Underwater robot multi-task-point path planning method considering marine environment

Abstract

The invention discloses an underwater robot multi-task-point path planning method considering a marine environment. Firstly, map construction is performed according to a grid method, and an ocean current function is constructed according to ocean current conditions; determining an evaluation system of path planning, and constructing a fitness function; then solving the shortest distance between any two points by adopting an A * algorithm, and transmitting a distance matrix to an improved ant colony algorithm; initializing related parameters of an ant algorithm, and setting the number of ants and the maximum number of iterations; an ant colony algorithm is improved by adopting a self-adaptive pheromone factor, a heuristic function factor and a volatile element factor; moving the positions of the ants by adopting a roulette method until the ants reach a target point, and updating pheromones after all the ants finish one iteration process; and repeating the iteration process until all ants reach the maximum number of iterations, and outputting an optimal path planning result. According to the improved ant colony algorithm provided by the invention, the convergence time is reduced, and different optimization results are obtained according to different requirements.

Description

Technical Field

[0001] The invention relates to a path planning method for an underwater robot, and in particular to a multi-task point path planning method for an underwater robot taking into account an ocean environment. Background Art

[0002] With the development of science and technology, underwater robots play an important role in the fields of marine exploration, resource exploration and environmental monitoring. Among them, the path planning technology of underwater robots is particularly critical, which enables the robot to efficiently complete the detection, observation and sampling of multiple mission points in the marine environment. The main purpose of path planning is to find one or more optimal driving routes given the starting point and the end point. These routes need to meet the requirements of safety, effectiveness and as short as possible or meet other performance indicators (such as time, energy consumption, smoothness, etc.). The successful application of this technology can not only improve the efficiency and accuracy of marine scientific research, but also support the development and utilization of marine resources and environmental monitoring and protection. The main purpose of path planning is to find one or more optimal driving routes given the starting point and the end point. These routes need to meet the requirements of safety, effectiveness and as short as possible or meet other performance indicators (such as time, energy consumption, smoothness, etc.).

[0003] The A* algorithm has the advantages of both the Dijkstra algorithm and the BFS algorithm. It uses heuristic search to determine the next step based on the value of the cost function, selects the node with the smallest cost as the expansion node, and finally generates a path.

[0004] Ant Colony Optimization (ACO), also known as the ant algorithm, is a bionic optimization algorithm proposed by Italian scholar Dorigo M et al. in the early 1990s. The algorithm is inspired by the foraging behavior of real ants in nature and solves complex optimization problems by simulating the pheromone transmission and path selection mechanism of ants in the process of finding food. It simulates the pheromones released by ants in the process of predation, and uses their accumulation and volatilization to guide ants to find the best or optimal solution.

[0005] In the ant colony algorithm, the problem to be solved is abstracted into a path planning problem in graph theory. Nodes represent candidate solutions in the solution space of the problem, and edges represent the transformation relationship between candidate solutions. Each ant randomly selects a starting point in the graph and chooses the next direction of travel based on the pheromone concentration and heuristic information on the path. Through multiple iterations, the ant colony gradually converges to the optimal solution or a near-optimal solution. Summary of the invention

[0006] In view of the defects of the prior art, the present invention provides a multi-task point path planning method for an underwater robot taking into account the marine environment. Different from the traditional ant colony algorithm, this method can solve the performance problems of the traditional ant colony algorithm such as slow convergence speed and falling into local optimum. On the basis of the algorithm integrating the A* algorithm, the traditional ant colony algorithm uses adaptive pheromone factors, heuristic function factors, and volatile factors, which can effectively speed up the path planning. At the same time, multiple indicators are used as evaluation functions, which is of great significance to the efficient and safe operation of underwater robots.

