate node of the (k + 1)-th node to obtain a cost set.

[0047] Define a cost function for path exploration according to the motion characteristics of the wheel-legged robot to reduce the motion energy consumption of the robot and make the path generation more in line with the motion characteristics of the wheel-legged robot.

[0048] Determine the passability cost of each candidate node of the (k + 1)-th node based on the passability index of each candidate node of the (k + 1)-th node, as shown in the following formula.

[0049] 。

[0050] Judge whether the difference between the step height of each candidate node of the (k + 1)-th node and the step height of the k-th node is greater than or equal to a first preset threshold to obtain a second judgment result. The first preset threshold is 3 / 2 , is the wheel radius at the wheel end of the wheel-legged robot.

[0051] If the second judgment result is yes, then use the legged motion cost function to determine the cost of each candidate node of the (k + 1)-th node based on the passability cost of each candidate node of the (k + 1)-th node to obtain a cost set, as follows.

[0052] 。

[0053] 。

[0054] 。

[0055] If the second judgment result is no, then judge whether the roughness of each candidate node of the (k + 1)-th node is greater than or equal to a second preset threshold to obtain a third judgment result. The second preset threshold is 1 / 4 。

[0056] If the third judgment result is yes, then use the wheel-legged composite motion cost function to determine the cost of each candidate node of the (k + 1)-th node based on the passability cost of each candidate node of the (k + 1)-th node to obtain a cost set, as follows.

[0057] 。

[0058] 。

[0059] If the third judgment result is no, then use the wheeled motion cost function to determine the cost of each candidate node of the (k + 1)-th node based on the passability cost of each candidate node of the (k + 1)-th node to obtain a cost set, as follows.

[0060] 。

[0061] 。

[0062] In order to penalize the lateral and backward movements of the wheel - leg robot, a turning cost is designed, so it includes two parts: the lateral movement cost and the backward movement cost. To calculate the turning cost, the angle between two adjacent nodes in the moving direction needs to be calculated first.

[0063] Calculate the angle between the \(i\) - th candidate node of the \((k + 1)\) - th node and the \(k\) - th node using the following formula.

[0064] 。

[0065] Among them, represents the angle between the \(i\) - th candidate node of the \((k + 1)\) - th node and the \(k\) - th node; represents the ordinate of the \(i\) - th candidate node of the \((k + 1)\) - th node; represents the ordinate of the \(k\) - th node; represents the abscissa of the \(i\) - th candidate node of the \((k + 1)\) - th node; represents the abscissa of the \(k\) - th node.

[0066] So the lateral movement cost of the \(i\) - th candidate node of the \((k + 1)\) - th node and the backward movement cost

[0067] 。

[0068] 。

[0069] Then the total turning cost is: 。

[0070] Among them, are the weights of the lateral movement cost and the backward movement cost of the \(i\) - th candidate node of the \((k + 1)\) - th node respectively, and their sum is 1.

[0071] Based on the different motion mode primitives selected in different terrains, they can be divided into wheel - type motion mode primitives, leg - type motion mode primitives, and wheel - leg type motion mode primitives.

[0072] The wheel - type motion mode primitive allows the wheel - leg robot to perform translational or rotational movements, and is suitable for flat terrains far from obstacles.

[0073] The cost of the motion mode primitive of the $i$-th candidate node of the $(k + 1)$-th node calculated using the wheeled motion cost function is as follows.

[0074] .

[0075] Among them, represents the number of grid cells that the robot moves along direction; represents the number of grid cells that the robot moves along direction; represents the distance between the $i$-th candidate node of the $(k + 1)$-th node and the $k$-th node; represents the cost related to the wheeled movement action required to reach the $i$-th candidate node of the $(k + 1)$-th node; represents the rotation angle required to reach the $i$-th candidate node of the $(k + 1)$-th node.

[0076] The legged motion mode primitive allows the legs of the wheel-legged robot to straddle obstacles and is suitable for complex environments with protruding obstacles, etc.

[0077] Taking the cost of the motion mode primitive of the $i$-th candidate node of the $(k + 1)$-th node calculated using the legged motion cost function as an example, it is as follows.

[0078] .

