A path planning method based on D* artificial potential field fusion using ant colony algorithm

Abstract

This invention discloses a path planning method based on D* artificial potential field fusion using the ant colony algorithm, comprising: 1. acquiring the robot's own positioning information and surrounding environmental information; 2. constructing an environmental grid map using a grid method; 3. constructing an artificial potential field model based on the collected information and calculating the potential field difference of each node; 4. obtaining an initial path using D*, and comprehensively improving the ant colony algorithm parameters, combining the potential field difference of each node in the artificial potential field to perform path planning, smoothing the shortest path, and finally obtaining the optimal path. This invention constructs a grid map and completes path planning within the robot's working area through D* artificial potential field fusion based on the ant colony algorithm, thereby enabling the robot to move more accurately and efficiently during operation, providing a guarantee for robot navigation.

Description

Technical Field

[0001] This invention belongs to the field of road planning, specifically a path planning method based on ant colony algorithm and D* artificial potential field fusion. Background Technology

[0002] With the rapid development of technological innovation and high-end manufacturing, intelligent manufacturing and the robotics industry have received increasing attention from countries around the world. Developing the robotics industry has become an important means to maintain and regain competitive advantage in the manufacturing sector. Path planning plays a crucial role in autonomous driving technology, robot obstacle avoidance, and urban road planning. Specifically, the performance level of the path planning module is positively correlated with the path selection and smoothness of operation for each entity. The core of path planning refers to using path planning algorithms to generate feasible paths for entities based on environmental models. Currently, the environmental information encountered in the field of path planning is becoming increasingly complex and abundant. This requires path planning algorithms to have the ability to react quickly to complex environments and extract information, while ensuring the accuracy of path planning.

[0003] The D-Star algorithm is not suitable for high-dimensional spaces due to its large computational cost and problems such as low search efficiency, uneven path planning, susceptibility to local optima, and difficulty in finding the globally optimal path. The ant colony algorithm suffers from long search time, slow search speed, weak local search capability, slow convergence speed, susceptibility to local optima, and strong dependence on initial values, which limits its application scenarios. The artificial potential field method has the disadvantage of local minima and situations where the target is unreachable or the controlled object stops, thus it cannot guarantee that a solution can be found.

[0004] The commonly used path planning algorithms mentioned above generally suffer from the problem of getting trapped in local optima, thus failing to plan the entire path completely. They also have poor adaptability to complex scenarios and long search times, which can easily cause the robot to lag in path planning in the work area, or even make incorrect plans, making it unable to work safely and normally. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by proposing a path planning method based on ant colony algorithm and D* artificial potential field fusion. This method aims to complete path planning within the robot's work area, enabling the robot to move more accurately and efficiently during operation and providing assurance for robot navigation.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0007] The path planning method based on ant colony algorithm and D* artificial potential field fusion of the present invention is characterized by the following steps:

[0008] Step 1: After the robot enters the rectangular working area, it uses its own Beidou navigation module to acquire a high-precision map of the working area. A two-dimensional rectangular coordinate system OXY is established, with the upper left vertex of the rectangular working area as the origin O, and the two sides adjacent to the origin O as the x-axis and y-axis, respectively. In the rectangular coordinate system OXY, the rectangular working area is divided into a grid map with dimensions M×N and a grid side length of a. Among them, S n The grid map H represents M×N The upper coordinate is (X n ,Y n The state of the nth grid point, if Sn＝0 , indicating that the nth grid point is a candidate point, if S n =1 indicates that the nth grid point is an obstacle point, and M represents the grid map H. M×N The horizontal grid number, N represents the vertical grid number;

[0009] Calculate the Cartesian coordinates (X, X, Y) of the nth grid point using equation (1). n ,Y n ):

[0010]

[0011] In equation (1), mod represents the remainder function, and ceil represents the minimum integer function;

[0012] With the robot in the grid map H M×N The current grid point in the middle is the starting point S. R The grid point where the robot's destination is located is denoted as the endpoint S. G Let the m-th obstacle point be S. P_m ;

[0013] Step 2, Parameter Initialization:

[0014] Step 2.1: Use the D-Star algorithm to select all candidate points within the raster map to obtain the initial path L0 = {L 01 ,L 02 ,...,L 0n} and its length D0, where L 01 L represents the first path node in the initial path. 0n Let D0 represent the nth path node in the initial path, and D0 be the sum of the Euclidean distances between all adjacent path nodes.

