Autonomous navigation method and system for underwater robot based on improved ant colony algorithm
By constructing a path planning method that combines a risk-weighted cost map, the PRM algorithm, and an improved ant colony algorithm, the problems of blindness and uneven path smoothness in the autonomous navigation of underwater robots by the traditional ant colony algorithm are solved, and safer and more efficient navigation path planning is achieved.
Patent Information
- Application Number
- CN202610886991.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-18
- Publication Date
- 2026-08-25
AI Technical Summary
Traditional ant colony algorithms lack prior guidance for complex marine environments in the autonomous navigation of underwater robots, resulting in strong blind search, difficulty in effectively avoiding high-risk areas, and uneven navigation paths, which affects stability and executability.
By constructing a risk-weighted cost map that integrates elevation, slope, and local undulation features, the PRM algorithm is used to generate initial feasible paths and allocate non-uniform pheromones. An improved ant colony algorithm is then used to update the pheromone concentration, and finally, cubic B-spline curves are used for path smoothing.
It significantly improves the safety and reliability of underwater robots in complex marine environments, shortens the navigation path length, reduces energy consumption, and enhances path feasibility and navigation stability.
Smart Images
Figure CN122632858A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of underwater robot control technology, specifically to an autonomous navigation method and system for underwater robots based on an improved ant colony algorithm. Background Technology
[0002] With the increasing demand for marine resource development and underwater operations, underwater robots are being used more and more widely in complex near-bottom terrain environments. However, the dramatic undulations of the seabed, the irregular distribution of obstacles, and the limited prior environmental information present challenges to the global autonomous navigation methods of underwater robots, including a large search space, slow convergence speed, and susceptibility to local optima. Traditional ant colony algorithms initialize pheromone distribution in a uniform grid environment, lacking prior guidance on terrain risks, resulting in strong blindness in the initial search phase and difficulty in effectively avoiding high-risk areas such as steep slopes and undulating terrain. The navigation paths planned by traditional methods often have many turns and poor smoothness, which do not match the actual dynamic characteristics of underwater robots, affecting navigation stability and executability.
[0003] Therefore, there is an urgent need for an autonomous navigation method and system for underwater robots based on an improved ant colony algorithm to address the shortcomings of existing technologies. Summary of the Invention
[0004] The purpose of this invention is to provide an autonomous navigation method and system for underwater robots based on an improved ant colony algorithm, so as to solve the technical problems mentioned in the background art.
[0005] To achieve the above objectives, the first aspect of this invention proposes an autonomous navigation method for underwater robots based on an improved ant colony algorithm, comprising: Step 1: Obtain a near-bottom terrain grid map, and mark impassable areas as obstacle grids and passable areas as passable grids in the grid map; Step 2: Randomly sample passable grid cells using the PRM algorithm to generate initial feasible paths, and distribute non-uniform initial pheromones on the initial feasible paths; Step 3: Use the improved ant colony algorithm to iteratively update the pheromone concentration on the initial feasible path until convergence, and select the path with the highest pheromone concentration to obtain the optimized path; Step 4: Smooth the optimized path using a cubic B-spline curve to obtain the autonomous navigation path of the underwater robot, and the underwater robot navigates according to the planned path.
[0006] Further, in step 1, marking impassable areas as obstacle grids and marking passable areas as passable grids in the grid map includes: The risk-weighted cost function value of the raster is calculated using the following expression: in, Represents grid The near-bottom slope, Represents grid The near-bottom terrain undulations, Represents grid The near-bottom topographic normalized elevation, , and For the corresponding weight coefficients, and + + =1.
[0007] When the risk-weighted cost function value of a grid exceeds a preset risk threshold, it is marked as an obstacle grid. When the risk-weighted cost function value of a grid is less than or equal to a preset risk threshold, it is marked as a passable grid.
[0008] Furthermore, in step 2, the PRM algorithm is used to randomly sample passable grid cells to generate initial feasible paths, including: Determine the starting and ending points of the underwater robot; The PRM algorithm is used to randomly sample a series of points within the passable grid as potential path nodes. When the distance between two sampling nodes is less than or equal to a preset distance threshold and the line connecting the two sampling nodes does not intersect with the obstacle grid, a connection relationship between the two sampling points is established. Based on the sampling nodes after establishing the connection relationship, a probabilistic route map is constructed, and the initial feasible path of the connection start position and end position is searched.
