Global path planning method for mobile robots based on improved potential field ant colony algorithm

Abstract

The present invention provides a global path planning method for a mobile robot based on an improved potential field ant colony algorithm. The method includes: S1, constructing a grid map; S2 includes: S21, initializing a taboo table; S22, obtaining comprehensive heuristic information of each ant's neighborhood node, the comprehensive heuristic information is obtained according to the potential field force, the distance between the neighborhood node and the starting node and the target node, and the obstacle coefficient between the nodes; obtaining the selection probability of the neighborhood node, selecting the next node, and updating the taboo table; S23, obtaining the shortest path of this iteration; S24, updating the path pheromone concentration, and updating the pheromone volatility coefficient; S3, outputting the shortest path in T iterations. Introducing the potential field force into the comprehensive heuristic information improves the search efficiency, introducing the distance from the starting node to the neighborhood node and the distance from the neighborhood node to the target node into the heuristic information improves the global search capability, and adopting the dynamic pheromone volatility coefficient improves the convergence speed.

Description

[0001] Technology Neighborhood

[0002] The present invention relates to the technical field of artificial intelligence path planning, and in particular to a global path planning method for a mobile robot based on an improved potential field ant colony algorithm. Background Art

[0003] Mobile robot path planning is a research hotspot in the robot community. Its purpose is to search for an optimal or near-optimal safe and collision-free path from the start node to the target node in an unknown or known environment according to specified optimization rules (such as shortest distance, shortest time, minimum work cost, etc.). At present, a lot of research has been done on mobile robot path planning algorithms at home and abroad, which can be roughly divided into global path planning algorithms and local path planning algorithms. Global path planning algorithms include A* algorithm, Dijkstra algorithm, ant colony algorithm, bat algorithm, particle swarm algorithm, etc. Local path planning algorithms include DWA (Dynamic Window Approach) algorithm, artificial potential field method, TEB (Timed Elastic Band) algorithm.

[0004] In the path planning of mobile robots in a two-dimensional static environment, the use of basic ant colony algorithm to find the optimal path will have problems such as low search efficiency, easy to fall into local optimality, slow convergence speed, etc. In response to the above problems, Liu Jiaqi et al. (Liu Jiaqi, Wang Taihua, Dong Zheng. Mobile robot path planning based on improved ant colony algorithm [J]. Sensors and Microsystems, 2022, 41 (5): 140-144.) proposed an improved ant colony algorithm. By modifying the distance heuristic function and establishing an angle index heuristic function to judge the size of the node angle, the search efficiency of the algorithm was improved. The adaptive pheromone volatility coefficient and the pheromone concentration reward for some of the better paths were used to improve the convergence speed of the algorithm. However, the algorithm has many parameters, high algorithm complexity, and is easy to cause parameter sensitivity. The algorithm is not robust enough. The Chinese patent (CN114610045A) discloses a robot path planning method and system based on an improved ant colony algorithm. First, combined with the distance factor, a preset formula is used to calculate the initial pheromone. Secondly, an improved pheromone update strategy is used to update the pheromone. Finally, the pheromone divergence factor is adjusted. This patent improves the convergence speed and reduces the running time of the algorithm. However, the initial pheromone update method makes the initial pheromone concentration difference not large enough. In the early stage of the algorithm, it has little effect on improving the algorithm search efficiency. Summary of the invention

[0005] The present invention aims to solve the problems of local optimum and slow convergence speed in traditional ant colony algorithm, and provides a global path planning method for mobile robots based on improved potential field ant colony algorithm.

[0006] In order to achieve the above-mentioned purpose of the present invention, according to the first aspect of the present invention, the present invention provides a global path planning method for a mobile robot based on an improved potential field ant colony algorithm, including: step S1, constructing a grid map, selecting a starting node and a target node in the grid map, initializing the ant colony algorithm, and initializing the number of iterations t to 1; step S2, entering the tth iterative search, specifically including: step S21, placing M ants to the starting node, where M is a positive integer, and initializing a taboo table; step S22, each ant selects the next node according to the following steps until it reaches the target node or a deadlock state occurs: obtaining comprehensive heuristic information of each ant's neighborhood nodes, and the comprehensive heuristic information is respectively compared with the starting node according to the potential field forces of the ant's current node and the neighborhood nodes. The distance from the target node and the obstacle coefficient between the ant's current node and the neighboring nodes are obtained; the selection probability of the neighboring nodes is obtained based on the comprehensive heuristic information and the pheromone concentration of the section from the ant's current node to the neighboring nodes, and each ant selects the next node based on the selection probability of all neighboring nodes, and updates the taboo table; step S23, after all ants have completed the search, the shortest path to the target node in this iteration is obtained; step S24, using the pheromone volatility coefficient to update the pheromone concentration of each ant's path that reaches the target node; update the pheromone volatility coefficient; step S3, if t<T, then set t=t+1, return to execute step S2, if t≥T, output the shortest path to the target node from T iterations, T represents the preset maximum number of iterations.

