An Improved Artificial Potential Field Method for Path Planning on the Deck of Multi-Heterogeneous Unmanned Equipment

Abstract

This invention discloses an improved artificial potential field method for path planning on the surface of multiple heterogeneous unmanned equipment, comprising: initializing the on-site environmental information of the multiple heterogeneous unmanned equipment, determining the starting point coordinates, obstacle coordinates, target point coordinates, maximum influence radius of obstacles, and step size information of the multiple heterogeneous unmanned equipment; setting up a potential field model, adjusting the effects of repulsive and attractive forces in real time by introducing dynamic factor coefficients to control obstacle avoidance during operation of the multiple heterogeneous unmanned equipment, controlling the multiple heterogeneous unmanned equipment to move towards the target point through a gravity function, and adding a longitudinal random factor to the gravity function to dynamically adjust the direction and intensity of gravity; and using the improved artificial potential field method to optimize the path of the multiple heterogeneous unmanned equipment and control the multiple heterogeneous unmanned equipment to move towards the target point.

Description

Technical Field

[0001] This invention belongs to the fields of aviation support technology and intelligent unmanned systems, and proposes an improved artificial potential field method for multi-heterogeneous unmanned equipment surface path planning. Background Technology

[0002] Aircraft carrier decks bear the brunt of numerous tasks, including the takeoff and landing, maintenance, and resupply of carrier-based aircraft. The efficiency and accuracy of these tasks directly impact the fleet's overall combat capability. Against this backdrop, to improve deck operational efficiency and reduce personnel losses, unmanned and intelligent technologies are increasingly being applied to aircraft carrier deck operations. When multiple heterogeneous unmanned systems (such as unmanned carrier-based aircraft and unmanned tractors) move collaboratively, not only planar positions but also the height of these systems must be considered to optimize the utilization of limited deck space. The different heights and volumes of unmanned carrier-based aircraft and unmanned tractors operating on the deck render traditional two-dimensional planar path planning ineffective in resolving conflicts in a three-dimensional dynamic environment. Therefore, developing efficient path planning methods for the collaborative movement of multiple heterogeneous unmanned systems in complex dynamic environments has become a critical issue that urgently needs to be addressed in the fields of aviation support technology and intelligent unmanned systems.

[0003] The aircraft carrier deck is a highly dynamic and densely populated environment. The frequent takeoffs, landings, movements, and parking of carrier-based aircraft place extremely high demands on path planning. Furthermore, various ground support equipment, such as unmanned towing vehicles, unmanned refueling vehicles, and unmanned maintenance vehicles, need to coordinate with unmanned carrier-based aircraft, cooperating synchronously and closely on the deck. Due to significant differences in size, motion characteristics, and functions among these unmanned devices, the complexity of their collaborative operations increases accordingly. For example, unmanned towing vehicles are relatively large and slow, while unmanned carrier-based aircraft require higher maneuverability and flexibility. Therefore, during peak operational periods, high-density collaborative operations involving multiple pieces of equipment occur on the aircraft carrier deck. How to effectively coordinate the three-dimensional path planning of multiple heterogeneous unmanned devices on the deck, effectively solve the problem of planar overlap, improve equipment utilization efficiency, avoid collisions, and increase operational efficiency has become a key technical issue in modern aircraft carrier deck operations.

[0004] Traditional aircraft carrier deck operations typically rely on manual command and real-time monitoring to coordinate the movement of carrier-based aircraft and ground support equipment. This operational mode is inefficient and prone to accidents due to human error. Furthermore, with the increasing prevalence of unmanned systems in future warfare, traditional manual operation methods are clearly insufficient to meet the higher demands for efficiency and safety in future warfare. Therefore, intelligent path planning technology will become a core technological means to solve the problems of efficiency and safety in aircraft carrier deck operations. However, the highly dynamic nature of aircraft carrier decks, the complex environment, and the extremely high timeliness requirements of operations place higher technical standards on existing path planning technologies.

