Robot path planning method and related equipment

Abstract

The invention discloses a robot path planning method and related equipment, and belongs to the technical field of robot path planing.The robot path planning method comprises the steps that firstly, a link network diagram capable of walking preliminarily is constructed through the method that a vertical line is made from the vertex of an obstacle to the boundary, then an improved Dijkstra algorithm is adopted to calculate a robot optimal movement path on the mobile network diagram, and the robot optimal movement path is obtained; the robot moving path is optimized by applying the ant colony algorithm for solving the minimum function value, the defects that the calculation complexity is high, directional guidance is lacked and the calculation amount is increased due to blind search are overcome, the path is optimized again through a triangular pruning method, the accuracy and efficiency of path planning are improved, and the path deviation risk is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of robot path planning, and specifically relates to a robot path planning method and related equipment. Background Art

[0002] The origin of intelligent mobile robot technology can be traced back to the mid-20th century. With the continuous development of computer science, sensor technology and artificial intelligence, robotics technology has gradually moved from the laboratory to practical applications. Intelligent mobile robots can autonomously navigate and perform tasks in unknown or dynamically changing environments, bringing revolutionary changes to many fields such as industry, agriculture, medical care, logistics, etc. Intelligent mobile robot technology has become a hot technology that countries at home and abroad are focusing on developing.

[0003] Navigation positioning and environment perception modeling technology are the core of intelligent mobile robot technology, involving key technologies such as multi-sensor fusion, autonomous positioning and path planning. As an important part of mobile robot autonomous navigation technology, in-depth research on path planning is of great significance.

[0004] Mobile robot path planning technology is an important research direction in the field of intelligent robots. It involves how to enable the robot to move from the starting point to the target point in an unknown or partially known environment, while avoiding obstacles and solving an optimal or nearly optimal path. Mobile robot path planning first models the environment, represents the actual physical environment with a mathematical model, and determines the initial position and target position of the robot. Then, through the path search algorithm, a collision-free path from the initial position to the target position is found in the environmental model. Finally, the initial path found is optimized, including reducing the path length and smoothing, reducing the number of turns and energy consumption of the mobile robot.

[0005] Path planning technology mainly involves environmental perception technology, map processing technology, search algorithms, path smoothing and optimization, dynamic planning technology, etc. Currently, the most widely used technology in mobile robots is map construction path planning technology, which is also a hot topic in current research. There are many map-building path planning technologies: the method based on topological maps (one-dimensional maps) uses nodes to represent key locations and edges to represent connection relationships. Path planning searches for paths between nodes in the topological map. However, it is difficult to define and extract key locations, and they are sensitive to environmental changes. Local changes may affect topological relationships and path planning; the method based on grid maps (two-dimensional maps) divides the environment into uniform grids and determines the grid status based on sensor information. However, the higher the resolution, the greater the amount of calculation and the update is not timely when the environment changes dynamically; the method based on visibility graphs solves path planning in dynamically changing environments, and requires frequent updates to reflect environmental changes, which may result in high computational costs and poor real-time performance. The visibility graph relies on the direct line of sight of the sensor. For obscured areas or obstacles, the visibility graph may not provide accurate information; the method based on Maklink graphs uses concave and convex polygons to represent obstacles and constructs a link network graph that can be initially walked, but the calculation method of Maklink graphs is too complicated. In complex environments or large-scale path planning problems, the amount of calculation will increase dramatically. Map construction and path planning technology is an important foundation for autonomous navigation of intelligent robots. It involves obtaining environmental data through sensors (such as lidar, cameras, etc.), and constructing raster maps, feature maps or topological maps after data fusion and processing. On this basis, path planning is performed using heuristic search, sampling-based algorithms, etc. to determine the best path from the starting position to the target position. In recent years, the application of new technologies such as deep learning and multi-sensor fusion has significantly improved the efficiency and accuracy of map construction and path planning, and can respond to changes in dynamic environments in real time, thereby enhancing the adaptability and safety of robots.

