A hybrid local path planning method for underwater environments

Abstract

The present invention discloses a hybrid local path planning method for underwater environments, belonging to the field of underwater path planning. The method comprises: improving the dynamic window method, the vector graph straight method, and the artificial potential field method; wherein the dynamic window method is improved by using velocity constraints, multi-target trajectory evaluation function reconstruction, and prediction of dynamic obstacle motion intentions; the vector graph straight method is improved by using an adaptive sector resolution method and constraining heading angle changes; the artificial potential field method is improved by considering the influence of water flow on the potential field and introducing a distance attenuation factor and an eddy potential field; the improved dynamic window method, vector graph straight method, and artificial potential field method are integrated and weights are assigned to the improved dynamic window method, vector graph straight method, and artificial potential field method to obtain a dynamic arbitrator; environmental data is input into the dynamic arbitrator to obtain the heading angle and velocity of the underwater robot, thereby completing path planning. By integrating the three algorithms, the method can generate an optimal path.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater path planning, and in particular relates to a hybrid local path planning method for an underwater environment. Background Art

[0002] With the continuous advancement of technology, underwater robots are increasingly being used in fields such as ocean exploration, resource development, marine scientific research, underwater facility operation and maintenance, military reconnaissance, and ecological protection. These robots must perform tasks in complex underwater environments, which places higher demands on their autonomous operation capabilities. Path planning technology is a core component of underwater robot intelligence and autonomy, directly determining their efficiency and safety.

[0003] Currently, there are three main path planning methods for underwater robots: the dynamic window method (DWA), the vector figure straight method (VFH), and the artificial potential field method (APF). These methods each have their own advantages and disadvantages, but all have certain limitations that affect the efficiency and safety of underwater robots.

[0004] The shortcomings of the dynamic windowing algorithm (DWA) include high computational complexity, poor environmental adaptability, and insufficient real-time performance. DWA requires simulating all possible speed combinations in velocity space, resulting in high computational complexity. Furthermore, when the surrounding environment changes, DWA requires replanning the path, which affects the algorithm's real-time performance.

[0005] The shortcomings of the Vector Graph Height (VFH) algorithm include unsmooth paths, difficulty finding the global optimal path, and strong parameter dependence on the environment. The paths generated by the VFH algorithm are often based on a greedy search of the local vector field, sometimes resulting in numerous inflection points and discontinuities. In complex environments, the VFH algorithm is prone to falling into local optimal solutions, making it difficult to find the true global optimal path.

[0006] The drawbacks of the artificial potential field (APF) method include being prone to local optima, unreachable targets, and path oscillation. In complex environments, once a robot reaches a local minimum, it becomes trapped and unable to reach its target. Furthermore, the robot may be unable to reach its target due to excessive repulsive forces or when the attractive and repulsive forces are balanced.

[0007] In order to overcome the limitations of these methods and improve the efficiency and safety of underwater robot path planning, the present invention proposes a hybrid local path planning method for underwater robots. Summary of the Invention

[0008] The present invention proposes a hybrid local path planning method for an underwater environment to solve the problems existing in the above-mentioned prior art.

[0009] To achieve the above object, the present invention provides a hybrid local path planning method for an underwater environment, comprising the following steps:

[0010] Improvements were made to the dynamic window method, vector diagram straight method, and artificial potential field method. The dynamic window method was improved by using velocity constraints, multi-target trajectory evaluation function reconstruction, and prediction of dynamic obstacle motion intentions. The vector diagram straight method was improved by using an adaptive sector resolution method and constraining heading angle changes. The artificial potential field method was improved by considering the impact of water flow on the potential field and introducing a distance attenuation factor and an eddy potential field.

[0011] The improved dynamic window method, vector graph straight method and artificial potential field method are integrated, and weights are assigned to the improved dynamic window method, vector graph straight method and artificial potential field method to obtain a dynamic arbitrator;

[0012] The environmental data is input into the dynamic arbitrator to obtain the heading angle and speed of the underwater robot and complete the path planning.

[0013] Preferably, the improvement of the dynamic window method includes:

[0014] The maximum speed is calculated based on the maximum power of the underwater robot propeller, water density, drag coefficient and flow area, and the speed constraint is set based on the maximum speed;

[0015] A multi-target trajectory evaluation function is constructed based on the distance between the underwater robot and the target point, the distance between the underwater robot and the obstacle, the robot's energy consumption, and the path curvature;

[0016] Predicting the motion intention of dynamic obstacles based on game theory.

[0017] Preferably, the calculation expression of the maximum speed is:

[0018] ;

[0019] Where, is the maximum power of the underwater robot propeller, ρ is the water density, is the drag coefficient, and A is the headwind area.

