Path planning method, system and device for air-ground amphibious robot and storage medium

Abstract

The invention discloses a path planning method, system and device for an air-ground amphibious robot and a storage medium, and belongs to the technical field of robot path planning. The method comprises the steps that parameter data of a target robot and map point cloud data of an area where the target robot is located are acquired, and a three-dimensional grid diagram is constructed according to the map point cloud data; constructing an air cost function and a ground cost function of the target robot; traversing adjacent nodes of the starting point, calculating the total cost of the adjacent nodes according to a cost function of the target robot, and taking the adjacent node with the minimum total cost as an expansion node; taking the expansion node as the current node, and taking the adjacent node with the minimum total cost as the next expansion node until the target point is traversed or no adjacent node capable of being traversed exists; and optimizing the original path by adopting a B spline curve to obtain a final smooth path. According to the invention, the robot can flexibly select an air flight mode or a ground movement mode according to different environments, so that a path with higher efficiency is planned.

Description

Technical Field

[0001] The present invention belongs to the technical field of robot path planning, and in particular relates to a path planning method, system, device and storage medium for a land and air amphibious robot. Background Art

[0002] With the continuous improvement of modern production level, robots have been widely used as important practical tools to help humans complete a variety of complex and difficult tasks. However, in today's engineering, robots with only one type of movement ability can no longer meet the needs. Since robots with a single movement mode only have the ability to work in a certain environmental domain, multi-robots that can work in multiple environments based on actual needs have gradually become a research hotspot at home and abroad. Robots with the ability to adapt to multiple environments can work in more complex environments and can make up for the shortcomings of robots working in a single environment. In order to enable robots to better adapt to complex environments, robots need to have the ability to switch modes autonomously. When encountering steep terrain or large gullies that are impassable, the robot can autonomously switch to flight mode to cross obstacles; when the robot is low on power, it can switch to ground wheeled mode to move.

[0003] In the existing technology, most of the robot paths are planned using the A* algorithm. However, the A* algorithm generally runs on a discretized grid map, and the path must be along the grid nodes and edges. This search method will result in more turning points on the path; at the same time, some paths may be very close to obstacles, which will cause collisions in practical applications; in addition, the traditional A* algorithm is mainly aimed at a single working domain and is not suitable for multi-habitat motion robots. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a path planning method, system, device and storage medium for a land-air amphibious robot, so that the robot can flexibly select air flight or ground movement mode according to different environments, thereby planning a more efficient obstacle avoidance path.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, a path planning method for a land-air amphibious robot is provided, comprising: obtaining parameter data of a target robot and map point cloud data of an area where the target robot is located, and constructing a three-dimensional grid map including a starting point and a target point according to the map point cloud data;

[0007] Based on the parameter data of the target robot, the dynamic models of the target robot in the air and on the ground are respectively established, and the air cost function and the ground cost function of the target robot are respectively constructed according to the dynamic models in the air and on the ground;

[0008] In the three-dimensional grid graph, the adjacent nodes of the starting point are traversed, the total cost of the adjacent nodes is calculated according to the air cost function and the ground cost function of the target robot, and the adjacent node with the smallest total cost is used as the expansion node; the expansion node is used as the current node, its adjacent nodes are traversed, and the adjacent node with the smallest total cost is used as the next expansion node, and the cycle is repeated until the target point is traversed or there are no adjacent nodes to traverse;

[0009] If the target point is traversed, all the extended nodes are used as the original path of the target robot, and the B-spline curve is used to optimize the original path to obtain the final smooth path; if there are no traversable adjacent nodes, no path is generated.

[0010] As an optional technical solution of the present invention, the parameter data of the target robot includes weight data, motor power and driving speed; and the three-dimensional grid map also includes obstacle points.