[0009] Furthermore, the initial pheromone concentration in step 2 is: Wherein, τ0(x,y) represents the initial pheromone concentration of grid (x,y); τ1(x,y) represents the pheromone concentration of grid (x,y) based on the shortest distance to the initial feasible path; τ2(x,y) represents the pheromone concentration of grid (x,y) based on the Euclidean distance to the target point; a and b are the corresponding weight coefficients, and a+b=1; ψ(x,y) represents the terrain risk suppression factor of grid (x,y).
[0010] Furthermore, step 3, which uses an improved ant colony algorithm to iteratively update the pheromone concentration on the initially feasible path until convergence, includes: The minimum path evaluation value is calculated based on the safety index, energy consumption index, and path tortuosity. An improved ant colony algorithm is used to update the pheromone concentration on the initial feasible path based on the minimum path evaluation value.
[0011] Furthermore, the expression for updating the pheromone concentration in step 3 is: in, express Time Path The concentration of pheromones on the surface; Volatility coefficient; Represents the path at time t The increase in pheromone concentration left by ant m, if ant m has traversed the path , = If ant m does not traverse the path Q is the pheromone intensity, P is the minimum path evaluation value, and n is the total number of ants.
[0012] Furthermore, the expression for the minimum path evaluation value is: P Where S represents the safety index, E represents the energy consumption index, F represents the path tortuosity, and k1, k2, and k3 are the corresponding weight coefficients, and k1+k2+k3=1.
[0013] Furthermore, the expression for smoothing the optimized path using a cubic B-spline curve in step 4 is as follows: Where C(φ) represents a cubic B-spline curve; P0, P1, P2, and P3 represent the four control points involved in the curve construction; J 0,3 (φ), J 1,3 (φ), J 2,3 (φ) and J 3,3 (φ) represent the cubic B-spline basis functions of the corresponding control points; φ is the curve parameter.
[0014] A second aspect of this invention proposes an autonomous navigation system for an underwater robot based on an improved ant colony algorithm, comprising: The acquisition module is used to acquire a near-bottom terrain grid map, and mark impassable areas as obstacle grids and passable areas as passable grids in the grid map; The generation module is used to randomly sample passable grids using the PRM algorithm to generate initial feasible paths and distribute non-uniform initial pheromones on the initial feasible paths. The update module is used to iteratively update the pheromone concentration on the initial feasible path using an improved ant colony algorithm until convergence, and selects the path with the highest pheromone concentration to obtain the optimized path. The optimization module is used to smooth the optimized path using cubic B-spline curves to obtain the autonomous navigation path of the underwater robot, which then navigates according to the planned path.
[0015] A third aspect of the present invention provides an electronic device for autonomous navigation of an underwater robot based on an improved ant colony algorithm, comprising: a memory, a processor, and a computer program, wherein the computer program is stored in the memory and configured to be executed by the processor to implement any of the methods described herein.
[0016] The beneficial effects of the technical solutions provided by the embodiments of the present invention include: 1. By constructing a risk-weighted cost map that integrates elevation, slope, and local undulation features, and introducing a terrain risk suppression factor, the system effectively avoids dangerous areas with high slopes and undulations near the bottom, significantly improving the safety and reliability of underwater robots navigating in complex marine environments.
[0017] 2. A non-uniform initial pheromone allocation strategy guided by PRM and a multi-target pheromone update mechanism are adopted to reduce search blindness, accelerate algorithm convergence, avoid getting trapped in local optima, and significantly improve path planning efficiency and global optimization ability.