[0006] Commonly used algorithms for path planning include heuristic search algorithms (A* algorithm, Dijkstra algorithm, artificial potential field method, genetic algorithm), local path planning algorithms (RRT fast random tree algorithm, PRM probabilistic RoadMap algorithm), bionic intelligence algorithms (ant colony algorithm, particle swarm optimization algorithm), and logic planning algorithms (PROLOG logic programming language for rule reasoning and planning). Representative path planning algorithms include A* algorithm, genetic algorithm, artificial potential field method and other algorithms. The combination of traditional technology and intelligent technology has opened up a new direction for mobile robot path planning technology. However, the A* algorithm consumes a lot of memory and its performance depends on heuristic functions. If it is not designed properly, it may easily lead to incorrect search directions or low efficiency. The genetic algorithm has high computational complexity and lacks directional guidance. It needs to traverse all nodes. In complex map environments, the time complexity increases dramatically with the number of nodes, and blind search will increase the amount of calculation. The artificial potential field method is prone to falling into local optimality. Under complex obstacle layouts, the robot may be trapped by local minimum points, and even the target may be unreachable due to potential field construction or obstacle distribution. General algorithms can only solve better paths, but cannot obtain the optimal solution to obtain the optimal path. Summary of the invention The present invention provides a robot path planning method and related equipment, which solve the problems of high computational complexity, lack of directional guidance, and increased computational complexity caused by blind search in existing robot path planning methods.

[0007] To achieve the above object, the present invention provides the following technical solutions: A robot path planning method, comprising: Construct vertex boundary vertical lines according to the working environment of the mobile robot, obtain free link lines based on the fixed-point boundary vertical lines, and construct the robot mobile network diagram through the free link lines; The improved Dijkstra algorithm is used to calculate a robot moving path on the mobile network graph; The ant colony algorithm for solving the minimum function value is used to optimize the robot's moving path and obtain the secondary optimized path; The secondary optimization path is optimized again using triangle pruning to obtain the global optimal path for the robot's movement.

[0008] Preferably, the step of constructing a vertical line of a vertex boundary according to the working environment of the mobile robot is specifically as follows: Use concave and convex polygons to represent obstacles. Draw a perpendicular line from each polygon vertex along the direction parallel to the Y axis to the X axis. Start from the end point in two directions and stop when encountering an obstacle.

[0009] Preferably, the specific steps of constructing the robot mobile network diagram through free link lines are: The midpoint, starting point and end point of each link line are connected in pairs to form an auxiliary line set. The auxiliary lines that pass through free links and obstacles are deleted to obtain the robot movement network diagram.

[0010] Preferably, the improved Dijkstra algorithm is specifically: Select a node closest to the source point from the unvisited set as the current node, then traverse each adjacent node of the current node to find a shorter path, update the distance of the adjacent node, and mark the current node as visited. Repeat the above process until all nodes have been visited or a specific target node has been visited. Once the target node has been visited, the algorithm reconstructs the shortest path from the source point to the target node by backtracking the predecessor node of each node.

[0011] Preferably, the ant colony algorithm for solving the minimum function value is applied to optimize the robot movement path, and the steps of obtaining the secondary optimization path are specifically as follows: The path length from a certain point on the free link line passing through the starting point to the end point is taken as the objective function, and a certain point on the free link line passed through is taken as the independent variable. The free link line passed through is normalized, and the specific position on the free link line is denormalized according to the result of the normalization processing in each calculation. Through multiple iterations, the optimal path for the mobile robot to switch between areas is obtained.

[0012] Preferably, the step of optimizing the secondary optimized path again by using triangle pruning is specifically: replacing a plurality of broken line segments with a straight line segment that does not pass through obstacles.

[0013] A robot path planning system, comprising: Network diagram construction module: used to construct vertex boundary vertical lines according to the working environment of the mobile robot, obtain free link lines based on the fixed-point boundary vertical lines, and construct the robot mobile network diagram through the free link lines; The first optimization module: used to calculate a robot moving path on the mobile network graph using an improved Dijkstra algorithm; The second optimization module is used to optimize the robot's moving path by applying the ant colony algorithm for solving the minimum function value to obtain a secondary optimization path; The third optimization module is used to optimize the secondary optimization path again by using triangle pruning to obtain the global optimal path for the robot to move.

[0014] Preferably, in the first optimization module, the improved Dijkstra algorithm is specifically: Select a node closest to the source point from the unvisited set as the current node, then traverse each adjacent node of the current node to find a shorter path, update the distance of the adjacent node, and mark the current node as visited. Repeat the above process until all nodes have been visited or a specific target node has been visited. Once the target node has been visited, the algorithm reconstructs the shortest path from the source point to the target node by backtracking the predecessor node of each node.