[0011] As an optional technical solution of the present invention, the air cost function and the ground cost function of the target robot are constructed according to the air and ground dynamic models respectively, including:

[0012] Construct the air energy consumption function and air time consumption function of the target robot, which are expressed as:

[0013] ;

[0014] ;

[0015] in, represents the air energy consumption function, P represents the motor power of the target robot, represents the air travel distance of the target robot, represents the time consumption function in the air, Indicates the air speed of the target robot;

[0016] Construct the ground energy consumption function and ground time consumption function of the target robot, which are expressed as:

[0017] ;

[0018] ;

[0019] in, represents the ground energy consumption function, is the ground friction coefficient, The angle representing the ground slope, represents the gravity of the target robot, represents the ground time consumption function, represents the ground speed of the target robot, represents the ground travel distance of the target robot;

[0020] The aerial cost function and ground cost function of the target robot are expressed as:

[0021] ;

[0022] ;

[0023] in, represents the air cost function, represents the ground cost function, Represents the Euclidean distance from the current node to the target point, and Respectively represent the air unit cost and the ground unit cost, which are expressed as:

[0024] ;

[0025] ;

[0026] in, represents the ground unit energy consumption, represents the unit energy consumption in the air, Indicates the time consumption per unit on the ground. Indicates the unit time consumption in the air, Represents the weight coefficient.

[0027] As an optional technical solution of the present invention, in the three-dimensional grid graph, the adjacent nodes of the starting point are traversed, the total cost of the adjacent nodes is calculated according to the cost function of the target robot, and the adjacent node with the smallest total cost is used as the expansion node, and the expansion node is used as the current node, and its adjacent nodes are traversed and the adjacent node with the smallest total cost is used as the next expansion node, and the cycle is performed until the target point is traversed or there is no adjacent node to be traversed, including:

[0028] Create an open list and a close list, add the starting point to the open list, add the non-obstruction adjacent nodes of the starting point to the open list and traverse;

[0029] Take the starting point as the current node, remove it from the open list and add it to the close list, determine the cost function based on the height of the current node and its adjacent nodes, get the actual cost increment, and get the actual cost of the adjacent nodes based on the actual cost increment;

[0030] Determine the cost function according to the height of adjacent nodes and obtain the heuristic cost;

[0031] The sum of the actual cost and the heuristic cost is taken as the total cost;

[0032] Take the adjacent node with the smallest total cost as the expansion node, remove the expansion node from the open list and add it to the close list;

[0033] Take the expanded node as the current node, add all its adjacent nodes to the open list and traverse, remove the adjacent node with the smallest total cost from the open list as the next expanded node and add it to the close list; repeat the process until the target node is traversed or the open list is empty;

[0034] If the target point is traversed, it means that the original path is obtained; if the open list is empty, it means that no path is generated.

[0035] As an optional technical solution of the present invention, the starting point is used as the current node, the cost function is determined according to the height of the current node and its adjacent nodes, the actual cost increment is obtained, and the actual cost of the adjacent nodes is obtained based on the actual cost increment, including:

[0036] If the current node height is 0 and the adjacent node height is also 0, the ground cost function is used to calculate the actual cost increment; if the current node height is 0 and the adjacent node height is greater than 0, the air cost function is used and the take-off cost is added to calculate the actual cost increment; if the current node height is greater than 0 and the adjacent node height is also greater than 0, the air cost function is used to calculate the actual cost increment; if the current node height is greater than 0 and the adjacent node height is equal to 0, the air cost function is used and the landing cost is added to calculate the actual cost increment;

[0037] The sum of the actual cost increment and the actual cost of the current node is taken as the actual cost of the adjacent node;

[0038] The cost function is determined according to the height of the adjacent node to obtain the heuristic cost, including: if the height of the adjacent node is 0, the heuristic cost is calculated using the ground cost function; if the height of the adjacent node is greater than 0, the sum of the landing cost and the ground cost is used as the heuristic cost.