[0007] In order to achieve the above-mentioned purpose of the present invention, based on the same inventive concept, the second aspect of the present invention provides a global path planning device for a mobile robot based on an improved potential field ant colony algorithm, including: an initialization module, constructing a grid map, selecting a starting node and a target node in the grid map, initializing the ant colony algorithm, and initializing the number of iterations t to 1; a t-th iteration search module, entering the t-th iteration search, specifically executing: placing M ants to the starting node, where M is a positive integer, and initializing a taboo table; each ant selects the next node according to the following steps until it reaches the target node: obtaining comprehensive heuristic information of each ant's neighborhood nodes, wherein the comprehensive heuristic information is based on the potential field forces of the ant's current node, the distances of the neighborhood nodes from the starting node and the target node respectively, and the ant's current node Obtain the obstacle coefficient between the point and the neighboring nodes; obtain the selection probability of the neighboring nodes based on the comprehensive heuristic information and the pheromone concentration of the road section from the current node of the ant to the neighboring nodes, and each ant selects the next node based on the selection probability of all neighboring nodes, and updates the taboo table; after all ants have completed the search, obtain the shortest path to the target node in this iteration; use the pheromone volatility coefficient to update the pheromone concentration of the path passed by each ant that arrives at the target node; after all ants arrive at the target node, obtain the shortest path of this iteration and update the pheromone volatility coefficient; iterative search termination judgment module, if t<T, then set t=t+1, return to the tth iteration search module, if t≥T, output the shortest path to the target node from T iterations, T represents the preset maximum number of iterations.

[0008] In order to achieve the above-mentioned objectives of the present invention, based on the same inventive concept, the third aspect of the present invention provides a mobile robot, on which is provided the mobile robot global path planning device based on the improved potential field ant colony algorithm as described in the second aspect of the present invention, and the mobile robot global path planning device based on the improved potential field ant colony algorithm plans a global path for the mobile robot.

[0009] The above technical solution: The present invention introduces the potential field force of the artificial potential field method into the comprehensive heuristic information of the neighborhood nodes of the ant colony algorithm, which can improve the search efficiency of the ant colony algorithm, and introduces the distance from the starting node to the neighborhood node and the distance from the neighborhood node to the target node into the comprehensive heuristic information of the neighborhood node, which can improve the global search ability of the ant colony algorithm, and uses the dynamic pheromone volatilization coefficient when updating the pheromone concentration, which can improve the convergence speed of the ant colony algorithm and plan a better global path for the mobile robot more quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a flow chart of a global path planning method for a mobile robot based on an improved potential field ant colony algorithm in Example 1 of the present invention;

[0011] Figure 2is a schematic diagram of neighborhood node distribution in Example 1 of the present invention;

[0012] Figure 3 It is a schematic diagram of an algorithm flow in an application scenario of Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0013] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0014] In the description of the present invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or node relationship are based on the orientation or node relationship shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0015] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "connection" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal connection between two elements. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0016] Example 1

[0017] This embodiment discloses a global path planning method for a mobile robot based on an improved potential field ant colony algorithm. Figure 1 As shown, including:

[0018] Step S1, construct a grid map, select a starting node and a target node in the grid map, initialize the ant colony algorithm, and initialize the number of iterations t to 1.

[0019] Preferably, the grid map is obtained by modeling the environment map through the grid method. Initializing the ant colony algorithm includes initializing the number of ants M, initializing the grid information concentration in the grid map, and presetting the first parameter γ, the maximum number of iterations T, the information heuristic factor α, the expected heuristic factor β, the initial value of the pheromone volatility coefficient and the initial value of the number of iterations t. The initial value of the number of iterations t is set to 1. These preset values ​​can be set according to experience.