[0005] In the future, unmanned equipment on aircraft carrier decks will consist of various heterogeneous systems. Unmanned carrier-based aircraft will need to perform a series of operations, including takeoff, landing, relocation, parking, and maintenance, while ground support equipment such as unmanned towing vehicles and unmanned refueling vehicles will primarily provide necessary support for the operation of unmanned carrier-based aircraft. The collaborative requirements among these heterogeneous equipment dictate that path planning must consider the parallel processing of multiple objectives and tasks to ensure efficient collaborative operation of various equipment on the deck. For example, when an unmanned towing vehicle tows an unmanned carrier-based aircraft for relocation, it forms a dynamic "consortium," and the path planning problem for this consortium is more complex than that for a single piece of equipment. The consortium occupies a large space on the deck and needs to have flexible obstacle avoidance capabilities to avoid collisions with other unmanned equipment or carrier-based aircraft.

[0006] In such a highly complex dynamic environment, traditional path planning algorithms (such as A) * While algorithms such as A and Djikstra's algorithm can provide good path planning results in static or low-dynamic environments, they show significant shortcomings when dealing with complex environments such as aircraft carrier decks, which are highly dynamic, have multiple obstacles, and contain multiple heterogeneous equipment. Specifically, A * Traditional path planning algorithms, including Djikstra's algorithm, rely on static environment assumptions, making them ill-suited for rapidly responding to frequently changing obstacles and complex interactions between multiple pieces of equipment in dynamic environments. Furthermore, these algorithms typically focus on calculating the globally optimal path for a single piece of equipment, failing to meet the demands for real-time performance, coordination, and efficiency in deck operations. Therefore, traditional path planning algorithms are inefficient in dynamic environments, prone to suboptimal paths or decreased operational efficiency, and unable to adequately guarantee the collaborative operation and dynamic obstacle avoidance requirements of multiple heterogeneous unmanned equipment.

[0007] Currently, path planning technology still faces many challenges in collaborative operations of multiple heterogeneous equipment in dynamic environments. Traditional graph-based path planning methods (such as A) *While algorithms like the Djikstra algorithm are relatively stable in their computation and produce highly deterministic results, they often assume a static or low-complexity environment. Their effectiveness significantly decreases when obstacles in the environment change frequently or when there are diverse equipment types. Furthermore, these algorithms are typically only suitable for path planning of a single piece of equipment and lack sufficient optimization capabilities for the coordinated movement of multiple pieces of equipment. Therefore, to meet the complex and dynamic demands of future aircraft carrier deck operations, more advanced dynamic optimization path planning technologies are needed.

[0008] The Artificial Potential Field (APF) method, a path planning approach based on physical principles, has been widely applied in the fields of obstacle avoidance and path planning for robots in recent years. This method guides unmanned equipment to avoid obstacles and reach the target location by simulating the attractive force of a target point on the equipment and the repulsive force of obstacles. Its advantages include fast computation speed, simple principle, and applicability to path planning in dynamic environments. However, traditional APF methods have limitations when dealing with complex dynamic environments, easily getting trapped in local minima, leading to suboptimal paths. Furthermore, in scenarios such as aircraft carrier decks where multiple heterogeneous unmanned equipment operate collaboratively, APF methods show certain shortcomings in handling multi-target, multi-equipment dynamic obstacle avoidance. Therefore, improving the APF method to enhance its adaptability and robustness in complex dynamic environments will become an important research direction for improving the efficiency of collaborative operations of unmanned equipment. Summary of the Invention

[0009] To address the problems existing in the prior art, this invention discloses an improved artificial potential field method for path planning on the surface of multi-heterogeneous unmanned equipment, specifically including the following steps:

[0010] Initialize the field environment information of multiple heterogeneous unmanned equipment, and determine the starting point coordinates, obstacle coordinates, target point coordinates, maximum influence radius of obstacles, and step size information of multiple heterogeneous unmanned equipment;

[0011] By setting up a potential field model and introducing dynamic factor coefficients to adjust the effects of repulsion and attraction in real time, obstacle avoidance control is achieved for multiple heterogeneous unmanned equipment during operation. The gravity function controls the multiple heterogeneous unmanned equipment to move toward the target point, and a longitudinal random factor is added to the gravity function to dynamically adjust the direction and intensity of gravity.

[0012] An improved artificial potential field method is used to optimize the path of multiple heterogeneous unmanned equipment and control the multiple heterogeneous unmanned equipment to run towards the target point.