[0039] Among them, the take-off cost is the energy and time consumed by the robot during take-off, and the landing cost is the energy and time consumed by the robot during landing; the take-off cost and landing cost are both obtained through the robot's weight data and motor power.

[0040] As an optional technical solution of the present invention, the method of optimizing the original path using a B-spline curve to obtain a final smooth path includes:

[0041] The B-spline curve is expressed as:

[0042] ;

[0043] in, Indicates positional parameters The corresponding curve point is represents the B-spline basis function of order p, represents the order of B-spline, represents the i-th control point, Indicates that the number of control points decreases by one.

[0044] In a second aspect, a path planning system for an amphibious robot is provided, comprising: a three-dimensional construction module for obtaining parameter data of a target robot and map point cloud data of its area, and constructing a three-dimensional grid map including a starting point and a target point according to the map point cloud data;

[0045] A function construction module, used to establish the target robot's dynamic models in the air and on the ground based on the parameter data of the target robot, and to construct the target robot's air cost function and ground cost function according to the air and ground dynamic models respectively;

[0046] A traversal module is used to traverse the adjacent nodes of the starting point in the three-dimensional grid map, calculate the total cost of the adjacent nodes according to the air cost function and the ground cost function of the target robot, and use the adjacent node with the smallest total cost as the expansion node; use the expansion node as the current node, traverse its adjacent nodes and use the adjacent node with the smallest total cost as the next expansion node, and repeat the cycle until the target point is traversed or there are no adjacent nodes to traverse;

[0047] The path smoothing module is used to use all the extended nodes as the original path of the target robot if the target point is traversed, and use the B-spline curve to optimize the original path to obtain the final smooth path; if there are no traversable adjacent nodes, no path is generated.

[0048] In a third aspect, a path planning device for a land-air amphibious robot is provided, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the path planning method for the land-air amphibious robot described in the first aspect.

[0049] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of the path planning method for a land-air amphibious robot described in the first aspect are implemented.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention provides a path planning method for a land-air amphibious robot. The method distinguishes the different costs of ground movement and aerial flight by constructing an aerial cost function and a ground cost function of a target robot, so that the robot can flexibly select an aerial flight or ground movement mode according to different environments, thereby planning a more efficient path. The original path is smoothed by a B-spline curve, which can effectively reduce the twists and turns in the path, reduce the number of turns of the robot, reduce energy consumption and improve the efficiency of path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of a path planning method for a land-air amphibious robot in an embodiment of the present invention;

[0053] Figure 2 is a schematic diagram of a ground dynamics model of a robot in an embodiment of the present invention;

[0054] Figure 3 is a schematic diagram of a robot aerial dynamics model in an embodiment of the present invention;

[0055] Figure 4 is a schematic diagram of a starting point, a target point, and a node to be expanded in an embodiment of the present invention;

[0056] Figure 5 is a schematic diagram of the expansion strategy of the current node height and adjacent nodes in an embodiment of the present invention;

[0057] Figure 6 Schematic diagram of the traditional A* algorithm obstacle avoidance path planning in an embodiment of the present invention;

[0058] Figure 7 is a schematic diagram of an improved A* algorithm obstacle avoidance path planning in an embodiment of the present invention;

[0059] Figure 8 Schematic diagram of the B-spline optimization path in the embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0061] Example 1

[0062] This embodiment provides a path planning method for an amphibious robot. Figure 1 As shown, the specific steps include:

[0063] Step 1: Obtain the parameter data of the target robot and the map point cloud data of its area, and construct a three-dimensional grid map including the starting point and the target point based on the map point cloud data.

[0064] The parameter data of the target robot include weight data, motor power and driving speed. The three-dimensional grid map also includes obstacle points.

[0065] The origin position, map size and resolution of the map are set through the map point cloud data; the three-dimensional size of the map is converted into the grid size of the occupancy map through the resolution. Considering the safety of the robot when avoiding obstacles, new points are generated by expanding each point in the three-dimensional space according to the set robot radius. These expanded points are used as representations of obstacles. These newly generated points are used as obstacle points and are updated in the three-dimensional grid map.