[0020] Step S2, entering the tth iteration search, specifically includes:

[0021] Step S21, placing M ants to the starting node, where M is a positive integer, and initializing a taboo table, which is used to store information about the nodes that a single ant has passed through in each iterative search;

[0022] Step S22: Each ant selects the next node according to the following steps until it reaches the target node or a deadlock occurs:

[0023] Step S221, obtaining comprehensive heuristic information of each ant's neighborhood node, the comprehensive heuristic information is obtained according to the potential force of the ant's current node, the distances between the neighborhood node and the start node and the target node, and the obstacle coefficient between the ant's current node and the neighborhood node.

[0024] Preferably, in step S22, Figure 2 As shown in the figure, each ant's neighborhood node includes 24 nodes in 16 directions centered on the ant's current node. The angles of the 16 directions with 0° as the reference are: In the grid map, the ants in the traditional ant algorithm can only have 4 or 8 search directions at the current node, while this application sets 16 search directions for the current node. The ants can view 24 neighborhoods of the current node each time, so that before the ants choose the next node, the long-term vision range allows the ants to plan a better path to the target node. Figure 2 As shown, the central grid is used as the current node, and the 24 grids surrounding the central grid are used as 24 neighboring nodes. Figure 2 The transmission line from each current node is a search direction. Some neighboring nodes share a search direction. The neighboring nodes sharing the same search direction have the same angle with 0° as the reference direction.

[0025] In this embodiment, preferably, in step S22, the comprehensive heuristic information of the neighborhood nodes is obtained according to the following formula:

[0026]

[0027] Among them, η ij (t) represents the comprehensive heuristic information of the neighboring node j of the ant’s current node i in the tth iteration, η ij The larger the (t) is, the greater the possibility of selecting the neighboring node j; γ represents the first parameter, γ∈[0,1]; Pass(i,j) represents the obstacle coefficient between the ant's current node i and the neighboring node j; d aj (t) represents the distance between the starting node and the neighboring node j; represents the distance coefficient between the neighboring node j and the target node, d jg(t) represents the distance between the neighbor node j and the target node, allowed k represents the set of neighboring nodes of ant k’s current node i, F tot represents the potential force of the ant's current node, θ j-diff It represents the angle difference between the neighborhood node j and the potential field. a represents the base of the exponential function, which is a constant and is generally greater than 1. It should be noted that all constants and variables in the above formula are dimensionless.

[0028] The above formula for obtaining the comprehensive heuristic information of the neighborhood nodes solves the problem that the traditional ant colony algorithm only considers the distance between the current node and the next node, which easily causes the algorithm to fall into a deadlock when selecting the next node, resulting in low search efficiency. Preferably, the present application considers the distance d between the starting point A and the neighborhood node j in the comprehensive heuristic information. aj , so that the current node i of the ant chooses a node far away from the starting point when selecting the next node; the distance d between the neighboring node j and the target point G is considered in the comprehensive heuristic information jg , so that when the current node i of the ant selects the next node, it tends to choose the neighboring node close to the target node as the next node. It also retains the influence factor of the traditional ant colony algorithm in the comprehensive heuristic information, and considers the distance d between the current node i and the neighboring node j in the comprehensive heuristic information. ij .

[0029] The formula for obtaining the comprehensive heuristic information of the above neighborhood nodes considers the influence of the artificial potential field force on the robot's motion in the comprehensive heuristic information. The obstacles in the environment produce a repulsive force F on the robot. rep , the target node in the environment generates gravitational force F on the current node robot att ,∑F rep It represents the sum of the repulsive forces of all obstacles in the environment on the current node robot. The combined force of repulsion and gravity guides the robot to move toward the target point. The potential field force on the robot at the current position is F tot =F att +∑F rep The angle difference θ between the potential field force and the neighboring nodes is also introduced j-diff , potential field force F tot The angle θ is taken as the reference direction of 0°, θ∈[0,2π], and the resultant force F acting on the grid position of the ant is calculated respectively. t o t The angle difference θ with the adjacent optional grid j-diff =|θ j -θ|,θ jIndicates the angle of the jth neighboring node with the 0° angle direction, j = 1, 2, 3, ..., 16. The artificial potential field method model is simple and practical. Combined with the heuristic information of the ant colony algorithm, it can effectively improve the search efficiency of the algorithm and increase the convergence speed of the algorithm.