[0020] Preferably, the calculation expression of the multi-target trajectory evaluation function is:

[0021] ;

[0022] Where, is the distance between the underwater robot and the target point, is the distance between the underwater robot and the obstacle, is the robot energy consumption, is the path curvature, is the weight coefficient of the distance between the underwater robot and the target point, is the weight coefficient of the distance between the underwater robot and the obstacle, is the weight coefficient of the robot’s energy consumption, is the weight coefficient of the path curvature.

[0023] Preferably, the improvement of the vector diagram straight method includes: dynamically adjusting the sector angle according to the obstacle density; and constraining the heading angle change according to the angle threshold.

[0024] Preferably, the improvement of the artificial potential field method includes:

[0025] Construct water flow potential field gradient to guide the robot to navigate downstream;

[0026] A distance attenuation factor is introduced into the repulsion function to make the repulsion of distant obstacles decay rapidly;

[0027] A vortex potential field is introduced to generate a small vortex field at the local minimum point, and the robot is guided out of the local minimum through the rotational force.

[0028] Preferably, the expression of the repulsion function after introducing the distance attenuation factor is:

[0029] ;

[0030] Where, represents the repulsion function, represents the repulsion coefficient, d is the distance between ROV and the obstacle, is the range of repulsive force, and σ is the distance attenuation factor.

[0031] Preferably, obtaining the dynamic arbitrator comprises:

[0032] Calculate the membership degree of the improved dynamic window method, vector graph straight method and artificial potential field method;

[0033] The weight distribution is performed according to the membership degree. If it is a dynamic obstacle environment, the weight of the dynamic window method is increased; if it is a dense obstacle environment, the weight of the vector graph straight method is increased; if it is a sparse obstacle environment, the weight of the artificial potential field method is increased.

[0034] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0035] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0036] Compared with the prior art, the present invention has the following advantages and technical effects:

[0037] The hybrid local path planning method for underwater robots proposed in this paper achieves significant technical results by comprehensively improving three algorithms: the dynamic window method (DWA), the vector graph hand (VFH), and the artificial potential field (APF) method, and designing a dynamic arbitrator to integrate the algorithms. This algorithm not only combines the advantages of each algorithm to generate a smoother and more globally optimal path, but also significantly improves environmental adaptability and real-time performance, enabling the underwater robot to respond more quickly to environmental changes and adjust its path promptly, thereby increasing operational efficiency. Furthermore, the improved algorithm reduces uncertainty and risk in path planning, enhancing the robot's safety during mission execution. The optimized path planning also helps reduce energy consumption, extend the robot's operating time, and improve energy efficiency. By introducing adaptive sector resolution and constraining heading angle changes, this method improves the stability of path planning, reduces path oscillation, and ensures smoother robot motion. Furthermore, by incorporating methods such as vortex potential fields, this method effectively addresses the problem of traditional artificial potential field methods easily falling into local optima, improving the algorithm's ability to find the global optimal solution. In summary, the hybrid local path planning method for underwater robots of the present invention has significant technical advantages in improving the efficiency, safety and adaptability of path planning, and has important theoretical and practical significance for promoting the development and application of underwater robot technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0039] Figure 1 Flowchart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0041] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] Example 1

[0043] like Figure 1As shown, this embodiment provides a method for hybrid local path planning in an underwater environment, including the following steps:

[0044] Improvements were made to the dynamic window method, vector diagram straight method, and artificial potential field method. The dynamic window method was improved by using velocity constraints, multi-target trajectory evaluation function reconstruction, and prediction of dynamic obstacle motion intentions. The vector diagram straight method was improved by using an adaptive sector resolution method and constraining heading angle changes. The artificial potential field method was improved by considering the impact of water flow on the potential field and introducing a distance attenuation factor and an eddy potential field.

[0045] The improved dynamic window method, vector graph straight method and artificial potential field method are integrated, and weights are assigned to the improved dynamic window method, vector graph straight method and artificial potential field method to obtain a dynamic arbitrator;

[0046] Environmental data is input into the dynamic arbitrator to obtain the underwater robot's heading angle and speed to complete path planning. The environmental data includes the distance between the underwater robot and the obstacle, which is collected by side-scan sonar; it also includes the angle information of the obstacle relative to the underwater robot, including the obstacle's azimuth and pitch angle, as well as the robot's own acceleration and angular velocity information.

[0047] Furthermore, the DWA algorithm is improved as follows:

[0048] The traditional DWA algorithm does not take into account the movement of obstacles, which results in a large amount of calculation and affects the real-time performance of the algorithm. To solve this problem, in order to optimize the energy consumption and improve the efficiency of obstacle avoidance, the present invention constrains the speed range according to the propeller power model, narrows the energy consumption perception speed, and reduces the maximum speed. From the formula we can get:

[0049] ;

[0050] Where, is the maximum power of the underwater robot propeller, ρ is the water density, is the drag coefficient, and A is the flow area. This ensures that the ROV moves within the energy consumption constraint.