[0015] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a robot path planning method are implemented.

[0016] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a robot path planning method are implemented.

[0017] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides a robot path planning method, which first constructs a preliminary walkable link network diagram by drawing perpendicular lines from the vertices of obstacles to the boundaries, thereby avoiding the large amount of calculation caused by the resolution of the grid map and the problem of untimely updating when the environment changes dynamically. Then, an improved Dijkstra algorithm is used to calculate a better path for the robot to move on the mobile network diagram, and then an ant colony algorithm for solving the minimum function value is applied to optimize the robot's moving path, thereby overcoming the defects of high computational complexity, lack of directional guidance, and increased computational complexity caused by blind search. Finally, the path is optimized again by the triangle pruning method, thereby improving the accuracy and efficiency of path planning and reducing unnecessary waste of computing resources and the risk of path deviation.

[0018] Furthermore, the construction of the vertex boundary vertical line partition map allows the robot to move in the environment without being restricted by traditional static maps, and can flexibly respond to changes in the environment. Using polygons to represent obstacles in the environment makes the map more realistic and accurate, effectively avoiding robot collisions. Constructing a free link line network diagram ensures that the robot has a feasible path throughout the environment, reducing dead ends and blind spots in path planning. This method is easier to understand and simpler to calculate. This method can better handle complex map representations and dynamic environmental changes, providing more accurate and efficient path planning results.

[0019] Furthermore, the improved Dijstra algorithm has higher computational efficiency than the traditional algorithm and can calculate an optimal path more quickly. The improved algorithm has stronger adaptability and can cope with complex and changing environments, ensuring the reliability and accuracy of the path calculation results, making the preliminary path calculation more in line with actual needs.

[0020] Furthermore, by using an improved ant colony algorithm to simulate the foraging process of ants, a preliminary calculated path is carefully optimized to ensure the best actual operation performance of the path. The algorithm can dynamically adjust the path to adapt to environmental changes, improve the real-time and effectiveness of path planning, and find the best performing path through careful optimization to ensure that the robot can complete the task with the optimal path.

[0021] Furthermore, the triangle pruning method is used to identify and replace the broken line path segments formed by multiple continuous nodes in the path with direct straight line segments, which effectively performs geometric optimization on the shortest path obtained by the minimum function value method, improves the path planning efficiency, simplifies the path, and avoids local optimality. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a robot path planning method; Figure 2 Schematic diagram for modeling the two-dimensional working environment space of a mobile robot; Figure 3 A network diagram for a mobile robot moving freely; Figure 4 This is a schematic diagram of the shortest path obtained using the improved Dijstra algorithm; Figure 5 Schematic diagram of the global shortest path for the ant colony algorithm to find the minimum function value; Figure 6 The relationship between the optimal solution of path planning and the number of iterations of the ant colony algorithm; Figure 7 Schematic diagram of the optimal solution for triangle pruning geometry optimization; Figure 8 This is a block diagram of a robot path planning system of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0026] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0027] like Figure 1 As shown, the present invention provides a robot path planning method, comprising: Construct vertex boundary vertical lines according to the working environment of the mobile robot, obtain free link lines based on the fixed-point boundary vertical lines, and construct the robot mobile network diagram through the free link lines; The improved Dijkstra algorithm is used to calculate a robot moving path on the mobile network graph; The ant colony algorithm for solving the minimum function value is used to optimize the robot's moving path and obtain the secondary optimized path; The secondary optimization path is optimized again using triangle pruning to obtain the global optimal path for the robot's movement.