[0007] The above purpose is achieved through the following technical solutions:

[0008] A multi-task point path planning method for an underwater robot considering an ocean environment, the method comprising the following steps:

[0009] S1: Map construction is carried out according to the grid method, and the ocean current function is constructed according to the ocean current conditions;

[0010] S2: Based on the determined grid, the evaluation system of the path planning is determined, and the energy consumption is constructed using the ocean current function of step S1, followed by the smoothness construction, and linear weighting to form the corresponding fitness function;

[0011] S3, according to the current environment model, set the starting point and multiple target points, and use the A* algorithm to find the shortest distance between any target points;

[0012] S4: After completing the distance calculation between any two target points, the distance matrix is ​​passed to the improved ant colony algorithm, the relevant parameters of the ant algorithm are initialized, and the number of ants and the maximum number of iterations are set;

[0013] S5: After initializing the parameters, the adaptive pheromone factor, heuristic function factor, and volatile factor are further used to improve the ant colony algorithm;

[0014] S6: On the basis of completing S5, the ants are moved by using the roulette method until they reach the target point. After all ants have completed one iteration, the pheromone is updated according to the fitness function determined in step S2;

[0015] S7, repeat the iterative process of S6 until all ants reach the maximum number of iterations, output the optimal path planning result, and obtain the corresponding path length, energy consumption value and smoothness value.

[0016] Preferably, the specific method of map modeling in step S1 is:

[0017] Using a square grid, grids with obstacles are marked as obstacle grids, representing areas that the robot cannot enter; grids without obstacles are marked as blank grids to indicate the range that the robot can enter. The motion model of the underwater robot in the ocean is as follows:

[0018]

[0019] Among them, θ is the angle of the underwater robot's navigation speed on the θ axis, and u and v represent the components of the ocean current along the x-axis and y-axis respectively.

[0020] Preferably, the specific method of step S2 includes:

[0021] The path length L p and energy consumption E p and path smoothness M p The multi-objective function S composed of these three indicators p , using a linear summation fitness function and improving the pheromone update rule:

[0022] S p =k 1 L p +k 2 E p +k 3 M p

[0023] In the formula, k 1 is the weight factor of the path length, k 2 is the weight factor of energy consumption, k 3 is the weight factor of the path smoothness.

[0024]

[0025] In the formula, n is the total number of nodes, p is i is the i-th node on path p; p i+1 is the i+1th node on path p; L(p i ,p i+1 ) is the Euclidean distance from node i to node i+1 on path p;

[0026] Assume that the energy consumption of the underwater robot moving from node i to node j is recorded as J(i,j), and the simplified model of J(i,j) is:

[0027]

[0028] Where T represents the time required for movement, V represents the speed of the underwater robot, and assuming that the underwater robot moves at a constant speed and the flow rate and direction of the water are constant, then:

[0029]

[0030] Take E p =∑J(i,j),(s…,i,j,…,g), s is the starting node, g is the target node, and it represents the total navigation energy consumption of the underwater robot when it is affected by the ocean current during movement, d ij Indicates the distance between two nodes;

[0031] At the same time, the path smoothness parameter is quoted to ensure that the underwater robot can avoid collision as much as possible during operation. During the movement of the underwater robot, the angle difference is θ. The smoothness is expressed by quoting the angle difference of the underwater robot during the movement.

[0032]

[0033] Among them, (x i ,y i ) is the current position of the underwater robot, (x i-1 ,y i-1 ) is the position of the underwater robot at the last moment, (x i+1 ,y i+1 ) is the position at the next moment.

[0034] Preferably, the specific method in step S3 includes:

[0035] f(n)=g(n)+h(n)

[0036] Among them, f(n) is the evaluation fitness function of the node, g(n) is the path distance from the current point to the starting point, and h(n) represents the estimated path distance between the target point and the current point. Its expression is:

[0037]

[0038]

[0039] Among them, (x 0 ,y 0 ) is the coordinate value of the current node, (x s ,y s )The coordinate value of the starting position, (x g ,y g ) is the coordinate value of the end point.

[0040] Preferably, the setting of the number of ants and the maximum number of iterations in step S4 is as follows: the maximum number of iterations is set to 100 and the number of ants is set to 50 based on multiple repeated experiments.