[0079] Among them, represents the movement distance of the center of mass of the torso of the wheel-legged robot to reach the $i$-th candidate node of the $(k + 1)$-th node; represents the lowest motion energy consumption of the legged motion of the wheel-legged robot to reach the $i$-th candidate node of the $(k + 1)$-th node; represents the movement distance of the wheel end of the wheel-legged robot to reach the $i$-th candidate node of the $(k + 1)$-th node.

[0080] The wheel-legged composite motion mode primitive enables the wheel-legged robot to move its legs and wheels simultaneously and can pass through continuous roads with large ground roughness. Its motion can be regarded as the decoupling of wheeled motion and legged motion. Therefore, the cost of the wheel-legged composite motion mode primitive can be regarded as the weighted sum of the costs of the wheeled motion mode primitive and the legged motion primitive.

[0081] Taking the cost of the motion mode primitive of the $i$-th candidate node of the $(k + 1)$-th node calculated using the wheel-legged composite motion cost function as an example, it is as follows.

[0082] .

[0083] Among them, are the weights of the motion mode primitive cost of the i-th candidate node of the (k + 1)-th node calculated using the wheeled motion cost function and the weights of the motion mode primitive cost of the i-th candidate node of the (k + 1)-th node calculated using the legged motion cost function, and there is , is the distance from the i-th candidate node of the (k + 1)-th node to the k-th node; is a parameter for adjusting passability; is a parameter for adjusting distance; is the cost of each candidate node of the (k + 1)-th node calculated using the legged motion cost function; is the cost of each candidate node of the (k + 1)-th node calculated using the wheel-legged compound motion cost function; is the cost of each candidate node of the (k + 1)-th node calculated using the wheeled motion cost function; is the heuristic estimate of the cost of the minimum path from the i-th candidate node of the (k + 1)-th node to the target point; is the cost from the starting point to the i-th candidate node of the (k + 1)-th node calculated using the legged motion cost function; is the cost from the starting point to the i-th candidate node of the (k + 1)-th node calculated using the wheel-legged compound motion cost function; is the cost from the starting point to the i-th candidate node of the (k + 1)-th node calculated using the wheeled motion cost function, where at when, ; is the scaling factor; is the passability cost of the i-th candidate node of the (k + 1)-th node; is the turning cost of the i-th candidate node of the (k + 1)-th node; is the motion mode primitive cost of the i-th candidate node of the (k + 1)-th node calculated using the legged motion cost function; is the motion mode primitive cost of the i-th candidate node of the (k + 1)-th node calculated using the wheel-legged compound motion cost function; is the motion mode primitive cost of the i-th candidate node of the (k + 1)-th node calculated using the wheeled motion cost function; is the spatial position of the i-th candidate node of the (k + 1)-th node; is the spatial position of the target point.

[0084] On the premise of ensuring terrain passability, the wheeled motion mode primitive is applicable to environments with a relatively low step height and small road surface roughness; the legged motion mode primitive is applicable to environments exceeding the tire obstacle-crossing ability; the wheel-legged compound motion mode primitive is applicable to environments with a relatively large ground roughness.

[0085] Take the candidate node corresponding to the minimum value in the cost concentration as the optimal (k + 1)-th node, and store the optimal (k + 1)-th node in the path dataset to obtain the path dataset after the k-th update.

[0086] Determine whether the iteration end condition is reached to obtain a first determination result. The iteration end condition is reaching the iteration number or the optimal (k + 1)-th node being the target point.

[0087] If the first determination result is yes, the iteration ends. Connect each node in the path dataset after the k-th update in sequence to obtain the path of the wheel-legged robot.

[0088] If the first determination result is no, increment the value of k by 1, and return to the step of using the A* algorithm to expand the k-th node based on the filtered global grid map to obtain multiple candidate nodes of the (k + 1)-th node.

[0089] The path planning of current traditional wheel-legged robots often defaults to using the same motion modality and cannot perform motion modality switching according to terrain features and its own motion characteristics. However, wheel-legged robots have multiple motion modalities and can select different motion modalities on different terrains, enhancing the smoothness and passability of path execution. And by assigning motion modalities to the path points during the planning process, the decoupling of the planning and control processes can be achieved, reducing the difficulty of the subsequent path tracking control.

[0090] The advantages of the present invention are as follows.