[0028] The detailed steps are: First, the experimental environment space needs to be modeled. There are multiple obstacles in the environment, and each obstacle is defined by several vertices. The coordinates of these vertices can be used to represent the shape and position of the obstacle. For each vertex of each obstacle, a vertical line is constructed along the direction parallel to the Y axis (that is, perpendicular to the X axis). These vertical lines start from each vertex and extend in two opposite directions until they encounter an obstacle. These vertical lines are free link lines. The midpoint line is defined as the line connecting the midpoints of each free link line, and it does not intersect with the free link line or obstacles. A network diagram is constructed in which the robot can move freely. The two-dimensional map constructed by the vertex boundary perpendicular line method of the present invention can process complex concave and convex polygons, and can quickly and effectively plan paths in complex environmental models and avoid obstacles. Preliminary path calculation, using the improved Dijkstra algorithm to calculate an optimal path on a freely moving network graph; Path optimization, using the ant colony algorithm to solve the minimum function value to perform secondary optimization on this optimal path; Global path optimization uses triangle pruning optimization to select the global optimal path as the robot's final motion path.

[0029] In a possible implementation, an ant colony algorithm that solves the minimum function value is used to find the best path in a two-dimensional plane, which can handle complex concave and convex polygonal obstacles. The focus is on establishing an environmental model and constructing a vertex boundary vertical line partition map as follows: First, set the boundary of the working environment, and process the input obstacle information to organize it into valid polygon vertex information. If the obstacle vertex is outside the boundary, readjust the obstacle vertex information by calculating its intersection with the boundary, and delete duplicate vertices and obstacles that are not within the boundary.

[0030] Next, a perpendicular line to the X-axis is drawn from each polygon vertex along a direction parallel to the Y-axis and starts from two directions and stops after encountering an obstacle, i.e., a free link line. In addition, obstacles of other shapes can be represented by a concave-convex polygon with the smallest area covered by it.

[0031] Then, the midpoint, starting point and end point of each link line are connected in pairs to form an auxiliary line set, and the auxiliary lines that pass through the free link lines and obstacles are deleted to obtain a network diagram in which the robot can move freely.

[0032] In a freely movable network graph, the improved Dijkstra algorithm is used to preliminarily solve the shortest path from the source point to the destination point, and at the same time, the link lines that the preliminary path needs to pass through are obtained.

[0033] The improved Dijkstra algorithm is as follows: First, select a node from the unvisited set that is closest to the source node as the current node; then, traverse each adjacent node of the current node to find a shorter path, update the distance of the adjacent node, and mark the current node as visited; repeat this process until all nodes have been visited or a specific target node has been visited. Once the target node has been visited, the algorithm reconstructs the shortest path from the source node to the target node by backtracking the predecessor nodes of each node.

[0034] The total distance and total path are calculated using the ant colony algorithm for solving the minimum function value. The total path length and specific path are obtained by inputting the proportion of each segment on the path and the connection line information. The specific algorithm is as follows: The path set consisting of this optimal path is defined as:

[0035] Where: V 1 ,V 2 ,…,V d are the nodes that the path passes through in sequence, and the corresponding free link lines are

[0036] S and T are the starting point and the end point respectively; They are The other points on the link line are represented as:

[0037] Among them: other points on the link line are expressed as proportional parameters Function, scale parameter Used to indicate a link line All parameters in the path Combine into a group This parameter group corresponds to a specific path. The optimized solution of the ant colony algorithm for finding the minimum function value.

[0038] In a possible implementation, a secondary optimization search is performed on the optimal path using an improved ant colony algorithm (ant colony algorithm for solving the minimum function value), and the specific steps are as follows: First, we represent the path and construct the function: let the starting point coordinates be The end point coordinates are , the goal is to construct the minimum path from the starting point to the end point to achieve path optimization. In order to facilitate calculation, the present invention divides this path into three segments, and the starting points of the three paths are , , The end points are: ; and the direction vectors of the three paths are , ,in .

[0039] Then the sections on the three paths can be expressed as: , , ,in .

[0040] Then we can express the distance from the starting point to the end point:

[0041] by is the independent variable, then:

[0042] After expressing the distance from the starting point to the end point, the above-mentioned optimized ant colony algorithm for solving the minimum function value is used to iterate the distance expression multiple times within the feasible range to find the optimal value (the minimum value of the function and the independent variable when taking the minimum value) ).

[0043] For each iteration, we get The solution and its corresponding path length The value of , let the maximum value be , the minimum value is , then the pheromone is defined as

[0044] In the iterative process of using the ant colony algorithm to solve the minimum function value, the expression of its heuristic function is:

[0045] Then the state transition probability expression of the ant is

[0046]

[0047] The probability values ​​for local search and global search are The median value ; The smaller the function value, the greater the corresponding transition probability, and the more local search is required; the larger the function value, the smaller the corresponding transition probability, and the more global search is required.