[0051] Afterwards, based on the particularity of underwater operations, comprehensive energy consumption, safety, and mission urgency, the multi-objective trajectory evaluation function was redesigned. The calculation formula of the multi-objective trajectory evaluation function is as follows:

[0052] ;

[0053] Where, , , , is the weight coefficient, , is the remaining task time, T is the total task time, is the distance to the target point, is the distance to the obstacle, is the robot energy consumption, is the path curvature. The weight of energy consumption Dynamically adjust according to the remaining time of the task. If the remaining time is long, increase , so that path planning pays more attention to energy consumption, thereby controlling the robot speed and reducing the risk of collision; if the remaining time is short, it means the task is urgent and thus reduce , making the algorithm pay more attention to other evaluation criteria.

[0054] Finally, considering that there are often dynamic obstacles such as aquatic animals in the water, such as schools of fish, and that the movement of schools of fish is relatively regular, we use game theory to predict the movement intention of dynamic obstacles. The calculation formula for the predicted obstacle position is as follows:

[0055] ;

[0056] ;

[0057] ;

[0058] Where, is the position vector of the obstacle, is the robot's position vector, is the obstacle acceleration, It is the Nash equilibrium solution obtained from the game between the robot and the obstacle, representing the next predicted acceleration of the obstacle. is the current speed of the obstacle, is the predicted speed of the obstacle after one time step, The position information is updated based on the predicted speed of the obstacle. The candidate trajectory set of the robot is generated based on the predicted obstacle position.

[0059] Furthermore, the VFH algorithm is improved as follows:

[0060] One drawback of the VFH algorithm is that the sector-shaped area division affects the algorithm performance. To address this issue, an adaptive sector resolution method was designed to dynamically adjust the sector angle according to the obstacle density, improving the flexibility of path planning. The formula for adjusting the sector angle is as follows:

[0061] ;

[0062] Where, is the adjusted sector angle, is the basic sector angle, α is the adjustment coefficient, , , Representing the maximum, minimum, and average density, respectively. This allows for reducing the sector angle in densely populated areas to improve resolution, while increasing the sector angle in sparsely populated areas to reduce computational complexity.

[0063] Sometimes the optimal sector and the current sector will have a large angle, which will cause the robot to have sharp turn candidate directions and cause path oscillation. Therefore, the present invention constrains the heading angle change to obtain an optimized safe direction set to improve the smoothness of the path. The calculation formula of the optimized safe direction set is as follows:

[0064] ;

[0065] Where, Represents the optimized set of safe directions, is the heading angle at the current moment, This is the angle change threshold. The angle threshold is used to prevent severe jitter during path planning, making the path smoother and improving the stability of the robot's motion.

[0066] Furthermore, the improvements to APF are as follows:

[0067] In underwater environments, the artificial potential field method not only considers the repulsive force of obstacles on the robot and the gravitational force of the target point on the robot, but also the influence of water flow on the ROV. Therefore, in order to optimize the path energy consumption and reduce upstream navigation, the present invention also regards water flow as a potential field. The formula for constructing the water flow potential field gradient is as follows:

[0068] ;

[0069] In the formula represents the water flow potential gradient, is the water flow potential field coefficient, is the speed of the ROV, The robot is guided to navigate downstream by converting the kinetic energy of the water into a potential field gradient, thereby reducing energy consumption.

[0070] In addition, in order to avoid interference from distant obstacles, the present invention introduces a distance attenuation factor β into the repulsion function. After the distance attenuation factor is introduced, the repulsion function is as follows:

[0071] ;

[0072] Where, represents the repulsion function, represents the repulsion coefficient, d is the distance between ROV and the obstacle, By introducing σ, the repulsive force of distant obstacles can be rapidly attenuated, thus reducing the interference to path planning.

[0073] Finally, in order to solve the problem that APF easily falls into the local minimum, the present invention introduces the vortex potential field , a small vortex field is generated at the local minimum point, and the robot is guided to break away by the rotation force. The calculation formula is as follows:

[0074] ;

[0075] Where, is the vortex field intensity coefficient, r is the vortex field action radius, ( , ) are the coordinates of the local minimum point.

[0076] Furthermore, the dynamic arbitrator integrates the three algorithms as follows:

[0077] 1. Input data: including the local optimal trajectory direction output by DWA and speed , the safe direction of VFH output , the potential field gradient direction output by APF .

