Path planning method and device for mobile robot, medium and product
By using the piecewise heuristic function and cost calculation of the FA* algorithm, the problems of slow and high path planning speed of agricultural mobile robots are solved, and fast and economical path planning is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHEJIANG UNIV OF SCI & TECH
- Filing Date
- 2024-11-06
- Publication Date
- 2026-05-08
AI Technical Summary
In existing technologies, agricultural mobile robots suffer from slow speed and high path cost in path planning, and traditional algorithms struggle to balance global optimal solutions and fast planning.
The FA* algorithm based on grid search is adopted. The path cost of the first half and the second half is calculated separately by using a segmented heuristic function and cost calculation formula. Different weight parameters are used to accelerate path planning, and the path is smoothed by Savitzky-Golay filter.
It improves the speed of path planning, reduces the cost of the planned path, reduces the number of search nodes and time, and improves the efficiency of robot work.
Smart Images
Figure CN121995908A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of path planning technology, and in particular to a path planning method, apparatus, medium and product for a mobile robot. Background Technology
[0002] The primary application of mobile robots in agricultural production is monitoring plant growth. However, the complex environment, diverse plant species, and various obstacles in agricultural production can severely disrupt the normal operation of mobile robots. Currently, the main problems faced by agricultural mobile robots in path planning are: traditional sampling-based path planning algorithms, while finding globally optimal solutions, are slow, especially in large areas or regions with numerous obstacles, thus reducing overall robot efficiency. Traditional search-based path planning algorithms, while focusing on efficiency and quickly finding effective paths, may not yield globally optimal solutions and are prone to getting trapped in local minima, resulting in higher path costs. Summary of the Invention
[0003] The purpose of this application is to provide a path planning method, device, medium, and product for mobile robots, so as to solve the problem that existing technologies cannot simultaneously achieve both path planning speed and path cost.
[0004] To achieve the above objectives, this application provides the following solution:
[0005] Firstly, this application provides a path planning method for a mobile robot, comprising:
[0006] Obtain the coordinates of all nodes in the activity area of the mobile robot; the nodes include: start point, end point, and feasible nodes; the start point is the initial position of the mobile robot; the end point is the target position of the mobile robot; and the feasible nodes are nodes without obstacles.
[0007] The starting point is determined as the center node;
[0008] All feasible nodes in the nine-square grid centered on the central node are identified as nodes to be planned.
[0009] For any node to be planned, the road segment where the node is located is determined based on the coordinates of the node, the coordinates of the starting point, and the coordinates of the ending point.
[0010] If the road segment where the current node to be planned is located is the first half, then the total cost of the current node to be planned is calculated using the first cost calculation formula.
[0011] If the road segment where the current node to be planned is located is the latter half, then the total cost of the current node to be planned is calculated using the second cost calculation formula.
[0012] The central node is updated to the node to be planned with the minimum total cost, and the program returns "all feasible nodes in the 3x3 grid centered on the central node are determined as nodes to be planned". This continues until an endpoint is found in the 3x3 grid centered on the central node. Then, all central nodes and endpoints are connected in sequence to obtain the planned path of the mobile robot.
[0013] Optionally, based on the coordinates of the current node to be planned, the coordinates of the starting point, and the coordinates of the ending point, the road segment where the current node to be planned is located is determined, including:
[0014] Determine the Euclidean distance from the starting point to the ending point based on the coordinates of the starting point and the ending point.
[0015] The baseline distance is determined by half of the Euclidean distance from the starting point to the ending point.
[0016] Based on the coordinates of the current node to be planned and the coordinates of the endpoint, determine the Euclidean distance from the current node to be planned to the endpoint;
[0017] Determine whether the Euclidean distance from the current node to be planned to the endpoint is less than the reference distance;
[0018] If so, then the road segment where the current node to be planned is located will be determined as the second half;
[0019] If not, the road segment where the current node to be planned is located will be designated as the first half.
[0020] Optionally, the first cost calculation formula includes:
[0021] f(n)=g(n)+w1(n)×d2+w2(n)×d2;
[0022] Where f(n) is the total cost of the current node to be planned; g(n) is the actual cost from the starting point to the current node to be planned; w1(n) is the first dynamic weighting parameter of the current node to be planned; d2 is the Euclidean distance from the current node to the end point; and w2(n) is the second dynamic weighting parameter of the current node to be planned.