[0018] 3. By optimizing the path straightness and smoothing the B-spline, the number of path turns and the turning amplitude are reduced, the navigation path length is shortened, energy consumption is reduced, and the planned trajectory is more in line with the dynamic characteristics of the underwater robot, thereby enhancing the feasibility of the path and the stability of navigation. Attached Figure Description
[0019] Figure 1 This is a flowchart illustrating an embodiment of the present invention; Figure 2 This is a schematic diagram of a grid map according to an embodiment of the present invention; Figure 3 As an embodiment of the present invention Figure 1 Comparison chart of global path planning results; Figure 4 As an embodiment of the present invention Figure 2 Comparison chart of global path planning results. Detailed Implementation
[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0021] Example 1 The first aspect of this invention proposes an autonomous navigation method for underwater robots based on an improved ant colony algorithm, such as... Figure 1 As shown, the method includes: Step 1: Obtain a near-bottom terrain grid map, and mark impassable areas as obstacle grids and passable areas as passable grids in the grid map; Furthermore, in step 1, marking impassable areas as obstacle grids and marking passable areas as passable grids in the grid map includes: The risk-weighted cost function value of the raster is calculated using the following expression: in, Represents grid The near-bottom slope, Represents grid The near-bottom terrain undulations, Represents grid The near-bottom topographic normalized elevation, , and For the corresponding weight coefficients, and + + =1.
[0022] In this embodiment, for underwater robots, abrupt changes in slope and local undulations in the terrain are the most direct risk factors leading to capsizing, bottoming out, or propeller collision. Therefore, treating slope and undulation as the highest risk factors is a primary condition for ensuring physical safety during navigation. Elevation information reflects the absolute elevation of the terrain; this invention sets it as a secondary weight, mainly used to differentiate the overall risk at different depths and to assist in the judgment of slope and undulation. Therefore, [the following is taken as a separate, unrelated point]. =0.4, =0.4, =0.2.
[0023] When the risk-weighted cost function value of a grid exceeds a preset risk threshold, it is marked as an obstacle grid. When the risk-weighted cost function value of a grid is less than or equal to a preset risk threshold, it is marked as a passable grid.
[0024] In this embodiment, the near-bottom topographic raster map of the digital elevation model published by the General Bathymetric Chart of the Oceans (GEBCO) is read, such as... Figure 2 As shown in the figure, the black grids are obstacle grids, and the white grids are passable grids.
[0025] Step 2: Determine the starting and ending positions of the underwater robot, use the PRM algorithm to randomly sample the passable grid to generate an initial feasible path, and distribute non-uniform initial pheromones on the initial feasible path; Furthermore, in step 2, the PRM algorithm is used to randomly sample passable grid cells to generate initial feasible paths, including: Determine the starting and ending points of the underwater robot; The PRM algorithm is used to randomly sample a series of points within the passable grid as potential path nodes. When the distance between two sampling nodes is less than or equal to a preset distance threshold and the line connecting the two sampling nodes does not intersect with the obstacle grid, a connection relationship between the two sampling points is established. Based on the sampling nodes after establishing the connection relationship, a probabilistic route map is constructed, and the initial feasible path of the connection start position and end position is searched.
[0026] Furthermore, the initial pheromone concentration in step 2 is: Wherein, τ0(x,y) represents the initial pheromone concentration of grid (x,y); τ1(x,y) represents the pheromone concentration of grid (x,y) based on the shortest distance to the initial feasible path; τ2(x,y) represents the pheromone concentration of grid (x,y) based on the Euclidean distance to the target point; a and b are the corresponding weight coefficients, and a+b=1; ψ(x,y) represents the terrain risk suppression factor of grid (x,y).
[0027] In this embodiment, .
[0028] The formula for calculating the terrain risk inhibition factor ψ(x,y) is: ψ(x,y) = 1 - C(x,y); Where C(x,y) is the risk-weighted cost function value of the grid (x,y).
[0029] The larger C(x,y) is, the higher the risk; the smaller ψ(x,y) is, the more the initial pheromone is suppressed. The smaller C(x,y), the lower the risk; the closer ψ(x,y) is to 1, the less affected the initial pheromone is.
[0030] Step 3: Use the improved ant colony algorithm to iteratively update the pheromone concentration on the initial feasible path until convergence, and select the path with the highest pheromone concentration to obtain the optimized path; Furthermore, step 3, which uses an improved ant colony algorithm to iteratively update the pheromone concentration on the initially feasible path until convergence, includes: The minimum path evaluation value is calculated based on the safety index, energy consumption index, and path tortuosity. An improved ant colony algorithm is used to update the pheromone concentration on the initial feasible path based on the minimum path evaluation value.