[0013] Furthermore, the potential field model includes a repulsive potential energy function, a repulsive function, a gravitational potential energy function, and a gravitational function; the repulsive potential energy function is improved to:

[0014]

[0015] Among them, U rep Let ′ be the potential field function of the repulsive field, and k rep To control the intensity parameters of the repulsive field, R o Let x represent the radius of the obstacle, and x represent the position of the heterogeneous unmanned equipment. o,i Let d represent the position of the i-th obstacle. i (x,x o,i Let d be the distance from the i-th obstacle to the unmanned equipment. o The influence range of the obstacle is represented by α and β, which are added dynamic adjustment factors whose magnitudes are related to d. i (x,x o,i (related to d) i (x,x o,i )≥d o When α is 1, according to the conventional gravitational function: when R o <d i (x,x o,i )<d o Heterogeneous unmanned equipment is in the gravitational transition zone, when d i (x,x o,i )≥R o +d o At that time, heterogeneous unmanned equipment is in a region free from repulsive forces; when d o <d i (x,x o,i ) < R o +d o At that time, heterogeneous unmanned equipment is in the repulsive force transition zone; when d i (x,x o,i )<d o At that time, the velocity direction of the corresponding heterogeneous unmanned equipment changes more, and due to the existence of β, the linear increase in repulsive force will make the obstacle avoidance path smoother.

[0016] Furthermore, the gravitational potential energy function is improved to:

[0017]

[0018] x g Indicates the position of the target point, d(x,x) g ) represents the distance from the heterogeneous unmanned equipment to the target point, when d(x,x) g When ) = 0, it indicates that the heterogeneous unmanned equipment has reached the target point, at which point gravity is 0, where ε is a positive parameter and satisfies ε < d(x) o,j ,x g ).

[0019] Furthermore, a longitudinal random factor is added to the gravitational function to dynamically adjust the gravitational direction, and an XY coordinate system is established. When the gravitational force f... att (X) and repulsive force f rep When (X) are equal, the angle between the gravitational force and the X-axis is obtained. Let the angle between the new gravitational force and the X-axis be γ, then the new gravitational force is:

[0020]

[0021] τ is a longitudinal random factor, and the angle δ between the new gravity and the x-axis is:

[0022]

[0023] So when At that time, the new expression for gravity is:

[0024]

[0025] The magnitude of the longitudinal random factor τ depends only on the angle δ, but it must satisfy:

[0026]

[0027] By adopting the above technical solution, this invention provides an improved artificial potential field method for multi-heterogeneous unmanned equipment ship deck path planning. This method designs a MATLAB simulation program for obstacle avoidance path optimization based on the improved artificial potential field method. Through this program, the path planning effect of multi-heterogeneous unmanned equipment in the complex environment of the ship deck is verified, demonstrating efficient obstacle avoidance path optimization performance and high path planning efficiency, providing important reference value for the practical application of intelligent aviation support.

[0028] This method significantly improves obstacle avoidance success rate, enabling heterogeneous equipment to more easily cope with complex deck environments during path planning, ensuring the safe and stable arrival of multiple heterogeneous unmanned equipment at the target point. Furthermore, this method is adaptable to different types and scales of unmanned equipment, such as unmanned tractors, unmanned carrier-based aircraft, and various ship deck equipment, thereby improving the broad adaptability and universality of path optimization strategies in practical applications. This invention also addresses the problem of traditional artificial potential field methods easily getting trapped in local minima, ensuring that path planning effectively avoids stagnation, thus improving the applicability and reliability of multiple heterogeneous unmanned equipment in complex tasks. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 The flowchart shows the improved artificial potential field method for multi-heterogeneous unmanned equipment surface path planning in this invention.

[0031] Figure 2 This is a flowchart of the algorithm in the path planning method of the present invention;

[0032] Figure 3 This is a schematic diagram of the potential field interaction region in the method of the present invention;

[0033] Figure 4 This is a schematic diagram illustrating the force analysis of heterogeneous unmanned equipment under the improved artificial potential field method in this invention.

[0034] Figure 5 This is a schematic diagram of path planning based on the traditional artificial potential field method in the method of this invention.

[0035] Figure 6 This is a schematic diagram of path planning for a single heterogeneous unmanned equipment on a deck in the method of this invention.

[0036] Figure 7 This is a distance map between the heterogeneous unmanned equipment and obstacles in the method of the present invention.