[0023] Optionally, the second cost calculation formula includes:
[0024]
[0025] Where θ is the angle between the vector pointing from the current node to be planned to the endpoint and the x-axis of the rectangular coordinates in the activity area of the mobile robot.
[0026] Optionally, the calculation formula for the first dynamic weighted parameter of the current node to be planned includes:
[0027]
[0028] Where e is the natural constant; d3 is the Euclidean distance from the starting point to the ending point.
[0029] Optionally, the calculation formula for the second dynamic weighted parameter of the current node to be planned includes:
[0030]
[0031] Where d1 is the Euclidean distance from the starting point to the current node to be planned.
[0032] Optionally, before obtaining the coordinates of all nodes in the mobile robot's activity area, the following steps are also included:
[0033] The activity area is divided into multiple grids of the same size;
[0034] The coordinates of the center point of each grid are determined as the coordinates of each node.
[0035] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the path planning method for a mobile robot as described in any of the preceding claims.
[0036] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the path planning method for the mobile robot described in any of the preceding claims.
[0037] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the path planning method for a mobile robot as described in any of the preceding claims.
[0038] According to the specific embodiments provided in this application, the following technical effects are disclosed:
[0039] This application discloses a path planning method, apparatus, medium, and product for a mobile robot. First, the coordinates of all nodes in the mobile robot's activity area are obtained. Second, the starting point is determined as the center node. All feasible nodes in a 3x3 grid centered on the center node are determined as nodes to be planned. For any current node to be planned, the road segment where the current node is located is determined based on the coordinates of the current node, the coordinates of the starting point, and the coordinates of the ending point. Then, if the road segment where the current node is located is the first half, the total cost of the current node is calculated using a first cost calculation formula; if the road segment where the current node is located is the second half, the total cost of the current node is calculated using a second cost calculation formula. Finally, the center node is updated to the node with the minimum total cost, and the process of "determining all feasible nodes in a 3x3 grid centered on the center node as nodes to be planned" is returned until an ending point exists in the 3x3 grid centered on the center node. Then, all center nodes and ending points are connected sequentially to obtain the planned path of the mobile robot. This application divides path planning into a first half and a second half, and uses different cost calculation formulas to select new center nodes for the first half and the second half, thereby improving the speed of path planning while reducing the cost of the planned path. Attached Figure Description
[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0041] Figure 1 A schematic flowchart of a path planning method for a mobile robot provided in an embodiment of this application;
[0042] Figure 2 For the A* algorithm in the ground Figure 1 A schematic diagram of the path planning on the map;
[0043] Figure 3 The method of this application is in the local Figure 1 A schematic diagram of the path planning on the map;
[0044] Figure 4 For the A* algorithm in the ground Figure 2 A schematic diagram of the first path planning on the path;
[0045] Figure 5 The method of this application is in the local Figure 2 A schematic diagram of the first path planning on the path;
[0046] Figure 6 For the A* algorithm in the ground Figure 2A schematic diagram of the second path planning on the above;
[0047] Figure 7 The method of this application is in the local Figure 2 A schematic diagram of the second path planning on the above;
[0048] Figure 8 For the A* algorithm in the ground Figure 3 A schematic diagram of the first path planning on the path;
[0049] Figure 9 The method of this application is in the local Figure 3 A schematic diagram of the first path planning on the path;
[0050] Figure 10 For the A* algorithm in the ground Figure 3 A schematic diagram of the second path planning on the above;
[0051] Figure 11 The method of this application is in the local Figure 3 A schematic diagram of the second path planning on the above;
[0052] Figure 12 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0053] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0054] The purpose of this application is to provide a path planning method, apparatus, medium, and product for mobile robots, which aims to improve the speed of path planning while reducing the cost of the planned path.
[0055] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0056] In one exemplary embodiment, such as Figure 1 As shown, the path planning method for the mobile robot in this embodiment includes:
[0057] Step 1: Obtain the coordinates of all nodes in the mobile robot's activity area.
[0058] The nodes include: a start point, an end point, and feasible nodes. The start point is the initial position of the mobile robot. The end point is the target position of the mobile robot, and feasible nodes are nodes without obstacles.
[0059] Specifically, mobile robots include, but are not limited to, the SCOUT 2.0 robot.
[0060] As an optional implementation, before step 1, the following steps are also included:
[0061] Step 01: Divide the activity area into multiple grids of the same size.
[0062] Step 02: Determine the coordinates of the center point of each grid as the coordinates of each node.