[0041] Preferably, the specific method of step S5 includes:

[0042] Adaptive pheromone factor is used. When the number of iterations is small, the pheromone factor is small. As the number of iterations increases, the pheromone factor gradually increases, so that the movement direction of the ants is mainly determined by the generated pheromones:

[0043]

[0044] In the formula, α 0 is the minimum value of the pheromone factor, α max is the maximum value of the pheromone factor, λ 1 is the pheromone factor variation parameter, t is the number of iterations;

[0045] Adopting the adaptive heuristic function factor β, β is reduced as the number of iterations increases, so that the β value is relatively small in the later stage of iteration:

[0046]

[0047] In the formula, β 0 is the minimum value of the heuristic function factor, β min is the minimum value of the heuristic function factor, λ 2 is the heuristic function factor change parameter, t is the number of iterations;

[0048] Adjust the pheromone volatility factor. Use the method of improving the pheromone volatility factor to make the pheromone volatility factor ρ decrease as the number of iterations increases. In the dynamic adjustment, ρ min is the minimum value of pheromone volatility factor,

[0049]

[0050] Use the global optimal method to update pheromones Where S best is the multi-objective indicator of the optimal path in this iteration,

[0051]

[0052] Among them, Q represents the coefficient of pheromone change, which is a constant, and λ 3 Represents the parameter for adjusting the pheromone factor.

[0053] Preferably, the specific method of step S6 includes using a roulette method to determine the moving position of the ant's next target point.

[0054]

[0055] Among them, τ ij (t) is the pheromone content between the t-iteration positions on the grid map, that is, the pheromone content between moving from node i to j, η ij(t) is the heuristic function, α is the pheromone factor, β is the heuristic function factor, allow k is the node that is allowed to pass next time, d ij Represents the Euclidean distance from position i to j;

[0056] When all ants have traversed all target points, they will update the remaining pheromones on each road. These pheromones are all on the path (i, j). The pheromones on the path (i, j) are adjusted and updated at the t+1 iteration:

[0057]

[0058]

[0059]

[0060] Among them, ρ is the pheromone volatility factor, and the range of ρ is 0<ρ<1; Δτ ij (t) represents the increment of pheromone in path (i, j) in this cycle, Δτ ij k (t) is the amount of information that the kth ant stays on the path at the current moment, Q represents the coefficient of pheromone change, and L k Indicates the total distance of the route traveled by the kth ant in the current cycle

[0061] Beneficial effects:

[0062] The present invention provides a multi-task point path planning method for an underwater robot taking the marine environment into consideration. Different from the traditional ant colony algorithm, the method can solve the performance problems of the traditional ant colony algorithm such as slow convergence speed and falling into local optimum. On the basis of the algorithm integrating the A* algorithm, the method uses adaptive pheromone factors, heuristic function factors and volatile factor for the traditional ant colony algorithm, which can effectively improve the path planning speed. At the same time, multiple indicators are used as evaluation functions, which is of great significance to the efficient and safe operation of underwater robots. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The illustrative embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application.

[0064] In addition, throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the components and elements are not necessarily drawn to scale.

[0065] In the accompanying drawings;

[0066] Figure 1 : Flowchart of a multi-task point path planning method for an underwater robot based on considering the marine environment proposed in the present invention.

[0067] Figure 2 : Schematic diagram of the grid modeling proposed in the present invention.

[0068] Figure 3 : Schematic diagram of the method for ocean current modeling proposed in the present invention.

[0069] Figure 4 : Schematic diagram of the method for calculating path smoothness proposed in the present invention.

[0070] Figure 5 :The flowchart of the improved ant colony algorithm proposed in the present invention.

[0071] Figure 6 : The path planning flow chart of the present invention using the fusion A* algorithm and the improved ant colony algorithm.

[0072] Figure 7: The path planning results of the improved ant colony algorithm proposed in the present invention on a 30x30 map, where a, b, and c correspond to the optimal path length, the minimum energy consumption, and the optimal smoothness, respectively.