[0048] During the iterative optimization process of the ant colony, if the value of the state transition probability is greater than , then the ant colony performs local search, and the search range for t in the local process is ,in is the number of iterations; if the value of the state transition probability is less than , then the ant colony conducts a global search, and the search range for t during the global search is , until you get Corresponding to a certain value, the path length Minimum. During the iterative solution process, if , then let If it appears , then let After that, it is substituted into the next iterative calculation. Until the number of iterations is exhausted or the optimal value in the iterative process does not change for several consecutive generations, the minimum function value of the ant colony algorithm based on solving the minimum function value can be obtained.

[0049] The triangular pruning method can be used to effectively perform geometric optimization on the shortest path obtained by the minimum function value method. The following are the specific steps of triangular pruning optimization: If three consecutive nodes in the path Form a triangle, and from arrive The direct path does not pass through any obstacles, then we can use and Replace the straight line segment between The entire path segment.

[0050] Based on the above technical solution, the robot path planning method based on the ant colony algorithm for solving the minimum function value and the vertex boundary perpendicular line method of the present invention solves the optimization problem of robot path planning in a complex environment by performing environmental modeling, preliminary path calculation, path optimization and global path selection. By combining the improved Dijkstra algorithm and the ant colony algorithm for solving the minimum function value, the calculation and secondary optimization of this optimal path are realized, thereby improving the efficiency and effect of path planning.

[0051] Another embodiment of the present invention provides a robot path planning, see Figure 1 , the method comprises the following steps: 101: Environmental modeling: Construct a vertex boundary vertical line partition diagram based on the working environment of the mobile robot. First, draw a perpendicular line from each polygon vertex along the direction parallel to the Y axis to the X axis. Start from the end point in both directions and stop when encountering an obstacle. Obstacles of other shapes can be represented by concave and convex polygons with the smallest covered area, and a network diagram in which the robot can move freely is constructed through free link lines.

[0052] 102: Preliminary path calculation: Use the improved Dijkstra algorithm to calculate an optimal path on a freely moving network graph.

[0053] 103: Path optimization: Apply the ant colony algorithm for solving the minimum function value to perform secondary optimization on this optimal path, and take the optimal path as the global optimal path.

[0054] In specific implementation, the second optimization may also adopt other intelligent algorithms such as genetic algorithms, particle swarm optimization algorithms, artificial bee colony algorithms, etc., which is not limited in the embodiment of the present invention.

[0055] Specifically, the first optimization in step 102 and step 103 mainly focuses on the preliminary selection of free link lines and the improvement of the quality of the path. In this stage, the basic properties and characteristics of the path are optimized by evaluating and adjusting the selection of free link lines. In the second optimization process, the focus is on in-depth analysis and further refinement of the first optimization results to eliminate any potential suboptimal paths and ensure that the final path can reach the theoretical global optimum.

[0056] Through these two rounds of optimization, we ensured the global optimization of the path planning problem under the vertex boundary vertical line partition diagram, so that theoretically 100% global optimization can be achieved.

[0057] The specific experimental contents are: The operating environment of this path planning simulation experiment is: Windows 11 64bit; Matlab R2017b; processor Intel (R) Core (TM) i5-3230M; main frequency 2.6GHz; memory 4GB. The mobile robot path planning method based on ant colony algorithm and ox-plow partition map is divided into the following steps: 1. Spatial Modeling of the Working Environment of Mobile Intelligent Robots At present, the commonly used environmental modeling methods for mobile robots include grid maps, free space diagrams, scale-space mapping, topological maps, etc. However, the trade-off between the accuracy and real-time performance of these models, the difficulty of map updating, overfitting in complex environments, scale problems, and the complexity of maintenance and management are all key issues that affect the practical application effects of these modeling methods. The embodiment of the present invention uses a link graph method based on a vertex boundary vertical line partition graph to establish an environmental model.

[0058] The core concept of the vertex-boundary-perpendicular method is to represent obstacles in the working environment as polygons and partition the environment by perpendicular lines parallel to the Y axis. This method can effectively handle complex concave and convex polygonal obstacles.