[0066] Step 2: Based on the parameter data of the target robot, the dynamic models of the target robot in the air and on the ground are respectively established, and the air cost function and the ground cost function of the target robot are respectively constructed according to the dynamic models in the air and on the ground.

[0067] like Figure 2 As shown, the robot is a four-wheel model in ground mode, where the rear two wheels are driving wheels and the front two wheels are driven wheels. Steering is achieved by driving wheel differential. In the figure, L represents the wheelbase. represents the turning angle, R represents the turning radius, represents the outer speed, Indicates the inside speed.

[0068] like Figure 3 As shown in the figure, the robot is a quadrotor (M1-M4) model in the air. In order to simplify the dynamic model, the body is treated as a rigid body and the air resistance is ignored. The body structure is symmetrical. The robot's motion is related to the speed of the rotor motor, and the lift is proportional to the speed. Forward motion, the three attitude angles do not change. Combining its position dynamics model and attitude dynamics model, the speed of each motor is solved, and the corresponding motor power is obtained according to the motor model. .

[0069] 2.1. Construct the air energy consumption function and air time consumption function of the target robot, which are expressed as:

[0070] ;

[0071] ;

[0072] in, represents the air energy consumption function, P represents the motor power of the target robot, represents the air travel distance of the target robot, represents the time consumption function in the air, Indicates the air speed of the target robot.

[0073] 2.2. During ground driving, the robot may encounter a certain slope . Construct the ground energy consumption function and ground time consumption function of the target robot, which are expressed as:

[0074] ;

[0075] ;

[0076] in, represents the ground energy consumption function, is the ground friction coefficient, The angle representing the ground slope, represents the gravity of the target robot, represents the ground time consumption function, represents the ground speed of the target robot, Represents the ground travel distance of the target robot.

[0077] 2.3. The aerial cost function and ground cost function of the target robot are expressed as:

[0078] ;

[0079] ;

[0080] in, represents the air cost function, represents the ground cost function, Represents the Euclidean distance from the current node to the target point, and Respectively represent the air unit cost and the ground unit cost, which are expressed as:

[0081] ;

[0082] ;

[0083] in, represents the ground unit energy consumption, represents the unit energy consumption in the air, Indicates the time consumption per unit on the ground. Indicates the unit time consumption in the air, Represents the weight coefficient.

[0084] Used to adjust the relative importance of time and energy factors in the total cost. The value of can be adjusted to reflect different requirements for time or energy efficiency. A larger value of indicates that more attention is paid to the energy factor; if A smaller value of indicates that more emphasis is placed on the time factor.

[0085] In this embodiment, the ground unit energy consumption , air unit energy consumption , Ground unit time consumption , air unit time consumption All are calculated using a unit distance of 1m.

[0086] Step 3: In the three-dimensional grid graph, traverse the adjacent nodes of the starting point, calculate the total cost of the adjacent nodes according to the air cost function and ground cost function of the target robot, and use the adjacent node with the smallest total cost as the expansion node; use the expansion node as the current node, traverse its adjacent nodes and use the adjacent node with the smallest total cost as the next expansion node, and repeat the cycle until the target point is traversed or there are no adjacent nodes to traverse.

[0087] 3.1. Create an open list and a close list, add the starting point to the open list, add the non-obstacle adjacent nodes of the starting point to the open list and traverse.

[0088] like Figure 4 As shown, there are 26 non-obstacle neighboring nodes around the starting point.

[0089] 3.2. Take the starting point as the current node, remove it from the open list and add it to the close list, determine the cost function according to the height of the current node and its adjacent nodes, get the actual cost increment, and get the actual cost of the adjacent nodes based on the actual cost increment.