[0073] Figure 8: The path planning results of the improved ant colony algorithm proposed in the present invention on a 50x50 map, where a, b, and c correspond to the optimal path length, the lowest energy consumption, and the optimal smoothness, respectively.

[0074] Fig. 9 : Comparison of the results of the improved ant colony algorithm proposed in this invention with the path distance convergence graph of the traditional ant colony algorithm. DETAILED DESCRIPTION

[0075] The following is a detailed description of the embodiments of the present invention: This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0076] It is worth mentioning that, unless otherwise specified, the technical terms used in the present application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0077] The present invention provides a multi-task point path planning method for an underwater robot taking the marine environment into consideration. Different from the traditional ant colony algorithm, the method can solve the performance problems of the traditional ant colony algorithm such as slow convergence speed and falling into local optimum. On the basis of integrating the algorithm of the A* algorithm, the heuristic function expression of the traditional ant colony is changed, and adaptive pheromone factors, heuristic function factors and volatile factors are used, so that the path planning speed can be effectively improved. At the same time, multiple indicators are used as evaluation functions, which is of great significance to the efficient and safe operation of the underwater robot.

[0078] The present invention provides a multi-task point path planning method for an underwater robot considering the marine environment, the method comprising the following steps:

[0079] S1: Map construction is carried out according to the grid method, and the ocean current function is constructed according to the ocean current conditions;

[0080] S2: Based on the determined grid, the evaluation system of the path planning is determined, and the energy consumption is constructed using the ocean current function of step S1, followed by the smoothness construction, and linear weighting to form the corresponding fitness function;

[0081] S3, according to the current environment model, set the starting point and multiple target points, and use the A* algorithm to find the shortest distance between any target points;

[0082] S4: After completing the distance calculation between any two target points, the distance matrix is ​​passed to the improved ant colony algorithm, the relevant parameters of the ant algorithm are initialized, and the number of ants and the maximum number of iterations are set;

[0083] S5: After initializing the parameters, the adaptive pheromone factor, heuristic function factor, and volatile factor are further used to improve the ant colony algorithm;

[0084] S6: On the basis of completing S5, the ants are moved by using the roulette method until they reach the target point. After all ants have completed one iteration, the pheromone is updated according to the fitness function determined in step S2;

[0085] S7, repeat the iterative process of S6 until all ants reach the maximum number of iterations, output the optimal path planning result, and obtain the corresponding path length, energy consumption value and smoothness value.

[0086] Preferably, the specific method of map modeling in step S1 is:

[0087] A precise square grid is used, and the grids with obstacles are marked as obstacle grids (black parts), representing the areas that the robot cannot enter; the grids without obstacles are marked as blank grids (blank areas) to indicate the range that the robot can enter. The modeling of this article is as follows Figure 2 As shown:

[0088] The energy consumption of underwater robots in the actual navigation process is not only affected by the navigation distance, but also by the ocean current. Therefore, in the overall planning, the role of water current cannot be ignored. Since this paper only focuses on underwater robots that navigate in a large range, the motion model under ocean current disturbance is simplified, and on this basis, the energy consumption coefficient is introduced to establish a disturbance model of ocean current on the system. The motion model of underwater robots in the ocean is as follows: Figure 3 As shown:

[0089]

[0090] Among them, θ is the angle of the underwater robot's navigation speed on the θ axis, and u and v represent the components of the ocean current along the x-axis and y-axis respectively.

[0091] Preferably, the specific method of step S2 includes:

[0092] The path length L p and energy consumption E p and path smoothness M p The multi-objective function S composed of these three indicators p ,This multi-objective function aims to plan a comprehensive path with the shortest path length, the least energy consumption and the best smoothness. A linear summation fitness function is used, and the pheromone update rule is improved.

[0093] S p =k 1 L p +k 2 E p +k 3 M p

[0094] In the formula, k 1 is the weight factor of the path length, k 2 is the weight factor of energy consumption, k 3 is the weight factor of the path smoothness.