[0015] Step 2.2: Define the rectangular working area as an artificial potential field, and initialize the gain coefficient m1 of the gravitational potential field function, the gain coefficient m2 of the potential field function, and the range d0 of the obstacle repulsive force of the artificial potential field.

[0016] Calculate the starting point S using equation (2) R With the endpoint S G gravitational potential difference U att (S R ,S G ):

[0017]

[0018] In equation (2), d(S) R ,S G (S is the starting point) R With the endpoint S G The Euclidean distance between them;

[0019] Calculate the starting point S using equation (4) R With obstacle point S P_m The repulsive potential field difference U rep (S R ,S P_m ):

[0020]

[0021] In equation (3), d(S) R ,S P_m (S is the starting point) R With the m-th obstacle point S P_m The Euclidean distance between them;

[0022] Using equation (4), we can obtain the robot's position at the starting point S. R The overall potential field U T (S R ):

[0023] U T (S R )=U att (S R ,S G )+U rep (S R ,S P_m (4)

[0024] Step 23: Use equation (5) to obtain the Nth... c Any candidate point S during the next foraging process I and its adjacent candidate points S J The pheromone τ between IJ (N c ):

[0025]

[0026] In equation (8), ω and ω′ are fixed coefficients, and 0 < ω′ < ω; U T(S J ) is a candidate point S I The overall potential field, U T (S J ) represents the adjacent candidate point S J The overall potential field;

[0027] Step 2.4: Define and initialize the current iteration number N. c =0;

[0028] Define the number of ants in the ant colony search mechanism as N. ant The current ant's sequence number is k, its pheromone concentration volatility coefficient is ρ, its heuristic factor is η, and its maximum number of foraging attempts is N. gen ;

[0029] Let L k Let D represent the path of the k-th ant. k This represents the path length of the k-th ant;

[0030] The tabu list contains candidate nodes that each ant has already visited;

[0031] Let the current node of the k-th ant be... L ki It is the i-th path node of the k-th ant;

[0032] Step 3: Use the fusion ant colony algorithm to obtain the optimal path;

[0033] Step 3.1, set N ant All ants were placed at the starting point S. R ;

[0034] Step 3.2, N c +1 is assigned to N c ;

[0035] Step 3.3: Initialize k = 1;

[0036] Step 3.4: Initialize i = 1;

[0037] Step 3.5, Let the starting point S R For L ki ;

[0038] Step 3.6: Assign i+1 to i;

[0039] Step 3.7: Calculate the Nth number using equation (6). c During the next foraging process, the kth ant is at the current node. Select the next node probability Thus, the Nth c Current node during the next foraging process The probability of reaching each adjacent node;

[0040]

[0041] In equation (6), τ UV (N c ) is the Nth c Current node during the next foraging process With the next node The pheromones between them, α is the importance factor of information, β is the importance factor of the heuristic function, and allowed k For the k-th ant (excluding nodes in the taboo list) at the current node The set of neighboring candidate nodes For allowed k Any node in the set; τ UW (N c ) is the Nth c Current node during the next foraging process With allowed k any node in the set The pheromones between them;

[0042] In the Nth c Current node during the next foraging process The Nth node is randomly determined from the probabilities of reaching each of its neighboring nodes. c During the next foraging process, the i-th path node L of the k-th ant is... ki Let the node with the highest probability be the (i+1)th path node L. k(i+1) ;

[0043] If the Nth c During the next foraging process, the kth ant is at the current node. If the pheromone value of all nodes adjacent to it is 0, then... Set it as an obstacle node, and assign i-1 to i, let L ki for Otherwise, let L k(i+1) for and path node L ki After adding to the taboo list, update the path length D. k ;

[0044] Step 3.8, return to step 3.6, until the Nth... c During the next foraging process, the kth ant reaches the destination S. G until;

[0045] Step 3.9: Use equation (7) to obtain the Nth... c Candidate point S during the next foraging process I and its adjacent candidate points SJ The pheromone released by the kth ant

[0046]

[0047] In equation (7), Q is a constant;