[0090] In this embodiment, the actual cost refers to the cost from the starting point to the current node. The heuristic cost refers to the cost from the current node to the target point. The cost from the current node to the adjacent node is the actual cost increment of the adjacent node. The actual cost of the updated adjacent node is obtained by adding the actual cost increment to the actual cost of the current node, and the heuristic cost of the adjacent node is the cost from the adjacent node to the target point.

[0091] like Figure 5 As shown, if the current node height is , the adjacent node height is also , then the ground cost function is used to calculate the actual cost increment; if the current node height is , the height of adjacent nodes , then use the air cost function and add the takeoff cost to calculate the actual cost increment; if the current node height , the height value of adjacent nodes , then use the air cost function to calculate the actual cost increment; if the current node height , the height of adjacent nodes , then use the air cost function and add the landing cost to calculate the actual cost increment. The sum of the actual cost increment and the actual cost of the current node is taken as the actual cost of the adjacent node.

[0092] Among them, the take-off cost is the energy and time consumed by the robot during take-off, and the landing cost is the energy and time consumed by the robot during landing; the take-off cost and landing cost are both obtained through the robot's weight data and motor power.

[0093] In this embodiment, the actual cost increment is calculated by multiplying the corresponding cost function by the distance from the current node to the adjacent node.

[0094] 3.3. Determine the cost function according to the height of adjacent nodes and obtain the heuristic cost.

[0095] The cost function is determined according to the height of the adjacent node to obtain the heuristic cost, including: if the height of the adjacent node is 0, the heuristic cost is calculated using the ground cost function; if the height of the adjacent node is greater than 0, the sum of the landing cost and the ground cost is used as the heuristic cost.

[0096] 3.4. The sum of the actual cost and the heuristic cost is taken as the total cost.

[0097] 3.5. Take the adjacent node with the smallest total cost as the expansion node, remove the expansion node from the open list and add it to the close list.

[0098] 3.6. Take the expanded node as the current node, add all its adjacent nodes to the open list and traverse it, take the adjacent node with the smallest total cost as the next expanded node, remove it from the open list and add it to the close list; repeat the loop until the target node is traversed or the open list is empty.

[0099] 3.7. If the target point is traversed, it means that the original path is obtained; if the open list is empty, it means that no path is generated.

[0100] like Figure 6 As shown in the figure, when encountering an obstacle, the traditional A* algorithm will directly cross the obstacle. Although this can find the shortest path, for amphibious robots, they may need to fly over the obstacle, thus taking off and landing, which will consume more energy and time. Figure 7 As shown, the improved A* algorithm provided by the present invention will choose to bypass small obstacles when encountering these obstacles, thereby saving energy and time, but the path must be carried out along the grid nodes and edges. This discretization causes the path to look tortuous.

[0101] Step 4: If the target point is traversed, all the extended nodes are used as the original path of the target robot, and the B-spline curve is used to optimize the original path to obtain the final smooth path; if there are no traversable adjacent nodes, no path is generated.

[0102] The B-spline curve is expressed as:

[0103] ;

[0104] in, Indicates positional parameters The corresponding curve point is represents the B-spline basis function of order p, represents the order of B-spline, represents the i-th control point, Indicates that the number of control points decreases by one.

[0105] like Figure 8 As shown in the figure, B-spline has good smoothness. It can optimize the generated path and adjust the shape of the path through control points, effectively reducing the twists and turns in the path and making the path smoother. In addition, B-spline allows the local part of the path to be adjusted without affecting the entire path, which makes it possible to accurately control the shape of a specific area when adjusting the path.