[0095]

[0096] In the formula, n is the total number of nodes, p is i is the i-th node on path p; p i+1 is the i+1th node on path p; L(p i ,p i+1 ) is the Euclidean distance from node i to node i+1 on path p.

[0097] Assume that the energy consumption of the underwater robot moving from node i to node j is recorded as J(i,j), and the simplified model of J(i,j) is:

[0098]

[0099] Where T represents the time required for movement, V represents the speed of the underwater robot, and assuming that the underwater robot moves at a constant speed and the flow rate and direction of the water are constant, then:

[0100]

[0101] Take E p =∑J(i,j),(s…,i,j,…,g), s is the starting node, g is the target node, and it represents the total navigation energy consumption of the underwater robot when it is affected by the ocean current during movement, d ij Indicates the distance between two nodes.

[0102] At the same time, the path smoothness parameter is used to ensure that the underwater robot can avoid collisions as much as possible during operation. Figure 4 The θ represents the degree of smoothness by citing the angle difference of the underwater robot during the motion process.

[0103]

[0104] Among them, (x i ,y i ) is the current position of the underwater robot, (x i-1 ,y i-1 ) is the position of the underwater robot at the last moment, (x i+1 ,y i+1 ) is the position at the next moment, M p To indicate the degree of smoothness of a corner during the movement of an underwater robot.

[0105] Preferably, the specific method of step S3 includes the following steps:

[0106] f(n)=g(n)+h(n)

[0107] Among them, f(n) is the evaluation fitness function of the node, g(n) is the path distance from the current point to the starting point, and h(n) represents the estimated path distance between the target point and the current point. Its expression is:

[0108]

[0109] Among them, (x 0 ,y 0 ) is the coordinate value of the current node, (x s ,y s )The coordinate value of the starting position, (x g ,y g ) is the coordinate value of the end point.

[0110] The traditional A* algorithm has four neighborhoods, namely upward, downward, left, and right, with a minimum rotation angle of 90 degrees; and eight neighborhoods, with four more directions, upper right, lower right, upper left, and lower left, with a minimum rotation angle of 45 degrees. Underwater robots move slowly underwater and have weak maneuverability, so they are not suitable for large-angle rotation. The eight-neighbor expansion algorithm is used to realize the movement of underwater robots.

[0111] Preferably, the specific method of step S4 includes: setting the maximum number of iterations to 100 and the number of ants to 50 according to multiple repeated experiments.

[0112] Preferably, the specific method of step S5 includes:

[0113] Adaptive pheromone factor is used. When the number of iterations is small, the pheromone factor is kept small. As the number of iterations increases, the pheromone factor gradually increases, so that the movement direction of the ants is mainly determined by the produced pheromones.

[0114]

[0115] In the formula, α 0 is the minimum value of the pheromone factor, α max is the maximum value of the pheromone factor, λ 1 is the pheromone factor variation parameter, and t is the number of iterations.

[0116] Adopting the adaptive heuristic function factor β, β is reduced as the number of iterations increases, so that the β value is relatively small in the later stage of iteration:

[0117]

[0118] In the formula, β 0 is the minimum value of the heuristic function factor, β min is the minimum value of the heuristic function factor, λ 2 is the heuristic function factor change parameter, and t is the number of iterations.

[0119] In the initial stage, a larger value is used to increase the search range of ants and improve the possibility of finding a solution. In the later stage, a smaller value of the volatility factor is used to increase the convergence speed. The present invention uses a method of improving the pheromone volatility factor so that the pheromone volatility factor ρ decreases with the increase of the number of iterations, while ensuring the diversity of the path and improving the convergence speed. min is the minimum value of the volatile factor.

[0120]

[0121] Use the global optimal method to update pheromones, where Sbest is the multi-objective indicator of the optimal path in this iteration.