[0048] Step 3.10: After assigning k+1 to k, if k≤N ant If yes, proceed to step 3.4; otherwise, it means the Nth step has been obtained. c Record the paths of all ants and their corresponding path lengths during the next foraging process, clear the taboo list, and proceed to step 3.11;

[0049] Step 3.11: Calculate the Nth digit using equation (8). c During this foraging process, all ants released pheromones Δτ. IJ (N c ):

[0050]

[0051] Step 3.12: Use equation (9) to obtain the Nth... c Candidate point S during +1 foraging process I and its adjacent candidate points S J The pheromone τ of all the ants IJ (N c +1):

[0052] τ IJ (N c +1)=(1-ρ)·τ IJ (N c )+Δτ IJ (N c (9)

[0053] Step 3.13, if N c ≥N gen This means that the Nth digit is obtained. gen The paths of all ants during this foraging process are L1, L2, ..., L k ,..., and their corresponding path lengths D1, D2, ..., D k ,..., Then select the path with the shortest path length as the optimal path and proceed to step 4; otherwise, proceed to step 3.2.

[0054] Step 4: After smoothing the optimal path, the best path for robot navigation is obtained.

[0055] The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the path planning method, and the processor is configured to execute the program stored in the memory.

[0056] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program is executed by a processor to perform the steps of the path planning method.

[0057] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0058] 1. This invention utilizes the artificial potential field method to construct a grid map. The artificial potential field method comprehensively considers the distance factor and smooth path control problem in traditional algorithms, overcoming the limitation of traditional path planning which takes distance as the single objective. The grid map simplifies environmental elements and is easier to construct than the coordinate map in traditional path planning, thus enabling more effective extraction of the information required for path planning.

[0059] 2. This invention integrates the ant colony algorithm and the artificial potential field algorithm, using the artificial potential field as the initial parameter of the pheromone, which improves the planning speed of the path planning method and overcomes the problems of weak local search ability and slow convergence speed of the traditional ant colony algorithm, thereby improving the search rate and real-time performance of the algorithm.

[0060] 3. This invention combines D* with ant colony algorithm, comprehensively considers the initial path obtained by D*, and corrects and improves the relevant parameters of ant colony algorithm. Compared with D*, which is prone to getting trapped in local optima and has difficulty in finding the global optimal path, the ant colony algorithm can learn how to get out of local stable points, thus ensuring the reliability of path planning in the face of complex environments. Attached Figure Description

[0061] Figure 1 This invention provides a raster map with M=6 and N=6 as an example.

[0062] Figure 2 This is a diagram of the robot path planning system of the present invention;

[0063] Figure 3 This is a diagram showing the positional relationship between a candidate point and its adjacent candidate points in this invention.

[0064] Figure 4 This is a flowchart illustrating the ant colony algorithm of this invention. Detailed Implementation

[0065] In this embodiment, a path planning method based on ant colony optimization and D* artificial potential field fusion constructs a grid map using an artificial potential field method, initializes the path using D*, and obtains the optimal path using an ant colony optimization algorithm. Simultaneously, the communication framework is optimized to reduce power consumption, enabling the robot to plan its walking path more accurately and efficiently during operation. Specifically, the method includes the following steps:

[0066] Step 1: After the robot enters the rectangular working area, it uses its own Beidou navigation module to acquire a high-precision map of the working area. A two-dimensional Cartesian coordinate system OXY is established, with the upper left vertex of the rectangular working area as the origin O, and the two sides adjacent to the origin O as the x-axis and y-axis, respectively. In the Cartesian coordinate system OXY, the rectangular working area is divided into a grid map with dimensions M×N and a grid side length of a. Among them, S n H represents a raster map M×N The upper coordinate is (X n ,Y n The state of the nth grid point, if S n =0, indicating that the nth grid point is a candidate point, if S n =1 indicates that the nth grid point is an obstacle point, and M represents the grid map H. M×N The horizontal grid number is M, and the vertical grid number is N; for example, a grid map with M=6 and N=6 is shown below. Figure 1 As shown.

[0067] Calculate the Cartesian coordinates (X, X, Y) of the nth grid point using equation (1). n ,Y n ):

[0068]

[0069] In equation (1), mod represents the remainder function, and ceil represents the minimum integer function;

[0070] With the robot in the grid map H M×N The current grid point in the middle is the starting point S. R The grid point where the robot's destination is located is denoted as the endpoint S. G Let the m-th obstacle point be S. P_m .