[0106] Example 2

[0107] This embodiment provides a path planning system for an amphibious robot, including:

[0108] A three-dimensional construction module is used to obtain parameter data of the target robot and map point cloud data of the area where it is located, and to construct a three-dimensional grid map including a starting point and a target point based on the map point cloud data;

[0109] A function construction module, used to establish the target robot's dynamic models in the air and on the ground based on the parameter data of the target robot, and to construct the target robot's air cost function and ground cost function according to the air and ground dynamic models respectively;

[0110] A traversal module is used to traverse the adjacent nodes of the starting point in the three-dimensional grid map, calculate the total cost of the adjacent nodes according to the air cost function and the ground cost function of the target robot, and use the adjacent node with the smallest total cost as the expansion node; use the expansion node as the current node, traverse its adjacent nodes and use the adjacent node with the smallest total cost as the next expansion node, and repeat the cycle until the target point is traversed or there are no adjacent nodes to traverse;

[0111] The path smoothing module is used to use all the extended nodes as the original path of the target robot if the target point is traversed, and use the B-spline curve to optimize the original path to obtain the final smooth path; if there are no traversable adjacent nodes, no path is generated.

[0112] Example 3

[0113] This embodiment provides a path planning device for a land-air amphibious robot, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the path planning method for the land-air amphibious robot described in Example 1.

[0114] Example 4

[0115] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the path planning method for the land-air amphibious robot described in Embodiment 1 are implemented.

[0116] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0117] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0118] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0120] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A path planning method for an amphibious robot, characterized in that: include: Obtain parameter data of the target robot and map point cloud data of its area, and construct a three-dimensional grid map including a starting point and a target point based on the map point cloud data; Based on the parameter data of the target robot, the dynamic models of the target robot in the air and on the ground are respectively established, and the air cost function and the ground cost function of the target robot are respectively constructed according to the dynamic models in the air and on the ground; In the three-dimensional grid graph, traverse the adjacent nodes of the starting point, calculate the total cost of the adjacent nodes according to the air cost function and ground cost function of the target robot, and use the adjacent node with the smallest total cost as the expansion node; Take the expanded node as the current node, traverse its adjacent nodes and take the adjacent node with the smallest total cost as the next expanded node, and repeat the process until the target node is traversed or there are no adjacent nodes to traverse; If the target point is traversed, all the extended nodes are used as the original path of the target robot, and the B-spline curve is used to optimize the original path to obtain the final smooth path; if there are no traversable adjacent nodes, no path is generated.

2. The path planning method for an amphibious robot according to claim 1, characterized in that: The parameter data of the target robot include weight data, motor power and driving speed; The three-dimensional grid map also includes obstacle points.

3. The path planning method for an amphibious robot according to claim 1, characterized in that: The step of constructing an air cost function and a ground cost function of the target robot according to the air and ground dynamics models respectively includes: Construct the air energy consumption function and air time consumption function of the target robot, which are expressed as: ； ； in, represents the air energy consumption function, P represents the motor power of the target robot, represents the air travel distance of the target robot, represents the time consumption function in the air, Indicates the air speed of the target robot; Construct the ground energy consumption function and ground time consumption function of the target robot, which are expressed as: ； ； in, represents the ground energy consumption function, is the ground friction coefficient, The angle representing the ground slope, represents the gravity of the target robot, represents the ground time consumption function, represents the ground speed of the target robot, represents the ground travel distance of the target robot; The aerial cost function and ground cost function of the target robot are expressed as: ； ； in, represents the air cost function, represents the ground cost function, Represents the Euclidean distance from the current node to the target point, and Respectively represent the air unit cost and the ground unit cost, which are expressed as: ； ； in, represents the ground unit energy consumption, represents the unit energy consumption in the air, Indicates the time consumption per unit on the ground. Indicates the unit time consumption in the air, Represents the weight coefficient.