[0030] In this embodiment, F att and F rep The algorithm for obtaining is preferably but not limited to that disclosed in the Chinese patent with publication number CN110928295A, which is the prior art and will not be described in detail here.

[0031] In this embodiment, the basic idea of ​​the artificial potential field method is to search for an optimal path without collision through the combined effect of the gravitational field of the target position and the repulsive field of the obstacle. In the virtual potential field, the farther the robot is from the target point, the greater the potential energy it receives, and the closer the robot is to the target point, the smaller the potential energy it receives, and the potential energy is zero when it reaches the target point; the closer the robot is to the obstacle, the greater the repulsive force, and vice versa. Preferably, the definition of the artificial potential field method is as follows:

[0032] If the robot's current position (i.e., current node position) is X = (x, y), the target node is X g =(x g ,y g ), then the gravitational potential field function is as follows:

[0033]

[0034] The gravitational potential field force is expressed as follows:

[0035] F att = -grad(U gra )=-λ(XX g )=λ(X g -X);

[0036] Where λ is the gravitational potential field constant.

[0037] The repulsive potential field function is shown as follows:

[0038]

[0039] The repulsive potential field force is expressed as follows:

[0040]

[0041] Where m is a repulsive field constant greater than zero, ρ is the distance from the robot to the obstacle, and ρ0 is the maximum operating range of the obstacle. Under the combined action of gravity and repulsion, the robot moves toward the target node and finally reaches the target node.

[0042] The above formula for obtaining comprehensive heuristic information of neighborhood nodes also fully considers the obstacles in the operating environment of the mobile robot, and judges whether there are obstacles between two nodes. If there are obstacles between the two nodes, the ant cannot reach it directly, otherwise the ant can reach it. Specifically, when there is no obstacle between the ant's current node i and the neighborhood node j, Pass(i,j)=1, and when there is an obstacle between the ant's current node i and the neighborhood node j, Pass(i,j)=0. This makes the optimal path planned for the mobile robot more practical.

[0043] Step S222, based on the comprehensive heuristic information and the pheromone concentration of the road section from the current node of the ant to the neighboring node, the selection probability of the neighboring node is obtained, and each ant selects the next node based on the selection probability of all neighboring nodes. Preferably, the next node is selected using the roulette method, and the taboo table is updated.

[0044] Preferably, the selection probability of the neighborhood node is calculated according to the following formula:

[0045]

[0046] in, represents the probability of ant k selecting neighboring node j as the next node in the tth iteration; τ ij (t) represents the pheromone concentration of the road segment (i, j) in the tth iteration; η ij (t) represents the comprehensive heuristic information of the neighboring node j of the ant’s current node i in the tth iteration; allowed k represents the set of neighboring nodes of ant k at the current node i; α represents the preset information heuristic factor; β represents the preset expected heuristic factor.

[0047] Step S23, after all ants have finished searching, the shortest path to the target node in this iteration is obtained. After all ants have finished searching, some ants will find the destination, and some ants will be deadlocked. When all ants in a generation have finished searching, the pheromone concentration increment of the path taken by the ants that have successfully reached the destination is updated.

[0048] Step S24, using the pheromone volatility coefficient to update the pheromone concentration of each ant that reaches the target node; updating the pheromone volatility coefficient.

[0049] Preferably, in step S24, the pheromone concentration of each section in the path passed by ant k is updated using the following formula:

[0050] τ ij (t+1)=(1-ρ(t))τ ij (t)+ρ(t)Δτ ij(t,t+1);

[0051] Among them, τ ij (t+1) represents the pheromone concentration left by M ants on the road section (i, j) after the t+1th iteration; τ ij (t) represents the pheromone concentration left by M ants on the road section (i, j) after the tth iteration; ρ(t) represents the pheromone volatility coefficient of the tth iteration, ρ(t)∈(0,1). The use of an adaptive pheromone volatility coefficient ρ(t) value can improve the convergence speed of the algorithm; Δτ ij (t, t+1) represents the increment of pheromone concentration left by ant k on road segment (i, j).