[0059] Imagine a mobile robot moving on a two-dimensional plane. The obstacles around it are represented by polygons. Obstacles of other shapes can be represented by concave and convex polygons that cover the smallest area. These polygons are perpendicular to the ground. To ensure the safety of the robot, the robot is regarded as a point, and each obstacle is represented by its vertex, such as The whole environment consists of Description. Here, represents the ith obstacle, and WSB represents the area without obstacles, such as Figure 2 shown.

[0060] In the process of map modeling, the obstacle information is input It is sorted into valid polygon vertex information. If the obstacle vertex is outside the boundary, the vertex information is readjusted by calculating its intersection with the boundary, and duplicate vertices and obstacles that are not within the boundary are deleted.

[0061] Draw a perpendicular line from each polygon vertex along the direction parallel to the Y axis to the X axis, and start from the end point in both directions and stop when encountering an obstacle to obtain the link line of each vertex. Generate the midpoint of the link line, and use the starting point, end point and all midpoints as inspection points. By combining inspection points in pairs, generating lines between all inspection points, and deleting lines that intersect with obstacles and link lines, a grid is formed in which the robot can move freely. Figure 3 As shown in the figure, the dotted lines represent free link lines, and the solid grids are feasible paths for the robot. This process ensures that the generated path is optimal by comprehensively considering the boundary conditions of obstacles and the effectiveness of the path.

[0062] The entire system integrates multiple modules such as environment modeling, auxiliary line generation, midpoint generation and connection. Through data sharing and mutual calling between each module, comprehensive modeling of the working environment is achieved. The connection generation process of the starting point, end point and midpoint ensures that obstacles are avoided during the path planning process and the optimal path is generated.

[0063] 2. Establishing the Improved Dijstra Algorithm The traditional Dijstra algorithm achieves the shortest path through a one-way search, but it is less efficient in large-scale graphs. The present invention uses an improved Dijstra algorithm for preliminary path planning, which helps to achieve global optimal path planning. The specific operation steps are: first, select a node closest to the source point from the unvisited set as the current node; then, traverse each adjacent node of the current node to find a shorter path, update the distance of the adjacent node, and mark the current node as visited; repeat this process until all nodes have been visited or a specific target node has been visited. Once the target node is visited, the algorithm reconstructs the shortest path from the source point to the target node by backtracking the predecessor nodes of each node, such as Figure 4 shown.

[0064] 3. Establish an ant colony algorithm to solve the minimum function value for secondary optimization The relationship between the optimal solution of path planning and the number of iterations of the ant colony algorithm is as follows: Figure 6 As shown; The Ant Colony Algorithm (ACO) is an optimization method that simulates the foraging behavior of ants and is widely used in path planning. It guides the search process through the pheromone mechanism and continuously adjusts the path selection to find the optimal solution. The algorithm can handle multi-objective optimization, dynamic environment adaptation, and has good robustness. The key lies in the scientific pheromone update strategy and adaptive adjustment. In addition, the algorithm can be parallelized and is suitable for solving large-scale problems.

[0065] The ant colony algorithm for solving the minimum function value is used to perform secondary optimization on this optimal path. The specific steps are as follows: First, the length of each free link line is normalized, and the normalization parameter How to represent each point on the link line They are the two endpoints of the link line. Multiple sets of parameters are randomly generated , and calculate the cost of the corresponding path (such as total distance or other evaluation criteria). Then, based on the quality of the path cost, the pheromone concentration of each path of the corresponding link line is updated. A good path obtains more pheromones, which makes it more attractive for subsequent searches. When ants choose a path, they not only choose randomly, but also consider the concentration of pheromones to increase the probability of choosing a high-quality path. Finally, the above process is iterated until the preset termination condition is reached (such as reaching a fixed number of iterations or the path cost is no longer significantly improved).

[0066] Finally, the optimized solution The optimal path can be determined, which corresponds to the best movement route of the robot from point S to point T, such as Figure 5 shown.

[0067] 4. Use the triangle pruning method to geometrically optimize the shortest path obtained by the minimum function value method If three consecutive nodes in the path Form a triangle, and from arrive The direct path does not pass through any obstacles, then we can use and Replace the straight line segment between The entire path segment, such as Figure 7 shown.