[0071] Robot path planning system such as Figure 2 As shown, the system obtains location and environmental information through the data receiving unit, inputs it into the portable storage unit, and then uses the image processing module and the calculation acceleration module in the data processing unit to perform real-time path planning. Finally, the planned path is output to the display unit.

[0072] Step 2, Parameter Initialization:

[0073] Step 2.1: Use the D-Star algorithm to select all candidate points within the raster map to obtain the initial path L0 = {L 01 ,L 02 ,...,L 0n} and its length D0, where L 01 L represents the first path node in the initial path. 0n D0 represents the nth path node in the initial path, and D0 is the sum of the Euclidean distances of all adjacent path nodes.

[0074] Step 2.2: Define the rectangular working area as an artificial potential field, and initialize the gain coefficient m1 of the gravitational potential field function, the gain coefficient m2 of the potential field function, and the range d0 of the obstacle repulsive force.

[0075] Calculate the starting point S using equation (2) R With the endpoint S G gravitational potential difference U att (S R ,S G ):

[0076]

[0077] In equation (2), d(S) R ,S G (S is the starting point) R With the endpoint S G The Euclidean distance between them;

[0078] Calculate the starting point S using equation (4) R With obstacle point S P_m The repulsive potential field difference U rep (S R ,S P_m ):

[0079]

[0080] In equation (3), d(S) R ,S P_m (S is the starting point) R With the m-th obstacle point S P_m The Euclidean distance between them;

[0081] Using equation (4), we can obtain the robot's position at the starting point S. R The overall potential field U T (S R ):

[0082] U T (S R )=U att (S R ,SG )+U rep (S R ,S P_m (4)

[0083] Step 2.3: Use equation (5) to obtain the Nth... c Any candidate point S during the next foraging process I and its adjacent candidate points S J The pheromone τ between IJ (N c Within a 3x3 grid centered on a candidate point, the positional relationships between a candidate point and its neighboring candidate points are as follows: Figure 3 As shown, a given candidate point can consider eight neighboring candidate points, such as... Figure 3 As shown.

[0084]

[0085] In equation (8), ω and ω′ are fixed coefficients, and 0 < ω′ < ω; U T (S J ) is a candidate point S I The overall potential field, U T (S J ) represents the adjacent candidate point S J The overall potential field;

[0086] Step 2.4: Define and initialize the current iteration number N. c =0;

[0087] Define the number of ants in the ant colony search mechanism as N. ant The current ant's sequence number is k, its pheromone concentration volatility coefficient is ρ, its heuristic factor is η, and its maximum number of foraging attempts is N. gen ;

[0088] Let L k Let D represent the path of the k-th ant. k This represents the path length of the k-th ant;

[0089] The tabu list contains candidate nodes that each ant has already visited;

[0090] Let the current node of the k-th ant be... L ki It is the i-th path node of the k-th ant.

[0091] Step 3: Obtain the optimal path using the fusion ant colony algorithm; the flowchart of the fusion ant colony algorithm is as follows. Figure 4 As shown;

[0092] Step 3.1, set N ant All ants were placed at the starting point S. R ;

[0093] Step 3.2, N c +1 is assigned to N c ;

[0094] Step 3.3: Initialize k = 1;

[0095] Step 3.4: Initialize i = 1;

[0096] Step 3.5, Let the starting point S R For L ki ;

[0097] Step 3.6: Assign i+1 to i;

[0098] Step 3.7: Calculate the Nth number using equation (6). c During the next foraging process, the kth ant is at the current node. Select the next node probability

[0099]

[0100] In equation (6), τ UV (N c ) is the Nth c Current node during the next foraging process With the next node The pheromones between them, α is the importance factor of information, β is the importance factor of the heuristic function, and allowed k For the k-th ant (excluding nodes in the taboo list) at the current node The set of adjacent candidate nodes, For allowed k Any node in the set; τ UW (N c ) is the Nth c Current node during the next foraging process With allowed k any node in the set pheromones;

[0101] Based on the probability of the current node reaching each of its neighboring nodes Randomly determine the i-th path node L of the k-th ant ki Let the node with the highest probability be the (i+1)th path node L. k(i+1) ;