[0063] Step 2: Determine the starting point as the center node.
[0064] Step 3: Determine all feasible nodes in the nine-square grid centered on the central node as nodes to be planned.
[0065] Step 4: For any current node to be planned, determine the road segment where the current node to be planned is located based on the coordinates of the current node to be planned, the coordinates of the starting point, and the coordinates of the ending point.
[0066] As an optional implementation, step 4, which determines the road segment where the current node to be planned is located based on the coordinates of the current node to be planned, the coordinates of the starting point, and the coordinates of the ending point, includes:
[0067] Step 41: Determine the Euclidean distance between the starting point and the ending point based on the coordinates of the starting point and the ending point.
[0068] Step 42: Determine half of the Euclidean distance from the starting point to the ending point as the baseline distance.
[0069] Step 43: Determine the Euclidean distance from the current node to be planned to the endpoint based on the coordinates of the current node to be planned and the coordinates of the endpoint.
[0070] Step 44: Determine whether the Euclidean distance from the current node to be planned to the endpoint is less than the baseline distance.
[0071] Step 45: If so, then the road segment where the current node to be planned is located is determined as the second half.
[0072] Step 46: If not, then the road segment where the current node to be planned is located is determined as the first half.
[0073] Step 5: If the road segment where the current node to be planned is located is the first half, then use the first cost calculation formula to calculate the total cost of the current node to be planned.
[0074] As an optional implementation method, the first cost calculation formula includes:
[0075] f(n)=g(n)+w1(n)×d2+w2(n)×d2;
[0076] Where f(n) is the total cost of the current node to be planned; g(n) is the actual cost from the starting point to the current node to be planned; w1(n) is the first dynamic weighting parameter of the current node to be planned; d2 is the Euclidean distance from the current node to the end point; and w2(n) is the second dynamic weighting parameter of the current node to be planned.
[0077] Step 6: If the road segment where the current node to be planned is located is the latter half, then use the second cost calculation formula to calculate the total cost of the current node to be planned.
[0078] As an optional implementation, the second cost calculation formula includes:
[0079]
[0080] Where θ is the angle between the vector pointing from the current node to be planned to the endpoint and the x-axis of the rectangular coordinates in the activity area of the mobile robot.
[0081] As an optional implementation method, the calculation formula for the first dynamic weighted parameter of the node to be planned includes:
[0082]
[0083] Where e is the natural constant; d3 is the Euclidean distance from the starting point to the ending point.
[0084] As an optional implementation method, the calculation formula for the second dynamic weighted parameter of the node to be planned includes:
[0085]
[0086] Where d1 is the Euclidean distance from the starting point to the current node to be planned.
[0087] Step 7: Update the center node to the node to be planned with the minimum total cost, and return to step 3. Continue until there is an endpoint in the 3x3 grid centered on the center node, and connect all the center nodes and endpoints in sequence to obtain the planned path of the mobile robot.
[0088] Specifically, the path planning method for the mobile robot in this application is implemented using the FA* algorithm, an improvement on the A* algorithm for path planning based on grid search. The heuristic function of the A* algorithm provides guidance to the current node to be planned towards the destination. However, if there is a long obstacle in front of the destination, it will search evenly to both sides, and may even produce nodes searching in the opposite direction to the destination. Therefore, this application proposes a new piecewise heuristic function with two stages. If the Euclidean distance from the current node to the destination is greater than or equal to half the Euclidean distance from the starting point to the destination, then the Euclidean distance from the target node to the destination is used as the heuristic function. When the Euclidean distance from the current node to the destination is less than half the Euclidean distance from the starting point to the destination, it indicates that the current node is relatively close to the destination. At this time, the angle between the vector pointing from the current node to the destination and the x-axis of the rectangular coordinates in the mobile robot's activity area is calculated, forming a sector area search, which can accelerate the search of the current node towards the destination. The specific formula is shown below:
[0089] f(n)=g(n)+w1(n)×h(n)+w2(n)×v(n).
[0090]
[0091] Where h(n) is the heuristic function of the A* algorithm, h(n) = d2; v(n) is the piecewise heuristic function of the FA* algorithm; r is the radius of the sector formed by the angle between the vector pointing to the endpoint of the current node to be planned and the x-axis of the rectangular coordinates in the activity area of the mobile robot, r = d2, and the angle ranges from 0 degrees to 180 degrees. The piecewise heuristic function can add a new guiding force to the FA* algorithm and greatly reduce the time and number of node searches.