[0037] Figure 8 This is a schematic diagram of the path planning for multiple heterogeneous unmanned equipment on the deck in the method of the present invention. Detailed Implementation

[0038] To make the technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention:

[0039] like Figure 1 The improved artificial potential field method for multi-heterogeneous unmanned equipment surface path planning includes the following steps:

[0040] S1. Initialize environmental information, determine the starting point coordinates, obstacle coordinates, target point coordinates, maximum influence radius of obstacles, and step size of multiple heterogeneous unmanned equipment;

[0041] S2. Obtain environmental information and construct a virtual force field:

[0042] S21. Set up the potential field model, specifically including defining the repulsive potential field function, repulsive function, gravitational potential field function, and gravitational function, to lay the foundation for path planning;

[0043] S22. Introduction of dynamic factor coefficient: Based on the traditional gravity and repulsion functions, a dynamic factor coefficient is introduced to adjust the effects of repulsion and gravity in real time, so as to ensure that multiple heterogeneous unmanned equipment avoids collisions in path planning and achieves collaborative obstacle avoidance effect.

[0044] S23. Improve the gravity function to solve the problem of unreachable targets: Further optimize the gravity function so that unmanned equipment can overcome the problem of unreachable targets encountered in the traditional artificial potential field method and improve the coverage of path planning;

[0045] S24. Introduce a longitudinal random factor: Add a random factor to the gravity function to dynamically adjust the direction and intensity of gravity, break the local minimum trap, ensure the continuity of the path planning process, and enable unmanned equipment to continuously move toward the target and avoid stagnation.

[0046] S3. Based on the above improvements, the improved artificial potential field method is applied to find paths for multiple heterogeneous unmanned equipment, and the optimal path is obtained through multiple iterations of optimization.

[0047] S31. Calculate the resultant force: At each moment, calculate the resultant force from all attractive and repulsive fields based on the current position of the unmanned equipment (refer to S2).

[0048] S32. By finding the direction of the resultant force using the gradient descent method, the direction of the next movement can be determined.

[0049] Furthermore, in step S1, initializing environmental information specifically includes: the system needs to initialize environmental information, including determining the starting point coordinates (x, y) and target point coordinates (x, y) of the multiple heterogeneous unmanned equipment. g ,y g ), coordinates of the obstacle (x) o ,y o ), radius R of the obstacle o Maximum range of influence d o The number of obstacles, n, and the step size of the multi-heterogeneous unmanned equipment are set to 1. Additionally, the gravity coefficient k needs to be set. att and repulsion coefficient k rep This is to ensure the accuracy and efficiency of path planning.

[0050] In step S2, environmental information is acquired and a virtual force field is constructed.

[0051] S21. The first step in path planning is to establish a potential field model. This involves reading the initial environmental information, constructing a virtual force field, and setting the potential field model. The potential field model includes the repulsive potential field function U.rep repulsive function f rep Gravitational potential field function U att and gravitational function f att This lays the foundation for subsequent route planning.

[0052] The repulsive potential field function is set as follows:

[0053]

[0054] Among them, U rep Let R be the potential field function of the repulsive field, and R be the distance between the heterogeneous unmanned equipment and the obstacle. Let ξ be the safe distance to the i-th obstacle, ξ be the strength parameter of the control repulsive field, and n be the number of obstacles.

[0055] The gravitational potential field function is set as follows:

[0056]

[0057] Among them, U att Let ξ be the potential field function of the gravitational field, d be the distance from the heterogeneous unmanned equipment to the target point, and ξ be the intensity parameter controlling the gravitational field.

[0058] S22. Introduction of dynamic factor coefficient: Based on the traditional gravity and repulsion functions, a dynamic factor coefficient is introduced to adjust the effects of repulsion and gravity in real time, so as to ensure that multiple heterogeneous unmanned equipment avoids collisions in path planning and achieves collaborative obstacle avoidance effect.

[0059] Because traditional artificial potential field methods have many drawbacks, a new gravitational and repulsive potential field function is proposed based on the influence range of gravity and the magnitude of gravity and repulsion. The improvement strategy involves adding a dynamic factor coefficient to the traditional artificial potential field function, enabling heterogeneous unmanned equipment to avoid the aforementioned problems without affecting overall performance, thus achieving a smoother path to the target point.