[0091] 1. By extracting terrain features and defining and calculating the passability index of the terrain, the priority of path points in the terrain can be obtained when expanding the path, making the path generation more reasonable and improving the path feasibility.

[0092] 2. Considering various influencing factors of the wheel-legged robot during movement in the cost makes the path generation more in line with the kinematic characteristics of the wheel-legged robot. Traditional wheel-legged robot path planning does not consider the switching of motion modalities of the wheel-legged robot, reducing the adaptability of the wheel-legged robot to the terrain and unable to exert its maximum motion ability.

[0093] 3. The wheel-legged robot of the present invention can select appropriate motion modalities on different terrains to improve the motion ability; by defining the motion modality of path points in the planning link, the decoupling of the path planning link and the subsequent path tracking control is achieved, reducing the difficulty of the path tracking control link.

[0094] Embodiment 2: The present invention also provides a wheel-legged robot path planning system, including the following modules.

[0095] A perception module, configured to obtain the environmental point cloud information, initial point, and target point of the wheel-legged robot. The perception module can also obtain obstacle information, terrain information, etc.

[0096] A construction module, configured to filter the environmental point cloud information and construct a global grid map based on the filtered environmental point cloud information.

[0097] A path planning module, which performs path planning for the wheel-legged robot based on the global grid map, initial point, and target point by using the A* algorithm and a cost function, to obtain the path of the wheel-legged robot; the cost function is used to calculate the cost of the nodes expanded by the A* algorithm.

[0098] Embodiment 3: A computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the wheel-legged robot path planning method in Embodiment 1 are implemented.

[0099] Embodiment 4: A computer program product, including a computer program. When the computer program is executed by a processor, the steps of the wheel-legged robot path planning method in Embodiment 1 are implemented.

[0100] Embodiment 5: A computer device, which can be a database. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, memory, and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store transactions to be processed. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. The computer program, when executed by the processor, implements the wheel-legged robot path planning method in Embodiment 1.

[0101] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0102] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided by the present invention can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided by the present invention can be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0103] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0104] Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A path planning method for a wheel-legged robot, characterized in that, The method includes: Obtaining the environmental point cloud information, initial point, and target point of the wheel-legged robot; Filtering the environmental point cloud information and constructing a global grid map based on the filtered environmental point cloud information; Performing path planning on the wheel-legged robot based on the global grid map, initial point, and target point using the A* algorithm and a cost function to obtain the path of the wheel-legged robot; the cost function is used to calculate the cost of the nodes expanded by the A* algorithm; the cost function is a legged motion cost function, a wheeled motion cost function, or a wheel-legged composite motion cost function.

2. The path planning method of the wheel-legged robot according to claim 1, characterized in that Obtaining the environmental point cloud information, initial point, and target point of the wheel-legged robot specifically includes: Using a lidar to obtain the environmental point cloud information, initial point, and target point of the wheel-legged robot.

3. The path planning method for a wheel-legged robot according to claim 1, wherein Performing path planning on the wheel-legged robot based on the global grid map, initial point, and target point using the A* algorithm and a cost function to obtain the path of the wheel-legged robot specifically includes: Constructing a path dataset; the path dataset includes the initial point and the target point; Setting the value of k to 1; taking the initial point as the first node; Using the A* algorithm to expand the k-th node based on the global grid map to obtain multiple candidate nodes for the (k + 1)-th node; Calculating the cost of each candidate node of the (k + 1)-th node based on the cost function to obtain a cost set; Taking the candidate node corresponding to the minimum value in the cost set as the optimal (k + 1)-th node and storing the optimal (k + 1)-th node in the path dataset to obtain the path dataset updated for the k-th time; Determining whether the iteration end condition is reached to obtain a first judgment result; the iteration end condition is reaching the iteration number or the optimal (k + 1)-th node being the target point; If the first judgment result is yes, the iteration ends, and each node in the path dataset updated for the k-th time is connected in sequence to obtain the path of the wheel-legged robot; If the first judgment result is no, then increasing the value of k by 1 and returning to the step of using the A* algorithm to expand the k-th node based on the global grid map to obtain multiple candidate nodes for the (k + 1)-th node.