[0092] By transforming the above formulas, we can obtain the first cost calculation formula and the second cost calculation formula.
[0093] During path planning, d3 remains constant throughout the planning process, while d1 gradually increases and d2 gradually decreases as the nodes continuously search towards the destination. Gradually increase As the number of ions gradually decreases, w1(n) gradually decreases, the effect of h(n) slowly diminishes, w2(n) gradually increases, the effect of v(n) slowly increases, and 1+e -xAs a smooth and bounded monotonically increasing function, the weights w1(n) and w2(n) will only fluctuate between 0 and 1 during path planning. The designed piecewise heuristic function is expected to provide an initial acceleration and traction in the early stages, while the closer to the destination, the greater the guiding force provided by the proposed piecewise heuristic function v(n) is expected to be. It will generate an angled sector area search in the latter half of the path, which will greatly reduce the search time and the number of nodes, and find an effective path more quickly. To smooth the planned path and prevent too many turning points from affecting the robot's normal movement, a Savitzky-Golay filter is finally used to smooth the path.
[0094] To verify the method of this application, land was also utilized. Figure 1 -land Figure 3 The effectiveness of the proposed FA* algorithm is verified through simulation experiments of the corresponding activity areas. The algorithm is compared with the original A* algorithm in terms of metrics such as the number of search nodes, search time, and path length. In the simulation experiments, each algorithm is tested 50 times on each map, and the average value is taken as the experimental result.
[0095] (1) In the local area Figure 1 The experiment uses different start points, end points, and obstacles; smoothing is not used in this part. For example... Figure 2 and Figure 3 shown, ground Figure 1 In the table, blue dots represent the starting point, yellow dots represent the ending point, red lines represent the final planned path, and blue filled areas represent the nodes searched by the algorithm. Table 1 records the performance of the A* algorithm and the algorithm proposed in this application. Figure 1 The comparison results show that the A* algorithm plans a path length of 127.1959595, a search time of 5.570 seconds, and searches for 734 nodes. In contrast, the FA* algorithm plans a path length of 127.1959595, a search time of 2.463 seconds, and searches for 379 nodes. It can be seen that compared to the original A* algorithm, the FA* algorithm proposed in this application significantly reduces the search time and the number of nodes searched.
[0096] Table 1. Different algorithms in the local area Figure 1 Comparison table of experimental results
[0097] algorithm Number of iterations Path length Search time Number of search nodes A* Algorithm 50 127.195959 5.570 seconds 734 FA* algorithm 50 127.195959 2.463 seconds 379
[0098] (2) In the local area Figure 2 Two sets of starting and ending points were designed to investigate the efficiency of different algorithms when the starting and ending points were located in narrow regions. Figures 4-7 shown, ground Figure 2In the two sets of experimental graphs, blue dots represent the starting point, yellow dots represent the ending point, red lines represent the final planned path, and blue filled areas represent the nodes searched by the algorithm. From Figures 4-7 As can be seen from the table, the A* algorithm requires more resources to search when the destination is located in a narrow region. Table 2 records the differences between the A* algorithm and the algorithm proposed in this application in terms of location. Figure 2 As shown in Table 2, the FA* algorithm still outperforms the A* algorithm in terms of search time and number of search nodes, but it reduces search cost more significantly when the endpoint is located in a narrow region.
[0099] Table 2. Different algorithms in the local area Figure 2 Comparison table of experimental results
[0100] algorithm Number of iterations Path length Search time Number of search nodes A* Algorithm 50 67.941125 2.923 seconds 489 FA* algorithm 50 67.941125 1.112 seconds 178 A* Algorithm 50 67.941125 1.105 seconds 146 FA* algorithm 50 67.941125 0.715 seconds 137
[0101] (3) Figures 8-11 shown, ground Figure 3 The starting and ending coordinates for the first path planning are (135.0, 45.0) and (20.0, 100.0), respectively. The starting and ending coordinates for the second path planning are (130.0, 30.0) and (20.0, 100.0), respectively. Figures 8-11 As can be seen, when the search area reaches an open area with fewer obstacles, the FA* algorithm will quickly search towards the destination, while the A* algorithm will continue to expand to both sides of invalid nodes during this process, thus reducing the planning efficiency of the algorithm. Table 3 records the performance of the A* algorithm and the algorithm proposed in this application in terms of local conditions. Figure 3 The comparison results in Table 3 show that, with the same planned path length, the FA* algorithm consumes significantly less time and has a much smaller number of search nodes.