[0060] First, a new gravitational potential field function is constructed for the gravitational source:

[0061]

[0062] The repulsive potential field function is improved to:

[0063]

[0064]

[0065] Among them, R o d represents the radius of the obstacle. i Let d be the distance from the i-th obstacle to the target point. oThe influence range of the obstacle is represented by α and β, which are added dynamic adjustment factors whose magnitudes are related to d. i (x,x o,i (This is related to) the improved gravitational potential field, when d i (x,x o,i When R is large, α is 1. According to the conventional gravitational function: when R... o <d i (x,x o,i )<d o The heterogeneous unmanned equipment is located in the gravitational transition zone. Due to the change in α, the gravitational effect on the heterogeneous unmanned equipment decreases more rapidly, making it easier to change the velocity direction and magnitude, and thus easier to avoid obstacles. For the new repulsive force function, when d i (x,x o,i When d is large, heterogeneous unmanned equipment is in the region free from repulsive forces; when d o <d i (x,x o,i ) < R o +d o At this time, the heterogeneous unmanned equipment is in the repulsive force transition zone. It can be seen that its influence range is wider than that of traditional repulsive forces, and the velocity direction of the heterogeneous unmanned equipment changes more significantly. Furthermore, due to the presence of β, the linear increase in repulsive force over time also makes the obstacle avoidance path smoother; when d... i (x,x o,i )<d o When acting according to traditional repulsive forces, the specific region of influence of the potential field is as follows: Figure 3 As shown.

[0066] S23. Improve the gravity function to solve the problem of unattainable targets.

[0067] The specific improvements to this gravitational potential field are as follows:

[0068]

[0069] The above formula can be simplified to:

[0070]

[0071] There is a positive parameter ε, and the selection only needs to satisfy ε < d(x). o,j ,x g ), x o,j This represents the coordinates of obstacles that affect the arrival of heterogeneous unmanned equipment at the target point. When d(x,x) g When )=0, it means that the heterogeneous unmanned equipment has reached the target point, and the gravity is also 0 at this time;

[0072] When heterogeneous unmanned equipment moves away from the target

[0073]

[0074] Compared with the traditional gravitational potential field function, the improved new gravitational potential field is stronger; when the heterogeneous unmanned equipment encounters the problem of not being able to reach the target, due to the addition of part of the gravitational field, the resultant force of the heterogeneous unmanned equipment will make it move towards the target and eventually reach the target.

[0075] S24. By adding a random factor to the gravity function, the direction and intensity of gravity are dynamically adjusted to break the local minimum trap, ensuring the continuity of the path planning process and enabling unmanned equipment to continuously move towards the target, avoiding stagnation. For example... Figure 4 As shown, the gravitational force of the target point on the heterogeneous unmanned equipment and the repulsive force of the obstacle reach a balance of 180° to each other. With the addition of a longitudinal random factor, a new gravitational force breaks the balance, causing the heterogeneous unmanned equipment to move towards the target.

[0076] like Figure 4 As shown in the figure, establish the XY coordinate system, when f att (X)=f rep When (X), we can obtain the angle between gravity and the X-axis. Let the angle between the new gravity and the X-axis be δ, where the new gravity is:

[0077]

[0078] τ is a longitudinal random factor, and the angle δ between the new gravity and the x-axis is:

[0079]

[0080] So when At that time, the new expression for gravity is:

[0081]

[0082] The magnitude of the longitudinal random factor τ depends only on the angle δ, but it must satisfy:

[0083]

[0084] In step S3, based on the above improvements, the improved artificial potential field method is applied to find paths for multiple heterogeneous unmanned equipment, so as to achieve safe arrival at the target point.

[0085] S31. Calculate the resultant force: At each moment, calculate the resultant force from all attractive and repulsive fields based on the current position of the unmanned equipment (refer to S2).

[0086] S32. Determine the direction of movement and update the position: Use the gradient descent method to accurately calculate the direction of the resultant force on the unmanned equipment and its corresponding velocity vector, and then iteratively update its path according to the displacement principle to ensure that each step moves along the direction of the fastest decrease in potential energy until the target point is safely reached.

[0087] The target point exerts an attractive force on the unmanned equipment, the strength of which increases as the distance decreases. The gradient of the attractive force of the target point on the unmanned equipment can be expressed as:

[0088]

[0089] Where, k att It is the gravity proportionality coefficient, and x is the position of the unmanned equipment. goal These are the coordinates of the target point for the unmanned equipment.