[0052] Q represents the pheromone intensity; L k Represents the total length of the path taken by ant k in this iteration.

[0053] Preferably, the pheromone volatility coefficient value is adaptively changed, and the update formula is:

[0054]

[0055] Among them, ρ(t+1) represents the pheromone volatility coefficient of the t+1th iteration; ρ(t) represents the pheromone volatility coefficient of the tth iteration, ρ(t)∈(0,1); L min (t-1) represents the shortest path obtained in the t-1th iteration; L min (t) represents the shortest path obtained in the tth iteration. By adaptively changing the volatility coefficient ρ, if the shortest distance of this generation is smaller than that of the previous generation, more pheromones need to be left for the next generation of ants. If the distance this time is larger than that of the previous generation, the pheromones of this path need to be diluted more.

[0056] Step S3, if t<T, set t=t+1, return to execute step S2, if t≥T, output the shortest path to the target node from T iterations, that is, select the shortest path with the shortest length from T iterations and output it, where T represents the preset maximum number of iterations.

[0057] In an application scenario of this embodiment, the algorithm flow is as follows: Figure 3 As shown, including:

[0058] Step 1: Use the grid method to establish an environmental map, initialize parameters, select the starting position A, the target position G, initialize the grid pheromone concentration, set parameters γ, the number of algorithm iterations N, the number of ants M, the information heuristic factor α, the expected heuristic factor β, and the pheromone volatility coefficient ρ;

[0059] Step 2: Ant path selection, place M ants at the starting point A, initialize the taboo table, and calculate the heuristic information value η of the neighborhood node ij ;

[0060] Step 3: Calculate the neighborhood node transfer probability Use the roulette method to select the next node and update the taboo table; use the roulette method to select the next node according to the following formula:

[0061] Step 4: After all ants complete one path search iteration, the pheromone concentration of the path that the ants that successfully reach the end point have taken is updated, and the pheromone concentration increment of the path that the ants that successfully reach the end point have taken is updated;

[0062] Step 5: End the search. Determine whether the number of iterations has reached the maximum number of iterations. If so, output the optimal path length. Otherwise, clear the taboo table, increase the number of iterations by 1, and go to Step 2 to execute in a loop until the maximum number of loops is reached. At this time, output the optimal path length and the algorithm ends.

[0063] The global path planning method for a mobile robot based on an improved potential field ant colony algorithm provided in this embodiment aims at the problems existing in the background technology. The traditional ant colony algorithm is prone to local optimality and slow convergence speed. A global path planning method based on a potential energy ant colony algorithm is proposed. Ants have 16 search directions and can view 24 neighborhoods of the current node. The resultant force of the artificial potential field method is introduced into the heuristic information of the ant colony algorithm. The angle between the resultant force of the artificial potential field and the 16 search directions is calculated and introduced into the heuristic information. By introducing the artificial potential field method, the search efficiency of the ant colony algorithm can be improved. The distance from the starting node to the next node and the distance from the next node to the target node are introduced into the heuristic information, which can improve the global search capability of the ant colony algorithm. In addition, the dynamic pheromone volatilization coefficient is used to improve the convergence speed of the ant colony algorithm.

[0064] Example 2

[0065] Based on the inventive concept of Example 1, this embodiment discloses a mobile robot global path planning device based on an improved potential field ant colony algorithm, comprising:

[0066] Initialization module, builds a grid map, selects the starting node and the target node in the grid map, initializes the ant colony algorithm, and initializes the number of iterations t to 1;

[0067] The t-th iteration search module enters the t-th iteration search, and specifically executes: M ants are placed at the starting node or a deadlock state occurs, where M is a positive integer, and the taboo table is initialized; each ant selects the next node according to the following steps until it reaches the target node: the comprehensive heuristic information of each ant's neighborhood nodes is obtained, and the comprehensive heuristic information is obtained according to the potential field force of the ant's current node, the distance between the neighborhood node and the starting node and the target node respectively, and the obstacle coefficient between the ant's current node and the neighborhood node; the selection probability of the neighborhood node is obtained based on the comprehensive heuristic information and the pheromone concentration of the section from the ant's current node to the neighborhood node, and each ant selects the next node based on the selection probability of all neighborhood nodes, and updates the taboo table; after all ants have searched, the shortest path to the target node in this iteration is obtained; the pheromone volatility coefficient is used to update the pheromone concentration of the path passed by each ant that reaches the target node; the pheromone volatility coefficient is updated;

[0068] Iterative search termination judgment module, if t<T, then set t=t+1 and return to the tth iterative search module. If t≥T, output the shortest path to the target node from T iterations, where T represents the preset maximum number of iterations.