[0068] like Figure 8 As shown, the present invention also provides a robot path planning system, comprising: Network diagram construction module: used to construct vertex boundary vertical lines according to the working environment of the mobile robot, obtain free link lines based on the fixed-point boundary vertical lines, and construct the robot mobile network diagram through the free link lines; The first optimization module: used to calculate a robot moving path on the mobile network graph using an improved Dijkstra algorithm; The second optimization module is used to optimize the robot's moving path by applying the ant colony algorithm for solving the minimum function value to obtain a secondary optimization path; The third optimization module is used to optimize the secondary optimization path again by using triangle pruning to obtain the global optimal path for the robot to move.

[0069] A terminal device is provided in one embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0070] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0071] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0072] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0073] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0074] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

Claims

1. A robot path planning method, characterized in that: include: Construct vertex boundary vertical lines according to the working environment of the mobile robot, obtain free link lines based on the fixed-point boundary vertical lines, and construct the robot mobile network diagram through the free link lines; The improved Dijkstra algorithm is used to calculate a robot moving path on the mobile network graph; The ant colony algorithm for solving the minimum function value is used to optimize the robot's moving path and obtain the secondary optimized path; The secondary optimization path is optimized again using triangle pruning to obtain the global optimal path for the robot to move.

2. A robot path planning method according to claim 1, characterized in that: The specific steps of constructing the vertical line of the vertex boundary according to the working environment of the mobile robot are: Use concave and convex polygons to represent obstacles. Draw a perpendicular line from each polygon vertex along the direction parallel to the Y axis to the X axis. Start from the end point in two directions and stop when encountering an obstacle.

3. A robot path planning method according to claim 1, characterized in that: The specific steps to construct a robot mobile network diagram through free link lines are: The midpoint, starting point and end point of each link line are connected in pairs to form an auxiliary line set. The auxiliary lines that pass through free links and obstacles are deleted to obtain the robot movement network diagram.

4. A robot path planning method according to claim 1, characterized in that: The improved Dijkstra algorithm is specifically as follows: Select a node from the unvisited set that is closest to the source point as the current node. Then, traverse each adjacent node of the current node to find a shorter path, update the distance of the adjacent node, and mark the current node as visited. Repeat the above process until all nodes have been visited or a specific target node has been visited. Once the target node has been visited, the algorithm reconstructs the shortest path from the source point to the target node by backtracking the predecessor node of each node.

5. A robot path planning method according to claim 1, characterized in that: The ant colony algorithm for solving the minimum function value is used to optimize the robot's moving path. The specific steps for obtaining the secondary optimization path are as follows: The path length from a certain point on the free link line passing through the starting point to the end point is taken as the objective function, and a certain point on the free link line passed through is taken as the independent variable. The free link line passed through is normalized, and the specific position on the free link line is denormalized according to the result of the normalization processing in each calculation. Through multiple iterations, the optimal path for the mobile robot to switch between areas is obtained.

6. A robot path planning method according to claim 1, characterized in that: The specific steps of optimizing the secondary optimization path again by using triangle pruning are: replacing multiple broken line segments with a straight line segment that does not pass through obstacles.

7. A robot path planning system, characterized in that: include: Network diagram construction module: used to construct vertex boundary vertical lines according to the working environment of the mobile robot, obtain free link lines based on the fixed-point boundary vertical lines, and construct the robot mobile network diagram through the free link lines; The first optimization module: used to calculate a robot moving path on the mobile network graph using an improved Dijkstra algorithm; The second optimization module is used to optimize the robot's moving path by applying the ant colony algorithm for solving the minimum function value to obtain a secondary optimization path; The third optimization module is used to optimize the secondary optimization path again by using triangle pruning to obtain the global optimal path for the robot to move.

8. A robot path planning system according to claim 7, characterized in that: In the first optimization module, the improved Dijkstra algorithm is specifically: Select a node from the unvisited set that is closest to the source point as the current node. Then, traverse each adjacent node of the current node to find a shorter path, update the distance of the adjacent node, and mark the current node as visited. Repeat the above process until all nodes have been visited or a specific target node has been visited. Once the target node has been visited, the algorithm reconstructs the shortest path from the source point to the target node by backtracking the predecessor node of each node.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the robot path planning method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a robot path planning method as claimed in any one of claims 1 to 7 are implemented.

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