Low-energy path planning method and system for unmanned boats under the influence of vector field environment

Abstract

The present invention discloses a low-energy path planning method and system for an unmanned vessel under the influence of a vector field environment. The method relates to the field of path planning and includes: obtaining the relative ocean current velocity of a target unmanned vessel based on a current path planning direction and an ocean current velocity model; constructing a step-size adaptive adjustment function based on the ocean current velocity model and the ocean current vorticity model to determine the node expansion step size, thereby determining a new path node and adding it to the path node set; determining an elliptical sampling area based on the path node set using an energy cost function as a cost function based on an Informed-RRT* algorithm; updating the working area to the elliptical sampling area, performing a path expansion iterative process, and obtaining a final path node set, including multiple standby paths consisting of nodes; and selecting a path with the lowest energy cost from the multiple standby paths as the final planned path. The present invention plans a low-energy path for an unmanned vessel under the interference of an ocean current vector field environment.

Description

Technical Field

[0001] The present invention relates to the field of path planning, and in particular to a low-energy-consumption path planning method and system for an unmanned ship under the influence of a vector field environment. Background Art

[0002] Unmanned vessels have gained widespread recognition for their applications in areas such as ocean monitoring and offshore ranching. They can reduce labor costs, improve work efficiency, and even replace humans in dangerous situations. However, due to their size and payload limitations, unmanned vessels cannot carry large-capacity batteries. With the advancement of communication technology, unmanned vessels are operating in increasingly larger areas at sea, making their limited range a prominent issue, significantly reducing their operating time and efficiency. Therefore, how to maximize the power conservation and energy consumption of unmanned vessels during mission execution has become a key issue in unmanned vessel research. Summary of the Invention

[0003] The purpose of the present invention is to provide a low-energy-consumption path planning method and system for an unmanned ship under the influence of a vector field environment, so as to plan a low-energy-consumption unmanned ship path under the interference of an ocean current vector field environment.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] In a first aspect, the present invention provides a low-energy path planning method for an unmanned vessel under the influence of a vector field environment, comprising:

[0006] Establishing a current velocity model and a current vortex model of the unmanned ship in the working area, and then constructing a step-size adaptive adjustment function based on the current velocity model and the current vortex model;

[0007] A random coordinate point is selected within the working area, and then a nearest path node corresponding to the random coordinate point is determined from a set of path nodes; a direction vector from the nearest path node to the random coordinate point is a current path planning direction; the set of path nodes includes at least a starting node and a target node;

[0008] Based on the current path planning direction and the ocean current speed model, adaptively adjusting the speed of the unmanned ship with the goal of minimizing the speed of the unmanned ship relative to the ocean current, to obtain a target speed of the unmanned ship relative to the ocean current;

[0009] Determining a node expansion step size according to the relative current speed of the target unmanned ship and the step size adaptive adjustment function, then determining a new path node according to the node expansion step size and the nearest path node, and adding the new path node to the path node set;

[0010] An energy cost function is constructed according to the relative current speed and draft depth of the target unmanned ship, and then an elliptical sampling area is determined according to the path node set based on the Informed-RRT* algorithm using the energy cost function as a cost function;

[0011] Updating the working area to the elliptical sampling area, and then returning to the step of selecting random coordinate points within the working area to perform path expansion until a preset termination condition is met, thereby obtaining a final path node set; the final path node set includes multiple standby paths consisting of nodes;

[0012] The path with the lowest energy cost is selected from multiple standby paths and determined as the final planned path.

[0013] In a second aspect, the present invention provides a low-energy path planning system for an unmanned vessel under the influence of a vector field environment, comprising:

[0014] A step length adjustment function determination module is used to establish a current velocity model and a current vorticity model of the unmanned ship in the working area, and then construct a step length adaptive adjustment function based on the current velocity model and the current vorticity model;

[0015] a path node determination module, configured to select a random coordinate point within the working area and then determine the nearest path node corresponding to the random coordinate point from a path node set; a direction vector from the nearest path node to the random coordinate point is a current path planning direction; the path node set includes at least a start node and a target node;

[0016] a speed calculation module, configured to adaptively adjust the speed of the unmanned ship based on the current path planning direction and the ocean current speed model, with the goal of minimizing the speed of the unmanned ship relative to the ocean current, to obtain a target speed of the unmanned ship relative to the ocean current;

[0017] a node expansion module, configured to determine a node expansion step size based on the relative ocean current speed of the target unmanned ship and the step size adaptive adjustment function, and then determine a new path node based on the node expansion step size and the nearest path node, and add the new path node to the path node set;

[0018] an elliptical sampling area determination module, configured to construct an energy cost function based on the relative current speed of the target unmanned ship and the draft depth of the unmanned ship, and then determine an elliptical sampling area based on the path node set using the energy cost function as a cost function based on the Informed-RRT* algorithm;

[0019] an iterative expansion module, configured to update the working area to the elliptical sampling area, and then return to the step of selecting random coordinate points within the working area to perform path expansion until a preset termination condition is met, thereby obtaining a final path node set; the final path node set includes multiple standby paths consisting of nodes;

[0020] The planning path determination module is used to select the path with the lowest energy cost from multiple standby paths and determine it as the final planning path.