[0122]

[0123] Preferably, the step S6 specifically includes: the specific method of the step S5 includes using the roulette method to determine the moving position of the next target point of the ant

[0124]

[0125] Among them, τ ij (t) is the pheromone content between the t-iteration positions on the grid map, that is, the pheromone content between moving from position i to j, η ij (t) is the heuristic function, α is the pheromone factor, β is the heuristic function factor, allow k is the node that is allowed to pass next time, d ij Represents the Euclidean distance from position i to j.

[0126] When all ants have traversed all target points, they will update the remaining pheromones on each path, which are all on the path (i, j). Usually, the pheromones on the path (i, j) are adjusted and updated in t+1 iterations:

[0127]

[0128]

[0129]

[0130] Among them, ρ is the pheromone volatility factor, and the range of ρ is 0<ρ<1; represents the increment of pheromone in path (i, j) in this cycle, The amount of information that the kth ant stays on the path at the current moment, Q represents the coefficient of pheromone change, L k Represents the total distance of the route traveled by the kth ant in the current cycle.

[0131] Preferably, step S7 is to obtain the path result of this planning after completing the process, as well as the required path distance, energy consumption and smoothness.

[0132] This method is a new underwater robot path planning result based on ant colony algorithm under the traditional ant colony algorithm. It can be directly applied to underwater robot path planning under multiple constraints in marine environment.

[0133] The system was compiled using Matlab language and used the Windows 11 operating system of i9-14900, with a main frequency of 2.4 GHz and a running memory of 16 GB. In order to make the experimental conclusion more representative and accurate, the experiment was repeated in two environments: 30x30 with fewer obstacles and 50x50 with more obstacles. The parameter is set to α 0 is 1, α max is 6, β 0 is 6, β min =1,ρ min is 0.2, λ 1 =0.5, =0.5, λ3 3 =1.

[0134] In the 30x30 environmental model, according to the actual ocean environment, the ocean current factor is added. It is assumed that the AUV is sailing at a uniform speed of V = 1m / s, the ocean velocity along the x-axis component u is 0.3m / s, and along the y-axis component v is 0.4m / s, k 1 ,k 2 ,k 3 The paths obtained by taking different ratio values ​​are shown in Figure 7(a) to (c):

[0135] Table 1 Path results of 30x30 with different weight coefficients

[0136]

[0137] As can be seen from Figure 7, when the weight of the multi-objective function focuses on the path length, the path order is S-4-2-1-3-5-7-6-8-S, when the weight of the multi-objective function focuses on energy consumption, the path order is S-2-4-8-1-3-5-7-6-S, and when the weight of the multi-objective function focuses on path smoothness, the path order is S-2-1-3-5-6-7-8-4-S. The results of Figure 7 are shown in Table 1, and it can be seen that the results meet the preset performance. Different optimal sequences are obtained according to different optimization conditions.

[0138] In the 50x50 environment model, the environment is more complex than that of 30x30. The parameter settings are the same as those of 30x30, and the following results are obtained in Figure 8: (a) is the sequence curve corresponding to the minimum path distance, (b) is the sequence curve corresponding to the minimum energy consumption, and (c) is the sequence curve corresponding to the smooth path.

[0139] In order to verify the effectiveness of the algorithm, the number of target points is increased to 9 in a dense environment. From the results in Figure 8, we can see that when the weight of the multi-objective function focuses on the path length, the path order is S-7-8-9-6-5-3-2-1-4-S, when the weight of the multi-objective function focuses on energy consumption, the path order is S-6-5-3-2-1-9-8-7-4-S, and when the weight of the multi-objective function focuses on path smoothness, the path order is S-6-5-4-1-2-3-9-8-7-S. The results of Figure 8 are shown in Table 2, and it can be seen that the results meet the preset performance, and different optimal sequences are obtained according to different optimization conditions.