[0078] 2. Calculate the confidence of each algorithm: The calculation formula for the DWA algorithm confidence is as follows:

[0079] ;

[0080] Where, is the DWA response time, and λ is the attenuation coefficient. This formula means that the shorter the response time, the higher the confidence of the DWA algorithm.

[0081] The calculation formula of the VFH algorithm confidence is as follows:

[0082] ;

[0083] Where, is the current maximum obstacle density, is the density threshold, exceeding which the current environment is considered complex. This formula indicates that the lower the obstacle density, the higher the confidence of the VFH algorithm.

[0084] The calculation formula of APF algorithm confidence is as follows:

[0085] ;

[0086] Where, represents the total potential field gradient of attraction and repulsion, Represents the gravitational potential field gradient. This formula indicates that the lower the repulsive force ratio, the higher the confidence of the AFH algorithm.

[0087] 3. Dynamic weight allocation: Based on the confidence assessment results, the weights of each algorithm are dynamically allocated. The weight calculation formula is as follows:

[0088] ;

[0089] Where, is the confidence of the ith algorithm, is the historical error change rate of the ith algorithm, is the average error, represents the confidence of the jth algorithm. The weight distribution logic is as follows: if the environment is dynamic, the DWA weight is increased to prioritize real-time obstacle avoidance; if the environment is dense, the VFH weight is increased to prioritize path safety; if the environment is sparse, the APF weight is increased to prioritize path smoothness.

[0090] 4. Final output: The dynamic arbitrator fuses the outputs of each algorithm based on the weights to generate the heading angle and speed of the ROV in the path planning task. The heading angle update formula is as follows:

[0091] ;

[0092] The speed update formula is as follows:

[0093] .

[0094] This embodiment further provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0095] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method are implemented.

[0096] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A hybrid local path planning method for underwater environment, characterized in that: The following steps are involved: Improvements were made to the dynamic window method, vector field histogram method, and artificial potential field method. The dynamic window method was improved by using velocity constraints, multi-target trajectory evaluation function reconstruction, and prediction of dynamic obstacle motion intentions. The vector field histogram method was improved by using an adaptive sector resolution method and constraining heading angle changes. The artificial potential field method was improved by considering the impact of water flow on the potential field and introducing a distance attenuation factor and vortex potential field. The improvement of the dynamic window method includes: The maximum speed is calculated based on the maximum power of the underwater robot propeller, water density, drag coefficient and flow area, and the speed constraint is set based on the maximum speed; A multi-target trajectory evaluation function is constructed based on the distance between the underwater robot and the target point, the distance between the underwater robot and the obstacle, the robot's energy consumption, and the path curvature; Predict the movement intention of dynamic obstacles based on game theory; The improvement of the vector field histogram method includes: dynamically adjusting the sector angle according to the obstacle density; constraining the heading angle change according to the angle threshold; The improvement of the artificial potential field method includes: Construct water flow potential field gradient to guide the robot to navigate downstream; A distance attenuation factor is introduced into the repulsion function to make the repulsion of distant obstacles decay rapidly; A vortex potential field is introduced to generate a small vortex field at the local minimum point, and the robot is guided out of the local minimum through the rotational force; The improved dynamic window method, vector field histogram method and artificial potential field method are integrated, and weights are assigned to the improved dynamic window method, vector field histogram method and artificial potential field method to obtain a dynamic arbitrator. The environmental data is input into the dynamic arbitrator to obtain the heading angle and speed of the underwater robot and complete the path planning.

2. The method according to claim 1, characterized in that The calculation expression of the maximum speed is: ； Where, is the maximum power of the underwater robot propeller, ρ is the water density, is the drag coefficient, and A is the headwind area.

3. The method according to claim 1, characterized in that The calculation expression of the multi-target trajectory evaluation function is: ； Where, is the distance between the underwater robot and the target point, is the distance between the underwater robot and the obstacle, is the robot energy consumption, is the path curvature, is the weight coefficient of the distance between the underwater robot and the target point, is the weight coefficient of the distance between the underwater robot and the obstacle, is the weight coefficient of the robot’s energy consumption, is the weight coefficient of the path curvature.

4. The method according to claim 1, wherein The expression of the repulsion function after introducing the distance attenuation factor is: ； Where, represents the repulsion function, represents the repulsion coefficient, d is the distance between ROV and the obstacle, is the range of repulsive force, Represents the distance decay factor.

5. The method according to claim 1, wherein Obtaining a dynamic arbiter involves: Calculate the membership degree of the improved dynamic window method, vector field histogram method and artificial potential field method; The weight is allocated according to the membership degree. If it is a dynamic obstacle environment, the weight of the dynamic window method is increased; if it is a dense obstacle environment, the weight of the vector field histogram method is increased; if it is a sparse obstacle environment, the weight of the artificial potential field method is increased.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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