[0021] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0022] Based on the current path planning direction and the ocean current velocity model, the present invention adaptively adjusts the speed of the unmanned ship with the goal of minimizing its relative ocean current velocity. This results in a target relative ocean current velocity for the unmanned ship. The node expansion step size is then determined using a step-size adaptive adjustment function, and new path nodes are then determined. An energy cost function is constructed based on the target relative ocean current velocity and the unmanned ship's draft depth, allowing the planned path to take into account the effects of ocean current and distance on the unmanned ship's energy consumption and effectively reflect the unmanned ship's energy consumption. This adaptive adjustment of the unmanned ship's relative ocean current velocity based on the ocean current environment minimizes the unmanned ship's energy consumption. Based on the Informed-RRT* algorithm, the energy cost function is used as the cost function. An elliptical sampling area is determined based on the path node set. The energy cost function is then used as the cost function to expand nodes between the starting point and the target point. The resulting planned path also reduces the unmanned ship's energy consumption. Through iteration, a final path node set is obtained, which includes multiple candidate paths consisting of nodes. Finally, the path with the lowest energy cost is selected from the multiple candidate paths and determined as the final planned path. In the present invention, the step size is adaptively adjusted, so that the algorithm is more efficient and can adapt to complex environments more flexibly. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 Schematic diagram of the process of low-energy path planning method for unmanned boat under the influence of vector field environment of the present invention;

[0025] Figure 2 Schematic diagram of the ocean current velocity model of the present invention;

[0026] Figure 3Schematic diagram of the ocean current vorticity model of the present invention;

[0027] Figure 4 A schematic diagram for determining the path nodes of the present invention;

[0028] Figure 5 This is a first schematic diagram of calculating the relative ocean current speed of the target unmanned ship of the present invention;

[0029] Figure 6 A second schematic diagram of calculating the relative ocean current speed of the target unmanned ship of the present invention;

[0030] Figure 7 This is a third schematic diagram of calculating the speed of the target unmanned ship relative to the ocean current of the present invention;

[0031] Figure 8 Schematic diagram for determining the elliptical sampling area of ​​the present invention;

[0032] Figure 9 The simulation of the method of the present invention in the absence of obstacles Figure 1 ;

[0033] Figure 10 The simulation of the method of the present invention in the absence of obstacles Figure 2 ;

[0034] Figure 11 Simulation of the method of the present invention in the presence of obstacles Figure 1 ;

[0035] Figure 12 Simulation of the method of the present invention in the presence of obstacles Figure 2 ;

[0036] Figure 13 Schematic diagram of an algorithm example of the method of the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] The purpose of the present invention is to provide a low-energy-consumption path planning method and system for an unmanned ship under the influence of a vector field environment. The planned path is the result of considering the influence of the ocean current environment. It can use the ocean current to provide a certain amount of power to reduce the energy consumption of the unmanned ship itself, thereby achieving the purpose of extending the endurance; the energy cost function of the unmanned ship is created based on the relative ocean current speed of the unmanned ship calculated according to the combined velocity model. The planned path takes into account the influence of ocean current and distance on the energy consumption of the unmanned ship, and can well reflect the energy consumption of the unmanned ship.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Example 1

[0041] like Figure 1 As shown, the present invention provides a low-energy path planning method for an unmanned ship under the influence of a vector field environment, comprising:

[0042] Step 100: Establish a current velocity model and a current vortex model of the unmanned ship in the working area, and then construct a step size adaptive adjustment function based on the current velocity model and the current vortex model.

[0043] Among them, such as Figure 2 As shown, the size of the unmanned ship working area configuration space is 500km*500km; the direction of the arrow indicates the direction of the current speed; the size of the arrow indicates the size of the current speed, in m / s; the current speed model is:

[0044] v cg (p)=(U p , V p ).

[0045]

[0046] Among them, v cg (p) represents the ocean current velocity at node p; F p (x, y) represents the vector field function of the ocean current at node p; U p (x, y) represents the vector field F at node p p (x, y) is the velocity component in the lateral direction; V p (x, y) represents the vector field F at node p p (x, y) is the velocity component in the longitudinal direction; (x, y) represents the position coordinates of the p node in the working area.

[0047] like Figure 3 As shown, the positive sign indicates that the vorticity is counterclockwise, and the negative sign indicates that the vorticity is clockwise; the arrow indicates the direction of the ocean current speed. The ocean current vorticity model is:

[0048]

[0049] Where curlp(x, y) represents the vorticity of the ocean current at node p.