4. The path planning method for an amphibious robot according to claim 2, characterized in that: In the three-dimensional grid graph, the adjacent nodes of the starting point are traversed, the total cost of the adjacent nodes is calculated according to the cost function of the target robot, and the adjacent node with the smallest total cost is used as the expansion node, and the expansion node is used as the current node, and the adjacent nodes are traversed and the adjacent node with the smallest total cost is used as the next expansion node, and the cycle is performed until the target point is traversed or there are no adjacent nodes to traverse, including: Create an open list and a close list, add the starting point to the open list, add the non-obstruction adjacent nodes of the starting point to the open list and traverse; Take the starting point as the current node, remove it from the open list and add it to the close list, determine the cost function based on the height of the current node and its adjacent nodes, get the actual cost increment, and get the actual cost of the adjacent nodes based on the actual cost increment; Determine the cost function according to the height of adjacent nodes and obtain the heuristic cost; The sum of the actual cost and the heuristic cost is taken as the total cost; Take the adjacent node with the smallest total cost as the expansion node, remove the expansion node from the open list and add it to the close list; Take the expanded node as the current node, add all its adjacent nodes to the open list and traverse, remove the adjacent node with the smallest total cost from the open list as the next expanded node and add it to the close list; repeat the process until the target node is traversed or the open list is empty; If the target point is traversed, it means that the original path is obtained; if the open list is empty, it means that no path is generated.

5. The path planning method for an amphibious robot according to claim 4, characterized in that: The starting point is taken as the current node, a cost function is determined according to the height of the current node and its adjacent nodes, an actual cost increment is obtained, and an actual cost of the adjacent nodes is obtained based on the actual cost increment, including: If the current node height is 0 and the adjacent node height is also 0, the ground cost function is used to calculate the actual cost increment; if the current node height is 0 and the adjacent node height is greater than 0, the air cost function is used and the take-off cost is added to calculate the actual cost increment; if the current node height is greater than 0 and the adjacent node height is also greater than 0, the air cost function is used to calculate the actual cost increment; if the current node height is greater than 0 and the adjacent node height is equal to 0, the air cost function is used and the landing cost is added to calculate the actual cost increment; The sum of the actual cost increment and the actual cost of the current node is taken as the actual cost of the adjacent node; The cost function is determined according to the height of the adjacent node to obtain the heuristic cost, including: if the height of the adjacent node is 0, the heuristic cost is calculated using the ground cost function; if the height of the adjacent node is greater than 0, the sum of the landing cost and the ground cost is used as the heuristic cost. Among them, the take-off cost is the energy and time consumed by the robot during take-off, and the landing cost is the energy and time consumed by the robot during landing; the take-off cost and landing cost are both obtained through the robot's weight data and motor power.

6. The path planning method for an amphibious robot according to claim 1, characterized in that: The method of optimizing the original path using the B-spline curve to obtain the final smooth path includes: The B-spline curve is expressed as: ； in, Indicates positional parameters The corresponding curve point is represents the B-spline basis function of order p, represents the order of B-spline, represents the i-th control point, Indicates that the number of control points decreases by one.

7. A path planning system for an amphibious robot, characterized in that: include: A three-dimensional construction module is used to obtain parameter data of the target robot and map point cloud data of the area where it is located, and to construct a three-dimensional grid map including a starting point and a target point based on the map point cloud data; A function construction module, used to establish the target robot's dynamic models in the air and on the ground based on the parameter data of the target robot, and to construct the target robot's air cost function and ground cost function according to the air and ground dynamic models respectively; The traversal module is used to traverse the adjacent nodes of the starting point in the three-dimensional grid graph, calculate the total cost of the adjacent nodes according to the air cost function and the ground cost function of the target robot, and take the adjacent node with the smallest total cost as the expansion node; Take the expanded node as the current node, traverse its adjacent nodes and take the adjacent node with the smallest total cost as the next expanded node, and repeat the process until the target node is traversed or there are no adjacent nodes to traverse; The path smoothing module is used to use all the extended nodes as the original path of the target robot if the target point is traversed, and use the B-spline curve to optimize the original path to obtain the final smooth path; if there are no traversable adjacent nodes, no path is generated.

8. A path planning device for an amphibious robot, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the path planning method for a land-air amphibious robot as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the path planning method for a land-air amphibious robot described in any one of claims 1 to 6 are implemented.

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