[0102] If the k-th ant is at the current node If the pheromone value of all nodes adjacent to it is 0, then... Set it as an obstacle node, and assign i-1 to i, let L ki for Otherwise, let L k(i+1) for and path node L ki After adding to the taboo list, update the path length D. k ;

[0103] Step 3.8, return to step 3.6, until the Nth... c During the next foraging process, the kth ant reaches the destination S. G until;

[0104] Step 3.9: Use equation (7) to obtain the Nth... c Candidate point S during the next foraging process I and its adjacent candidate points S J The pheromone released by the kth ant

[0105]

[0106] In equation (7), Q is a constant;

[0107] Step 3.10: After assigning k+1 to k, if k≤N ant If yes, proceed to step 3.4; otherwise, it means the Nth step has been obtained. c The paths of all ants during this foraging process and its corresponding path length Clear the taboo list and proceed to step 3.11;

[0108] Step 3.11: Calculate the Nth digit using equation (8). c During this foraging process, all ants released pheromones Δτ. IJ (N c ):

[0109]

[0110] Step 3.12: Use equation (9) to obtain the Nth... c Candidate point S during +1 foraging process I and its adjacent candidate points S J The pheromone τ of all the ants IJ (N c +1):

[0111] τ IJ (N c +1)=(1-ρ)·τ IJ (N c )+Δτ IJ (N c (9)

[0112] Step 3.13, if N c ≥N gen This means that the Nth digit is obtained. gen The paths of all ants during this foraging process and its corresponding path length Shortest path and its corresponding shortest path length Proceed to step 4; otherwise, proceed to step 3.2.

[0113] Step 4: After smoothing the shortest path, the optimal path for robot navigation is output.

[0114] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0115] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

Claims

1. An ant colony algorithm-based path planning method of D* artificial potential field fusion, characterized in that, Comprising the following steps: Step 1: After the robot enters the rectangular working area, it uses its own Beidou navigation module to acquire a high-precision map of the working area. A two-dimensional rectangular coordinate system OXY is established, with the upper left vertex of the rectangular working area as the origin O, and the two sides adjacent to the origin O as the x-axis and y-axis, respectively. Within this rectangular coordinate system OXY, the rectangular working area is divided into a grid map with dimensions M×N and a grid side length of a. Among them, S n The grid map H represents M×N The upper coordinate is (X n ,Y n The state of the nth grid point, if S n =0, indicating that the nth grid point is a candidate point, if S n =1 indicates that the nth grid point is an obstacle point, and M represents the grid map H. M×N The horizontal grid number, N represents the vertical grid number; The rectangular coordinate system coordinates (X n ,Y n ) corresponding to the nth grid point are calculated using formula (1): In formula (1), mod represents a remainder function, and ceil represents a minimum integer function; With the robot in the grid map H M×N The current grid point in the middle is the starting point S. R The grid point where the robot's destination is located is denoted as the endpoint S. G Let the m-th obstacle point be S. P_m ; Step 2, parameter initialization: Step 2.1: Use the D-Star algorithm to select all candidate points within the raster map to obtain the initial path L0 = {L 01 ,L 02 ,...,L 0n } and its length D0, where L 01 L represents the first path node in the initial path. 0n Let D0 represent the nth path node in the initial path, and D0 be the sum of the Euclidean distances between all adjacent path nodes. Step 2.2, defining the rectangular working area as an artificial potential field, initializing a gain coefficient m1 of a gravitational potential field function, a gain coefficient m2 of a potential field function, and an action range d0 of an obstacle repulsion of the artificial potential field; The difference U between the gravitational potential fields of the start point S R and the end point S G is calculated using formula (2) att (S R ,S G ): In formula (2), d(S R ,S G ) is the Euclidean distance between the starting point S R and the ending point S G . The start point S is calculated using formula (4) R The repulsive potential field difference U P_m of the obstacle point S rep (S R , S P_m ) In formula (3), d(S R ,S P_m ) is the Euclidean distance between the starting point S R and the mth obstacle point S P_m . The total potential field U of the robot at the start S is obtained using formula (4) R T (S R )​ U T (S R ) = U att (S R , S G ) + U rep (S R , S P_m ) (4) Step 23, obtaining the Nth candidate point S c between any one candidate point S I and its adjacent candidate point S J between any one candidate point S IJ (N c ): In formula (8), ω and ω' are fixed coefficients, and 0 < ω' < ω; U T (S J ) is the overall potential field of the candidate point S I ; U T (S J ) is the overall potential field of the adjacent candidate point S J ; Step 2.