[0090] Each obstacle exerts a repulsive force on the unmanned equipment (UAV) to prevent it from getting too close. This repulsive force typically employs an exponential decay model, meaning it weakens rapidly with increasing distance. The gradient of the repulsive force on the UAV for each obstacle can be expressed as:

[0091]

[0092] Where, k rep It is the repulsion proportionality coefficient, x o The position coordinates of the obstacle are d(x,x). o ) is the current position x of the unmanned equipment o Distance to the obstacle, d o It is the radius of influence of the obstacle.

[0093] Adding the gradients of all attractive and repulsive forces together gives the total gradient.

[0094]

[0095] To ensure that the unmanned equipment moves along the direction of the fastest decrease in potential energy, the gradient descent method is used to find the direction of the resultant force. That is, along... The direction in which the potential field decreases the fastest, i.e., its negative direction, has a velocity vector v(x) that can be calculated using the following formula:

[0096]

[0097] Where η is the step size, which controls the amount of movement in each iteration.

[0098] Finally, the position of the unmanned equipment is updated based on the velocity vector v(x). This step involves the actual movement and is the final output of the path planning.

[0099] x t+1 =x t +v(x t )·Δt

[0100] Where, x t+1x represents the position of the unmanned equipment at time t+1. t Let t represent the position of the unmanned equipment at time t, and Δt represent the time interval. This update process will be repeated in each time interval until the unmanned equipment reaches the target point and stops.

[0101] Example 1 describes a path planning method for multiple heterogeneous unmanned equipment on a ship deck based on an improved artificial potential field method. This method was used to plan the paths of multiple unmanned equipment on a ship deck for a single unmanned carrier-based aircraft to avoid obstacles, as well as the path planning between multiple heterogeneous unmanned equipment (taking unmanned carrier-based aircraft and unmanned tractor as examples).

[0102] The path planning settings for a single unmanned carrier-based aircraft to avoid multiple unmanned devices on the deck, based on an improved artificial potential field method for multi-heterogeneous unmanned equipment ship deck path planning, are as follows: the position of the unmanned carrier-based aircraft is set as [4m 4m 0m], and the target point position is set as [88m 7m 0m]. An unmanned tractor is set as an obstacle on the line connecting the starting point and the target point. Taking a cube with a radius of 0.5m as an example, the vertical position represents the height of the unmanned tractor. The specific obstacle information is shown in Table 1.

[0103] Table 1 Obstacle Information for Path Planning of a Single Heterogeneous Unmanned Equipment

[0104]

[0105] The path planning settings for multiple heterogeneous unmanned equipment based on an improved artificial potential field method for shipboard path planning are as follows: the starting positions of unmanned shipboard aircraft No. 1 are set to [8m 14m 0m], No. 2 to [18m 3m 0m], No. 3 to [13m 9m 0m], and the starting position of the unmanned tractor is set to [15m 20m 0m]. Their target point positions are [50m 12m 0m], [40m 24m 0m], [60m 6m 0m], and [45m 3m 0m], respectively. The obstacle information is shown in Table 2.

[0106] Table 2 Obstacle Information under Path Planning for Multi-Heterogeneous Unmanned Equipment

[0107]

[0108] Comparative Example

[0109] Comparative Example 1: This invention also compares and contrasts path planning simulation tests based on the traditional artificial potential field method. The path planning simulation settings for the traditional artificial potential field method are as follows, with the starting position set as... [0m 0m] The target point is located at [5m 5m]A cube obstacle with a radius of 1m is placed on the line connecting the starting point and the target point. The coordinates of the obstacle are... [1.5m,1.5m] , [3.5m,3.5m] .