[0069] Example 3

[0070] Based on the inventive concept of Example 1, this embodiment discloses a mobile robot, which is provided with a mobile robot global path planning device based on the improved potential field ant colony algorithm provided by Example 2, and the mobile robot global path planning device based on the improved potential field ant colony algorithm plans a global path for the mobile robot.

[0071] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0072] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A global path planning method for mobile robots based on an improved potential field ant colony algorithm, characterized in that: include: Step S1, constructing a grid map, selecting a starting node and a target node in the grid map, initializing the ant colony algorithm, and initializing the number of iterations t to 1; Step S2, entering the tth iteration search, specifically includes: Step S21, placing M ants to the starting node, where M is a positive integer, and initializing the taboo table; Step S22: Each ant selects the next node according to the following steps until it reaches the target node or a deadlock occurs: Obtaining comprehensive heuristic information of each ant's neighborhood node, wherein the comprehensive heuristic information is obtained according to the potential field force of the ant's current node, the distances of the neighborhood node from the start node and the target node respectively, and the obstacle coefficient between the ant's current node and the neighborhood node; Based on the comprehensive heuristic information and the pheromone concentration of the road section from the ant's current node to the neighboring node, the selection probability of the neighboring node is obtained. Each ant selects the next node based on the selection probability of all neighboring nodes and updates the taboo table. Step S23, after all ants have finished searching, the shortest path to the target node in this iteration is obtained; Step s24, using the pheromone volatility coefficient to update the pheromone concentration of each ant's path to the target node; updating the pheromone volatility coefficient; Step S3, if t<T, set t=t+1, return to execute step S2, if t≥T, output the shortest path to the target node from T iterations, T represents the preset maximum number of iterations; In step S22, the comprehensive heuristic information of the neighborhood nodes is obtained according to the following formula: Among them, η ij (t) represents the comprehensive heuristic information of the neighboring node j of the ant's current node i in the tth iteration; γ represents the first parameter, γ∈[0,1]; Pass(i,j) represents the obstacle coefficient between the ant's current node i and the neighboring node j; d aj (t) represents the distance between the starting node and the neighboring node j; represents the distance coefficient between the neighboring node j and the target node, d jg (t) represents the distance between the neighbor node j and the target node, allowed k represents the set of neighboring nodes of ant k’s current node i, F tot represents the potential force of the ant's current node, θ j-diff represents the angle difference between the neighborhood node j and the potential field; a represents the base of the exponential function, which is a number greater than 1; d ij Represents the distance between the current node i and the neighboring node j; The potential force F of the ant's current node tot =F att +∑F rep , where F att represents the gravitational force generated by the target node on the current node robot, ∑F rep Represents the sum of the repulsive forces of all obstacles in the environment on the current node robot; When there is no obstacle between the ant's current node i and the neighboring node j, Pass(i, j)=1; when there is an obstacle between the ant's current node i and the neighboring node j, Pass(i, j)=0.

2. The global path planning method for a mobile robot based on an improved potential field ant colony algorithm according to claim 1, characterized in that: In step S22, the neighborhood nodes of each ant include 24 nodes in 16 directions with the ant's current node as the center, and the angles of the 16 directions with 0° as the reference are:

3. The mobile robot global path planning method based on the improved potential field ant colony algorithm according to claim 1 or 2, characterized in that: The selection probability of the neighboring node is calculated according to the following formula: in, represents the probability of ant k selecting neighboring node j as the next node in the tth iteration; τ ij (t) represents the pheromone concentration of the road segment (i, j) in the tth iteration; η ij (t) represents the comprehensive heuristic information of the neighboring node j of the ant’s current node i in the tth iteration; allowed k represents the set of neighboring nodes of ant k at the current node i; α represents the preset information heuristic factor; β represents the preset expected heuristic factor.