[0140] Table 2 Path results of 50x50 with different weight coefficients

[0141]

[0142] At the same time, by setting the performance comparison with the traditional ant colony algorithm, the target point is set the same as 30x30, the parameters of the traditional ant colony algorithm are set to α as 1.5, β as 2, ρ as 0.9, but the weight coefficient is only k 1 is 1, where the convergence curve comparison between the proposed algorithm and the traditional algorithm is shown in the figure Fig. 9 As shown in the figure: The improved results not only speed up the convergence speed, but also avoid falling into the local optimum.

[0143] In summary, in combination with the accompanying drawings and the above table, the algorithm proposed in this embodiment can significantly accelerate the convergence speed and obtain a better distance than the traditional ant colony algorithm.

[0144] This embodiment provides an underwater robot path planning method based on an improved ant colony that takes the marine environment into consideration. Different from the traditional ant colony algorithm, this method can solve the performance problems of the traditional ant colony algorithm, such as slow convergence speed and falling into local optimum. On the basis of the algorithm, adaptive pheromone factors, heuristic function factors, and volatile factors are used to effectively increase the path planning speed. At the same time, multiple indicators are used as evaluation functions, which is of great significance to the efficient and safe operation of underwater robots.

[0145] Finally, it should be noted that the above implementation case is only a detailed explanation of the technical solution, rather than a limitation thereof; although the technical solution of the present invention has been described with reference to this specific implementation case, ordinary technicians in the field should understand that they can still modify the solution of the implementation case, or make equivalent replacements for some of them; such modification or replacement cannot essentially deviate from the scope of the technical solution proposed by the present invention, and should be included in the scope of the claims and specification of the present invention.

Claims

1. A multi-task point path planning method for an underwater robot considering the marine environment, characterized in that: The method comprises the following steps: S1: Map construction based on the grid method, and ocean current function construction based on ocean current conditions; S2: Based on the determined grid, the evaluation system of the path planning is determined, and the energy consumption is constructed using the ocean current function of step S1, followed by the smoothness construction, and linear weighting to form the corresponding fitness function; S3, according to the current environment model, set the starting point and multiple target points, and use the A* algorithm to find the shortest distance between any target points; S4: After completing the distance calculation between any two target points, the distance matrix is ​​passed to the improved ant colony algorithm, the relevant parameters of the ant algorithm are initialized, and the number of ants and the maximum number of iterations are set; S5: After initializing the parameters, the adaptive pheromone factor, heuristic function factor, and volatile factor are further used to improve the ant colony algorithm; S6: On the basis of completing S5, the ants are moved by using the roulette method until they reach the target point. After all ants have completed one iteration, the pheromone is updated according to the fitness function determined in step S2; S7, repeat the iterative process of S6 until all ants reach the maximum number of iterations, output the optimal path planning result, and obtain the corresponding path length, energy consumption value and smoothness value.

2. The method for multi-task point path planning of an underwater robot considering the marine environment according to claim 1, characterized in that: The specific method of map modeling in step S1 is: Using a square grid, grids with obstacles are marked as obstacle grids, representing areas that the robot cannot enter; grids without obstacles are marked as blank grids to indicate the range that the robot can enter. The motion model of the underwater robot in the ocean is as follows: Among them, θ is the angle of the underwater robot's navigation speed on the θ axis, and u and v represent the components of the ocean current along the x-axis and y-axis respectively.