[0110] Comparative Example 1: The path obtained by path planning based on the traditional artificial potential field method is as follows: Figure 5 As shown, the green curve represents the path obtained through path planning based on the traditional artificial potential field method, and the black cubes represent obstacles. Figure 5 As can be seen, the path planning based on the traditional artificial potential field method gets trapped in a local minimum and fails to reach the designated target point. However, unlike Example 1, the path planning in Example 1... Figure 6 In the diagram, the black cubes represent obstacles, symbolizing equipment on the deck (unmanned tractors or the island superstructure). The red curved code represents a path planned based on an improved artificial potential field method, starting from... Figure 6 The diagram showing the path planning effect of a single heterogeneous unmanned equipment on a deck based on the improved artificial potential field method demonstrates that the present invention effectively avoids the drawbacks of the traditional artificial potential field method. Figure 7 In the display, different colors represent the real-time distance between obstacles and the target heterogeneous unmanned equipment at different locations. Figure 7 As shown, the shortest distance between heterogeneous unmanned equipment is greater than the safe distance of 0.5m, effectively avoiding collisions between heterogeneous unmanned equipment during path planning and allowing for manual modification of the safety threshold; from Example 1... Figure 8 In the diagram, black cubes represent obstacles, while other colors represent information about the target heterogeneous unmanned equipment, including its starting point and planned path. Figure 8 As can be seen from the present invention, it reduces the local minimum problem, improves the reliability and safety of obstacle avoidance, can approach the target point more smoothly, reduces oscillation, generates a more optimized path, reduces path interference between multiple UAVs, and improves overall performance.

[0111] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An improved artificial potential field method for path planning on the surface of multi-heterogeneous unmanned equipment, characterized in that... include: Initialize the field environment information of multiple heterogeneous unmanned equipment, and determine the starting point coordinates, obstacle coordinates, target point coordinates, maximum influence radius of obstacles, and step size information of multiple heterogeneous unmanned equipment; By setting up a potential field model and introducing dynamic factor coefficients to adjust the effects of repulsion and attraction in real time, obstacle avoidance control is achieved for multiple heterogeneous unmanned equipment during operation. The gravity function controls the multiple heterogeneous unmanned equipment to move toward the target point, and a longitudinal random factor is added to the gravity function to dynamically adjust the direction and intensity of gravity. An improved artificial potential field method is used to optimize the path of multiple heterogeneous unmanned equipment and control the multiple heterogeneous unmanned equipment to move toward the target point. The potential field model includes a repulsive potential energy function, a repulsive function, a gravitational potential energy function, and a gravitational function; the repulsive potential energy function is improved to: Among them, U rep,i Let ′ be the potential field function of the repulsive field, and k rep To control the intensity parameters of the repulsive field, R o Let x represent the radius of the obstacle, and x represent the position of the heterogeneous unmanned equipment. o,i Let d represent the position of the i-th obstacle. i (x,x o,i Let d be the distance from the i-th obstacle to the unmanned equipment. o The influence range of the obstacle is represented by α and β, which are added dynamic adjustment factors whose magnitudes are related to d. i (x,x o,i (related to d) i (x,x o,i )≥d o When α is 1, according to the conventional gravitational function: when R o <d i (x,x o,i )<d o Heterogeneous unmanned equipment is in the gravitational transition zone, when d i (x,x o,i )≥R o +d o At that time, heterogeneous unmanned equipment is in a region free from repulsive forces; when d o <d i (x,x o,i ) < R o +d o At that time, heterogeneous unmanned equipment is in the repulsive force transition zone; when d i (x,x o,i )<d o At that time, the velocity direction of the corresponding heterogeneous unmanned equipment changes more, and due to the existence of β, the linear increase in repulsive force will make the obstacle avoidance path smoother; By adding a longitudinal random factor to the gravitational function to dynamically adjust the gravitational direction, and establishing an XY coordinate system, when the gravitational force f... att (X) and repulsive force f rep When (X) are equal, the angle between the gravitational force and the X-axis is obtained. Let the angle between the new gravitational force and the X-axis be γ, then the new gravitational force is: τ is a longitudinal random factor, and the angle δ between the new gravity and the x-axis is: So when At that time, the new expression for gravity is: The magnitude of the longitudinal random factor τ depends only on the angle δ, but it must satisfy:

2. The improved artificial potential field method for multi-heterogeneous unmanned equipment surface path planning according to claim 1, characterized in that: The gravitational potential energy function is improved to: x g Indicates the position of the target point, d(x,x) g ) represents the distance from the heterogeneous unmanned equipment to the target point, when d(x,x) g When ) = 0, it indicates that the heterogeneous unmanned equipment has reached the target point, at which point the gravity is 0, where ε is a positive parameter and satisfies ε < d(x, x g ).

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