[0031] Furthermore, the expression for updating the pheromone concentration in step 3 is: in, express Time Path The concentration of pheromones on the surface; Volatility coefficient; Represents the path at time t The increase in pheromone concentration left by ant m, if ant m has traversed the path , = If ant m does not traverse the path Q is the pheromone intensity, P is the minimum path evaluation value, and n is the total number of ants.
[0032] In this embodiment, Q=5.
[0033] Furthermore, the expression for the minimum path evaluation value is: Where S represents the safety index, E represents the energy consumption index, F represents the path tortuosity, and k1, k2, and k3 are the corresponding weight coefficients, and k1+k2+k3=1.
[0034] In this embodiment, =0.4, 2 = 0.3, 3 = 0.3.
[0035] In this embodiment, the safety index The safety index S is defined as the average Euclidean distance from a hazardous grid to an obstacle. If the average distance from a grid to an obstacle is less than a preset safety distance threshold, the grid is considered a hazardous grid. The greater the average Euclidean distance between a grid and an obstacle, the higher the safety index S of the path.
[0036] Energy consumption index It is the sum of the Euclidean distances between any two adjacent grid cells along the path. The shorter the path length, the less energy is consumed.
[0037] Path tortuosity Related to the turning angle and the number of turns, frequent sharp turns increase sailing time and risk, directly affecting the stability and efficiency of navigation.
[0038] set up For the i-th grid cell on the planning path, for The coordinate values.
[0039] Assume the path has a grid. and Define the slopes of the two grids as follows: and : Within the path and rotation angle between Represented as: ; The path tortuosity F takes into account both the turning angle and the number of turns, and its expression is: in, and These are the weighting coefficients. This represents the number of turns.
[0040] In this embodiment, =0.6, =0.4.
[0041] Step 4: Smooth the optimized path using a cubic B-spline curve to obtain the autonomous navigation path of the underwater robot, and the underwater robot navigates according to the planned path.
[0042] Furthermore, the expression for smoothing the optimized path using a cubic B-spline curve in step 4 is as follows: Where C(φ) represents a cubic B-spline curve; P0, P1, P2, and P3 represent the four control points involved in the curve construction; J 0,3 (φ), J 1,3 (φ), J 2,3 (φ) and J 3,3 (φ) represent the cubic B-spline basis functions of the corresponding control points; φ is the curve parameter.
[0043] In this embodiment, based on GEBCO digital elevation model data, two dimensions are selected. Simulation experiments were conducted on typical near-bottom terrain areas.
[0044] land Figure 1 The starting point is set to (35, 35), and the ending point is set to (350, 350); Figure 2 The starting point is set to (300, 35), and the ending point is (35, 360).
[0045] The algorithm simulation parameters are set as follows: number of ants 100, number of iterations 2000, pheromone factor 1, heuristic function factor 7, pheromone evaporation factor 0.3, number of random PRM samples 500, maximum distance of PRM connection 100.
[0046] The evaluation metrics include path length, safety index, path tortuosity, minimum path assessment value, and running time. The safety index reflects the distance between the path and obstacles and the number of hazardous grids; a higher safety index indicates a safer path. Path tortuosity combines turning angles and the number of turns; a lower value indicates a smoother path. The minimum path assessment value comprehensively reflects safety, energy consumption, and tortuosity; a lower value is better.
[0047] Table 1. Experimental results of path planning using three algorithms In Table 1, the first ant colony algorithm is the traditional ant colony algorithm, and the second ant colony algorithm is an ant colony algorithm based on deadlock strategy and adaptive heuristic function. As can be seen from Table 1, the method of this invention significantly outperforms the traditional ant colony algorithm and existing improved ant colony algorithms on both maps.