[0102] Table 3. Different algorithms in the local area Figure 3 Comparison table of experimental results
[0103] algorithm Number of iterations Path length Search time Number of search nodes A* Algorithm 50 138.367532 4.917 seconds 588 FA* algorithm 50 138.367532 0.377 seconds 69 A* Algorithm 50 137.823376 6.258 seconds 633 FA* algorithm 50 137.823376 0.433 seconds 55
[0104] In one exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a path planning method for a mobile robot.
[0105] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements a path planning method for a mobile robot.
[0106] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements a path planning method for a mobile robot.
[0107] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 12 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a path planning method for a mobile robot.
[0108] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0109] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0110] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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), magnetic 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 take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0111] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0112] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this specification.
[0113] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A path planning method for a mobile robot, characterized in that, The path planning method for the mobile robot includes: Obtain the coordinates of all nodes in the activity area of the mobile robot; the nodes include: start point, end point, and feasible nodes; the start point is the initial position of the mobile robot; the end point is the target position of the mobile robot; and the feasible nodes are nodes without obstacles. The starting point is determined as the center node; All feasible nodes in the nine-square grid centered on the central node are identified as nodes to be planned. For any node to be planned, the road segment where the node is located is determined based on the coordinates of the node, the coordinates of the starting point, and the coordinates of the ending point. If the road segment where the current node to be planned is located is the first half, then the total cost of the current node to be planned is calculated using the first cost calculation formula. If the road segment where the current node to be planned is located is the latter half, then the total cost of the current node to be planned is calculated using the second cost calculation formula. The central node is updated to the node to be planned with the minimum total cost, and the program returns "all feasible nodes in the 3x3 grid centered on the central node are determined as nodes to be planned" until an endpoint is found in the 3x3 grid centered on the central node. Then, all central nodes and endpoints are connected in sequence to obtain the planned path of the mobile robot.
2. The path planning method for a mobile robot according to claim 1, characterized in that, Based on the coordinates of the current node to be planned, the coordinates of the starting point, and the coordinates of the ending point, determine the road segment where the current node to be planned is located, including: Determine the Euclidean distance from the starting point to the ending point based on the coordinates of the starting point and the ending point. The baseline distance is determined by half of the Euclidean distance from the starting point to the ending point. Based on the coordinates of the current node to be planned and the coordinates of the endpoint, determine the Euclidean distance from the current node to be planned to the endpoint; Determine whether the Euclidean distance from the current node to be planned to the endpoint is less than the reference distance; If so, then the road segment where the current node to be planned is located will be determined as the second half; If not, the road segment where the current node to be planned is located will be designated as the first half.
3. The path planning method for a mobile robot according to claim 1, characterized in that, The first cost calculation formula includes: f(n)=g(n)+w1(n)×d2+w2(n)×d2; Where f(n) is the total cost of the current node to be planned; g(n) is the actual cost from the starting point to the current node to be planned; w1(n) is the first dynamic weighting parameter of the current node to be planned; d2 is the Euclidean distance from the current node to the end point; and w2(n) is the second dynamic weighting parameter of the current node to be planned.
4. The path planning method for a mobile robot according to claim 3, characterized in that, The second cost calculation formula includes: Where θ is the angle between the vector pointing from the current node to be planned to the endpoint and the x-axis of the rectangular coordinates in the activity area of the mobile robot.
5. The path planning method for a mobile robot according to claim 4, characterized in that, The formula for calculating the first dynamic weighted parameter of the node to be planned includes: Where e is the natural constant; d3 is the Euclidean distance from the starting point to the ending point.
6. The path planning method for a mobile robot according to claim 5, characterized in that, The calculation formula for the second dynamic weighted parameter of the node to be planned includes: Where d1 is the Euclidean distance from the starting point to the current node to be planned.
7. The path planning method for a mobile robot according to claim 6, characterized in that, Before obtaining the coordinates of all nodes in the mobile robot's activity area, the following steps are also included: The activity area is divided into multiple grids of the same size; The coordinates of the center point of each grid are determined as the coordinates of each node.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the path planning method for the mobile robot according to any one of claims 1-7.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the path planning method for the mobile robot according to any one of claims 1-7.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the path planning method for the mobile robot according to any one of claims 1-7.