4. The global path planning method for a mobile robot based on an improved potential field ant colony algorithm according to claim 1 or 2, characterized in that: In step S23, the pheromone concentration of each section of the path passed by ant k is updated using the following formula: t ij (t+1)=(1-ρ(t))τ ij (t)+ρ(t)Δτ ij (t,t+1); Among them, τ ij (t+1) represents the pheromone concentration left by M ants on the road section (i, j) after the t+1th iteration; τ ij (t) represents the pheromone concentration left by M ants on the road section (i, j) after the tth iteration; ρ(t) represents the pheromone volatility coefficient of the tth iteration, ρ(t)∈(0,1); Δτ ij (t, t+1) represents the increment of pheromone concentration left by ant k on road segment (i, j); Said Q represents the pheromone intensity; L k Represents the total length of the path taken by ant k in this iteration.

5. The global path planning method for a mobile robot based on an improved potential field ant colony algorithm according to claim 1 or 2, characterized in that: In step S24, the pheromone volatility coefficient is updated according to the following formula: Where, ρ(t+1) represents the pheromone volatility coefficient of the t+1th iteration; ρ(t) represents the pheromone volatility coefficient of the tth iteration, ρ(t)∈(0,1); L min (t-1) represents the shortest path obtained in the t-1th iteration; L min (t) represents the shortest path obtained in the tth iteration.

6. A mobile robot global path planning device based on an improved potential field ant colony algorithm, used to implement the mobile robot global path planning method based on an improved potential field ant colony algorithm as described in any one of claims 1 to 5, characterized in that: include: Initialization module, builds a grid map, selects the starting node and the target node in the grid map, initializes the ant colony algorithm, and initializes the number of iterations t to 1; The t-th iteration search module enters the t-th iteration search, and specifically executes: Place M ants to the starting node, where M is a positive integer, and initialize the taboo table; Each ant selects the next node according to the following steps until it reaches the target node or a deadlock occurs: Obtaining comprehensive heuristic information of each ant's neighborhood node, wherein the comprehensive heuristic information is obtained according to the potential field force of the ant's current node, the distances of the neighborhood node from the start node and the target node respectively, and the obstacle coefficient between the ant's current node and the neighborhood node; The selection probability of the neighboring nodes is obtained based on the comprehensive heuristic information and the pheromone concentration of the road section from the ant's current node to the neighboring nodes. Each ant selects the next node based on the selection probability of all neighboring nodes and updates the taboo table. After all ants have finished searching, they obtain the shortest path to the target node in this iteration; Use the pheromone volatility coefficient to update the pheromone concentration of each ant's path to the target node; update the pheromone volatility coefficient; Iterative search termination judgment module: if t<T, set t=t+1 and return to the t-th iterative search module; if t≥T, output the shortest path to the target node from T iterations, where T represents the preset maximum number of iterations; Obtain the comprehensive heuristic information of the neighborhood nodes according to the following formula: Among them, η ij (t) represents the comprehensive heuristic information of the neighboring node j of the ant's current node i in the tth iteration; γ represents the first parameter, γ∈[0,1]; Pass(i,j) represents the obstacle coefficient between the ant's current node i and the neighboring node j; d aj (t) represents the distance between the starting node and the neighboring node j; represents the distance coefficient between the neighboring node j and the target node, d jg (t) represents the distance between the neighbor node j and the target node, allowed k represents the set of neighboring nodes of ant k’s current node i, F tot represents the potential force of the ant's current node, θ j-diff represents the angle difference between the neighborhood node j and the potential field; a represents the base of the exponential function, which is a number greater than 1; d ij Represents the distance between the current node i and the neighboring node j; The potential force F of the ant's current node tot =F att +∑F rep , where F att represents the gravitational force generated by the target node on the current node robot, ∑F rep Represents the sum of the repulsive forces of all obstacles in the environment on the current node robot; When there is no obstacle between the ant's current node i and the neighboring node j, Pass(i, j)=1; when there is an obstacle between the ant's current node i and the neighboring node j, Pass(i, j)=0.

7. A mobile robot, characterized in that: The mobile robot is provided with the mobile robot global path planning device based on the improved potential field ant colony algorithm as described in claim 6, and the mobile robot global path planning device based on the improved potential field ant colony algorithm plans a global path for the mobile robot.

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