[0048] land Figure 1 The results of the intermediate path planning are as follows Figure 3 shown, ground Figure 2 The results of the intermediate path planning are as follows Figure 4 As shown, the traditional ant colony algorithm has a path length of 427.09m and suffers from problems such as being too close to obstacles, looping, and moving too far from the target. Its safety index is only 0.89, path tortuosity is 83.95, and minimum path evaluation value is as high as 3089.12. The ant colony algorithm based on deadlock strategy and adaptive heuristic function shows some improvement over the traditional ant colony algorithm, but its optimization effect is still inferior to that of this invention. The average path length planned by the method of this invention is 152.84m, which is 64.21% less than the traditional ant colony algorithm and 19.63% less than the ant colony algorithm based on deadlock strategy and adaptive heuristic function; the safety index is 0.92, indicating a greater distance from obstacles compared to other algorithms; the path tortuosity is 8.27, which is 90.15% less than the traditional ant colony algorithm and 67.60% less than the ant colony algorithm based on deadlock strategy and adaptive heuristic function; the minimum path evaluation value is 757.02, which is 75.49% less than the traditional ant colony algorithm; and the running time is 26.66% less than the traditional ant colony algorithm. Figure 1 The path length of the method of this invention is only 237.72m, which is far better than the 772.16m of the traditional ant colony algorithm. The path tortuosity is reduced to 9.65, and the running time is reduced from 418.04s to 326.02s, showing a significant performance improvement.
[0049] In terms of convergence analysis, the method of this invention significantly outperforms the comparative methods in terms of security, energy consumption, tortuosity, and minimum path evaluation. The method of this invention converges to the optimal solution after approximately 1500 iterations, while the ant colony algorithm based on deadlock strategy and adaptive heuristic function and the traditional ant colony algorithm only converge after 1950 iterations and are prone to falling into local optima, failing to perform effective global search of the map. The algorithm of this invention maintains a certain degree of exploratory nature while effectively utilizing the PRM optimal solution, balancing global search and local search.
[0050] Example 2 A second aspect of this invention proposes an autonomous navigation system for an underwater robot based on an improved ant colony algorithm, comprising: The acquisition module is used to acquire a near-bottom terrain grid map, and mark impassable areas as obstacle grids and passable areas as passable grids in the grid map; The generation module is used to randomly sample passable grids using the PRM algorithm to generate initial feasible paths and distribute non-uniform initial pheromones on the initial feasible paths. The update module is used to iteratively update the pheromone concentration on the initial feasible path using an improved ant colony algorithm until convergence, and selects the path with the highest pheromone concentration to obtain the optimized path. The optimization module is used to smooth the optimized path using cubic B-spline curves to obtain the autonomous navigation path of the underwater robot, which then navigates according to the planned path.
[0051] Example 3 A third aspect of the present invention provides an electronic device for autonomous navigation of an underwater robot based on an improved ant colony algorithm, comprising: a memory, a processor, and a computer program, wherein the computer program is stored in the memory and configured to be executed by the processor to implement any of the methods described herein.
[0052] The contents not described in detail in this specification are prior art known to those skilled in the art. Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0053] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0054] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0055] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.
Claims
1. A method for autonomous navigation of an underwater robot based on an improved ant colony algorithm, characterized in that, include: Step 1: Obtain a near-bottom terrain grid map, mark impassable areas as obstacle grids and passable areas as passable grids in the grid map; Step 2: Randomly sample passable grid cells using the PRM algorithm to generate initial feasible paths, and distribute non-uniform initial pheromones on the initial feasible paths; Step 3: Use the improved ant colony algorithm to iteratively update the pheromone concentration on the initial feasible path until convergence, and select the path with the highest pheromone concentration to obtain the optimized path; Step 4: Smooth the optimized path using a cubic B-spline curve to obtain the autonomous navigation path of the underwater robot, and the underwater robot navigates according to the planned path.
2. The underwater robot autonomous navigation method based on the improved ant colony algorithm according to claim 1, characterized in that, In step 1, marking impassable areas as obstacle grids and marking passable areas as passable grids in the grid map includes: The risk-weighted cost function value of the raster is calculated using the following expression: in, Represents grid The near-bottom slope, Represents grid The near-bottom terrain undulations, Represents grid The near-bottom topographic normalized elevation, , and For the corresponding weight coefficients, and + + =1; When the risk-weighted cost function value of a grid exceeds a preset risk threshold, it is marked as an obstacle grid. When the risk-weighted cost function value of a grid is less than or equal to a preset risk threshold, it is marked as a passable grid.