3. The method for multi-task point path planning of an underwater robot considering the marine environment according to claim 1, characterized in that: The specific method of step S2 includes: The path length L p and energy consumption E p and path smoothness M p The multi-objective function S composed of these three indicators p , using a linear summation fitness function and improving the pheromone update rule: S p =k1L p +k2E p +k3M p Where k1 is the weight factor of path length, k2 is the weight factor of energy consumption, and k3 is the weight factor of path smoothness. In the formula, n is the total number of nodes, p is i is the i-th node on path p; p i+1 is the i+1th node on path p; L(p i ,p i+1 ) is the Euclidean distance from node i to node i+1 on path p; Assume that the energy consumption of the underwater robot moving from node i to node j is recorded as J(i,j), and the simplified model of J(i,j) is: Where T represents the time required for movement, V represents the speed of the underwater robot, and assuming that the underwater robot moves at a constant speed and the flow rate and direction of the water are constant, then: Take E p =∑J(i,j),(s…,i,j,…,g), s is the starting node, g is the target node, and it represents the total navigation energy consumption of the underwater robot when it is affected by the ocean current during movement, d ij Indicates the distance between two nodes; At the same time, the path smoothness parameter is quoted to ensure that the underwater robot can avoid collision as much as possible during operation. During the movement of the underwater robot, the angle difference is θ. The degree of smoothness is expressed by quoting the angle difference of the underwater robot during the movement. Among them, (x i ,y i ) is the current position of the underwater robot, (x i-1 ,y i-1 ) is the position of the underwater robot at the last moment, (x i+1 ,y i+1 ) is the position at the next moment.

4. The method for multi-task point path planning of an underwater robot considering the marine environment according to claim 1, characterized in that: The specific method of step S3 includes: f(n)=g(n)+h(n) Among them, f(n) is the evaluation fitness function of the node, g(n) is the path distance from the current point to the starting point, and h(n) represents the estimated path distance between the target point and the current point. Its expression is: Among them, (x0, y0) is the coordinate value of the current node, (x s ,y s )The coordinate value of the starting position, (x g ,y g ) is the coordinate value of the end point.

5. The method for multi-task point path planning of an underwater robot considering the marine environment according to claim 1, characterized in that: The step S4 sets the number of ants and the maximum number of iterations: the maximum number of iterations is set to 100 and the number of ants is set to 50 based on repeated experiments.

6. The method for multi-task point path planning of an underwater robot considering the marine environment according to claim 1, characterized in that: The specific method of step S5 includes: Adopting adaptive pheromone factor, when the number of iterations is relatively small, the pheromone factor is relatively small, and as the number of iterations increases, the pheromone factor gradually increases, so that the movement direction of the ants is mainly determined by the generated pheromones: In the formula, α0 is the minimum value of the pheromone factor, α max is the maximum value of the pheromone factor, λ1 is the pheromone factor variation parameter, and t is the number of iterations; Adopting the adaptive heuristic function factor β, β is reduced as the number of iterations increases, so that the β value is relatively small in the later stage of iteration: In the formula, β0 is the minimum value of the heuristic function factor, β min is the minimum value of the heuristic function factor, λ2 is the change parameter of the heuristic function factor, and t is the number of iterations; Adjust the pheromone volatility factor. Use the method of improving the pheromone volatility factor to make the pheromone volatility factor ρ decrease as the number of iterations increases. In the dynamic adjustment, ρ min is the minimum value of pheromone volatility factor, Use the global optimal method to update pheromones Where S best is the multi-objective indicator of the optimal path in this iteration, Among them, Q represents the coefficient of pheromone change, which is a constant, and λ3 represents the parameter for adjusting the pheromone factor.

7. The method for multi-task point path planning of an underwater robot considering the marine environment according to claim 6, characterized in that: The specific method of step S6 includes using the roulette method to determine the moving position of the ant's next target point Among them, τ ij (t) is the pheromone content between the t-iteration positions on the grid map, that is, the pheromone content between moving from node i to j, η ij (t) is the heuristic function, α is the pheromone factor, β is the heuristic function factor, allow k is the node that is allowed to pass next time, d ij Represents the Euclidean distance from position i to j; When all ants have traversed all target points, they will update the remaining pheromones on each path. These pheromones are all on the path (i, j). The pheromones on the path (i, j) are adjusted and updated at the t+1 iteration: Among them, ρ is the pheromone volatility factor, and the range of ρ is 0<ρ<1; Δτ ij (t) represents the increment of pheromone in path (i, j) in this cycle, Δτ ij k (t) is the amount of information that the kth ant stays on the path at the current moment, Q represents the coefficient of pheromone change, and L k Represents the total distance of the route traveled by the kth ant in the current cycle.

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