4. The path planning method for a wheel-legged robot according to claim 3, wherein, Calculating the cost of each candidate node of the (k + 1)-th node based on the cost function to obtain a cost set specifically includes: Determining the passability index of each candidate node of the (k + 1)-th node; Determining the cost of each candidate node of the (k + 1)-th node based on the passability index of each candidate node of the (k + 1)-th node to obtain a cost set.

5. The path planning method of the wheel-legged robot according to claim 4, characterized in that Determining the passability index of each candidate node of the (k + 1)-th node specifically includes: Performing feature extraction on each candidate node of the (k + 1)-th node to obtain the feature data of each candidate node of the (k + 1)-th node; the feature data includes roughness, slope, and step height; Calculating the passability index of the feature data of each candidate node of the (k + 1)-th node based on the feature data of each candidate node of the (k + 1)-th node: ； Among them, is the passability index of the i-th candidate node of the (k + 1)-th node; is the ratio of the slope; is the slope of the i-th candidate node of the (k + 1)-th node; is the maximum slope value that the wheel-legged robot can pass; is the ratio of the roughness; is the roughness of the i-th candidate node of the (k + 1)-th node; is the maximum roughness value that the wheel-legged robot can pass; is the ratio of the step height; is the step height of the i-th candidate node of the (k + 1)-th node; is the maximum step height value that the wheel-legged robot can pass.

6. The path planning method of the wheel-legged robot according to claim 4, wherein Determining the cost of each candidate node of the (k + 1)-th node based on the passability index of each candidate node of the (k + 1)-th node to obtain a cost set specifically includes: Determining the passability cost of each candidate node of the (k + 1)-th node based on the passability index of each candidate node of the (k + 1)-th node: ； Determine whether the difference between the step height of each candidate node of the (k + 1)-th node and the step height of the k-th node is greater than or equal to a first preset threshold to obtain a second judgment result; If the second judgment result is yes, use the legged motion cost function to determine the cost of each candidate node of the (k + 1)-th node based on the passability cost of each candidate node of the (k + 1)-th node to obtain a cost set: ； ； ； If the second judgment result is no, determine whether the roughness of each candidate node of the (k + 1)-th node is greater than or equal to a second preset threshold to obtain a third judgment result; If the third judgment result is yes, use the wheel-legged composite motion cost function to determine the cost of each candidate node of the (k + 1)-th node based on the passability cost of each candidate node of the (k + 1)-th node to obtain a cost set: ； ； If the third judgment result is no, use the wheeled motion cost function to determine the cost of each candidate node of the (k + 1)-th node based on the passability cost of each candidate node of the (k + 1)-th node to obtain a cost set: ； ； Wherein, is the passability index of the i-th candidate node of the (k + 1)-th node; is the distance from the i-th candidate node of the (k + 1)-th node to the k-th node; is the parameter for adjusting passability; is the parameter for adjusting distance; is the cost of each candidate node of the (k + 1)-th node calculated by using the legged motion cost function; is the cost of each candidate node of the (k + 1)-th node calculated by using the wheel-legged compound motion cost function; is the cost of each candidate node of the (k + 1)-th node calculated by using the wheeled motion cost function; is the heuristic estimate of the cost of the minimum path from the i-th candidate node of the (k + 1)-th node to the target point; is the cost from the starting point to the i-th candidate node of the (k + 1)-th node calculated by using the legged motion cost function; is the cost from the starting point to the i-th candidate node of the (k + 1)-th node calculated by using the wheel-legged compound motion cost function; is the cost from the starting point to the i-th candidate node of the (k + 1)-th node calculated by using the wheeled motion cost function; is the scaling factor; is the passability cost of the i-th candidate node of the (k + 1)-th node; is the turning cost of the i-th candidate node of the (k + 1)-th node; is the motion mode primitive cost of the i-th candidate node of the (k + 1)-th node calculated by using the legged motion cost function; is the motion mode primitive cost of the i-th candidate node of the (k + 1)-th node calculated by using the wheel-legged compound motion cost function; is the motion mode primitive cost of the i-th candidate node of the (k + 1)-th node calculated by using the wheeled motion cost function; is the spatial position of the i-th candidate node of the (k + 1)-th node; is the spatial position of the target point.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the wheel-legged robot path planning method described in any one of claims 1-6.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the wheel-legged robot path planning method described in any one of claims 1-6.

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