3. The underwater robot autonomous navigation method based on the improved ant colony algorithm according to claim 1, characterized in that, Step 2 uses the PRM algorithm to randomly sample passable grid cells to generate initial feasible paths, including: Determine the starting and ending points of the underwater robot; The PRM algorithm is used to randomly sample a series of points within the passable grid as potential path nodes. When the distance between two sampling nodes is less than or equal to a preset distance threshold and the line connecting the two sampling nodes does not intersect with the obstacle grid, a connection relationship between the two sampling points is established. Based on the sampling nodes after establishing the connection relationship, a probabilistic route map is constructed, and the initial feasible path of the connection start position and end position is searched.
4. The autonomous navigation method for underwater robots based on the improved ant colony algorithm according to claim 1, characterized in that, The initial pheromone concentration in step 2 is: Wherein, τ0(x,y) represents the initial pheromone concentration of grid (x,y); τ1(x,y) represents the pheromone concentration of grid (x,y) based on the shortest distance to the initial feasible path; τ2(x,y) represents the pheromone concentration of grid (x,y) based on the Euclidean distance to the target point; a and b are the corresponding weight coefficients, and a+b=1; ψ(x,y) represents the terrain risk suppression factor of grid (x,y).
5. The autonomous navigation method for underwater robots based on the improved ant colony algorithm according to claim 1, characterized in that, Step 3, which uses an improved ant colony algorithm to iteratively update the pheromone concentration on the initially feasible path until convergence, includes: The minimum path evaluation value is calculated based on the safety index, energy consumption index, and path tortuosity. An improved ant colony algorithm is used to update the pheromone concentration on the initial feasible path based on the minimum path evaluation value.
6. The autonomous navigation method for underwater robots based on the improved ant colony algorithm according to claim 5, characterized in that, The expression for the minimum path evaluation value is: Where S represents the safety index, E represents the energy consumption index, F represents the path tortuosity, and k1, k2, and k3 are the corresponding weight coefficients, and k1+k2+k3=1.
7. The autonomous navigation method for underwater robots based on the improved ant colony algorithm according to claim 1, characterized in that, The expression for updating the pheromone concentration in step 3 is: in, express Time Path The concentration of pheromones on the surface; Volatility coefficient; Represents the path at time t The increase in pheromone concentration left by ant m, if ant m has traversed the path , = If ant m does not traverse the path Q is the pheromone intensity, P is the minimum path evaluation value, and n is the total number of ants.
8. The autonomous navigation method for underwater robots based on the improved ant colony algorithm according to claim 1, characterized in that, The expression for smoothing the optimized path using a cubic B-spline curve in step 4 is as follows: Where C(φ) represents a cubic B-spline curve; P0, P1, P2, and P3 represent the four control points involved in the curve construction; J 0,3 (φ), J 1,3 (φ), J 2,3 (φ) and J 3,3 (φ) represent the cubic B-spline basis functions of the corresponding control points; φ is the curve parameter.
9. An autonomous navigation system for underwater robots based on an improved ant colony algorithm, characterized in that, include: The acquisition module is used to acquire a near-bottom terrain grid map, and mark impassable areas as obstacle grids and passable areas as passable grids in the grid map; The generation module is used to randomly sample passable grids using the PRM algorithm to generate initial feasible paths and distribute non-uniform initial pheromones on the initial feasible paths. The update module is used to iteratively update the pheromone concentration on the initial feasible path using an improved ant colony algorithm until convergence, and selects the path with the highest pheromone concentration to obtain the optimized path. The optimization module is used to smooth the optimized path using cubic B-spline curves to obtain the autonomous navigation path of the underwater robot, which then navigates according to the planned path.
10. An electronic device for autonomous navigation of an underwater robot based on an improved ant colony algorithm, characterized in that, include: A memory, a processor, and a computer program, the computer program being stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1 to 8.