4. Define and initialize the current iteration number N c = 0; The number of ants defining the ant colony search mechanism is N ant , the serial number of the current ant is k, the pheromone concentration volatilization coefficient is p, the heuristic factor is η, and the maximum foraging number is N gen ; Let L k represents the path of the kth ant, D k represents the path length of the kth ant; The tabu table contains candidate nodes that have been passed by each ant; Let the current node of the kth ant be L ki is the ith path node of the kth ant; Step 3, obtaining an optimal path by using a fusion ant colony algorithm; Step 3.1, place the ants at the start S ant Only ants are placed at the start S R ; Step 3.2, assign +1 to N c +1 to N c ; Step 3.3, initializing k = 1; Step 3.4, initializing i = 1; Step 3.5, Let the starting point S R For L ki ; Step 3.6, assigning i + 1 to i; Step 3.7, calculate the probability of the kth ant in the Nth foraging process to select the next node using formula (6) c the probability of the kth ant in the Nth foraging process to select the next node Step 4.2, calculate the probability of the kth ant in the Nth foraging process to select the next node using formula (7) the probability of the kth ant in the Nth foraging process to select the next node Step 4.3, calculate the probability of the kth ant in the Nth foraging process to select the next node using formula (8) c the probability of the kth ant in the Nth foraging process to select the next node the probability of the kth ant in the Nth foraging process to select the In equation (6), τ UV (N c ) is the Nth c Current node during the next foraging process With the next node The pheromones between them, α is the importance factor of information, β is the importance factor of the heuristic function, and allowed k For the k-th ant (excluding nodes in the taboo list) at the current node The set of neighboring candidate nodes For allowed k Any node in the set; τ UW (N c ) is the Nth c Current node during the next foraging process With allowed k any node in the set The pheromones between them; In the N c th foraging process, the current node The probability of each adjacent node is randomly determined in the N c th foraging process, the i th path node L ki of the k th ant, and the node with the maximum probability is the i+1 th path node L k(i+1) ; If the kth ant in the Nth foraging process is at the current node c and the pheromone value of its adjacent node is 0, then the current node is set as an obstacle node, i-1 is assigned to i, and L is set as the adjacent node; otherwise, L is set as the node with the maximum pheromone value, and the path node L ki is added to the tabu list, and the path length D is updated; and the pheromone value of the current node is updated. k(i+1) Otherwise, L is set as the node with the maximum pheromone value, and the path node L ki is added to the tabu list, and the path length D k is updated; and the pheromone value of the current node is updated. Step 3.

8. Return to Step 3.6 until the Nth c ant in the kth foraging process reaches the goal S G . Step 3.9, obtaining the Nth candidate point S c the kth ant released pheromone between the candidate point S I and its adjacent candidate point S J the kth ant released pheromone between the candidate point S In formula (7), Q is a constant; Step 3.

10. After k+1 is assigned to k, if k≤N ant then go to Step 3.4, otherwise, the paths of all ants in the N c th foraging process and their corresponding path lengths are displayed and the tabu table is emptied, and go to Step 3.

11. Step 3.11, Calculate the Nth foraging process using formula (8) c The pheromone released by all ants in the Nth foraging process Δτ IJ (N c ): Step 3.12, obtain the Nth candidate point S c +1th candidate point S I and its adjacent candidate points S J all ants between the N IJ (N c +1)th candidate point S τ IJ (N c +1) = (1 - p) - τ IJ (N c )+ Δτ IJ (N c ) (9) Step 3.13, if N c ≥ N gen , then the path of all ants in the N gen th foraging process is obtained and its corresponding path length , and the path corresponding to the shortest path length is selected as the optimal path, and the process goes to Step 4, otherwise, the process goes to Step 3.2; Step 4, after performing a smoothness processing on the optimal path, a best path for robot working navigation is obtained.

2. An electronic device comprising a memory and a processor, characterized in that The memory is configured to store a program supporting the processor to execute the path planning method of claim 1, and the processor is configured to execute the program stored in the memory.

3. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is configured to execute the steps of the path planning method of claim 1 when the computer program is executed by the processor.

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