[0050] According to the ocean current velocity and vorticity model, the step size of the expansion node is adaptively adjusted. The step size adaptive adjustment function is:

[0051]

[0052]

[0053] Among them, customize_step represents the node expansion step, STEP is the fixed step, rov is the ocean current vorticity, v cg represents the speed of ocean current relative to the ground, θ represents the angle of ocean current path; e represents a natural constant.

[0054] Step 200: Select a random coordinate point in the working area, and then determine the nearest path node corresponding to the random coordinate point from the path node set; the direction vector from the nearest path node to the random coordinate point is the current path planning direction; the path node set includes at least the starting node and the target node. Figure 4 As shown, q x1 ,q x2 ,q init are all nodes in the path node set, q rand is a random coordinate point, q nearest is the nearest path node, U rand is the direction vector from the nearest path node to the random coordinate point. When the direction vector is used for subsequent processing, the direction can be used as the current path planning direction. The corresponding direction vector can be expressed as U plan express.

[0055] Step 300 : Based on the current path planning direction and the ocean current velocity model, the unmanned vessel speed is adaptively adjusted with the goal of minimizing the unmanned vessel speed relative to the ocean current, thereby obtaining a target unmanned vessel speed relative to the ocean current. The ocean current velocity model includes the speed of the ocean current relative to the ground.

[0056] Specifically, based on the current path planning direction and the ocean current speed model, the unmanned ship speed is adaptively adjusted with the goal of minimizing the unmanned ship speed relative to the ocean current, to obtain the target unmanned ship speed relative to the ocean current, including:

[0057] (1) Determine the ocean current path angle θ based on the current path planning direction and the ocean current relative ground speed; the ocean current path angle is the angle between the path planning direction and the vector direction corresponding to the ocean current relative ground speed.

[0058] (2) When the ocean current path angle satisfies the first preset condition θ∈[90°, 180°], the target unmanned ship's relative ocean current speed is calculated based on the ocean current speed relative to the ground, the preset minimum speed of the unmanned ship relative to the ground, and the current path planning direction. The calculation formula is: like Figure 5 As shown, at this time, ||v sg ||=V min . Figure 5 In, v sg Indicates the speed of the unmanned ship relative to the ground.

[0059] (3) When the ocean current path angle satisfies the second preset condition θ∈[0°, 90°), the projected speed V of the planned path is calculated based on the ocean current path angle and the ocean current relative ground speed. projection The projected speed of the planned path is the projection of the ocean current relative to the ground speed in the path planning direction, and the calculation formula is:

[0060] V projection =||v cg ×cosθ||.

[0061] (4) When the projected speed of the planned path meets the third preset condition V projection >V min When , the speed of the target unmanned ship relative to the current is calculated according to the current relative ground speed, the planned path projection speed and the current path planning direction. The calculation formula is: like Figure 6 As shown, at this time, ||v sg ||=V projection >V min .

[0062] (5) When the projected speed of the planned path meets the fourth preset condition V projection ≤V min When , the speed of the target unmanned ship relative to the sea current is calculated according to the speed of the sea current relative to the ground, the preset minimum speed value of the unmanned ship relative to the ground and the current path planning direction. The calculation formula is: like Figure 7 As shown, at this time, ||v sg ||=V min >V projection .

[0063] Among them, v scIndicates the speed of the target unmanned ship relative to the current, in m / s; v cg Indicates the speed of ocean current relative to the ground; U plan Is the vector representing the current path planning direction; V min represents the preset minimum speed of the unmanned ship relative to the ground, and || || represents the norm.

[0064] Step 400, determine the node expansion step size according to the relative current speed of the target unmanned ship and the step size adaptive adjustment function, then determine a new path node according to the node expansion step size and the nearest path node, and add the new path node to the path node set.

[0065] The step of determining a new path node according to the node expansion step and the nearest path node specifically includes:

[0066] (1) Based on the nearest path node and the node expansion step, expand along the current path planning direction to obtain the initial path node; Figure 4 As shown, q new ′ represents the initial path node.

[0067] (2) Connecting the nearest path node with the initial path node to obtain an inter-node connection.

[0068] (3) If the connection line between the nodes does not overlap with the obstacles in the working area, the initial path node is marked as a new path node.

[0069] (4) If the connection line between the nodes overlaps with the obstacle in the working area, the node expansion step is reduced using a preset step adjustment function to obtain a new expansion step. The preset step adjustment function is:

[0070]

[0071] Among them, new_step represents the new expansion step, and customize_step represents the node expansion step.

[0072] (5) Based on the nearest path node and the new extension step, the path is expanded along the current path planning direction to obtain a new path node.

[0073] The present invention adaptively adjusts the step size of the expansion node according to the current speed and vorticity model, which enables the algorithm to follow the direction of the current more closely when the current speed is high and the vorticity is low, and to explore the surrounding areas when the current intensity and vorticity are low.

[0074] If an obstacle is encountered during the exploration process, the present invention does not directly abandon the sampling point. Instead, it reduces the current expansion step size according to the corresponding formula and expands it again, making the path planning more efficient. At the same time, when encountering an obstacle, it can increase the probability of exploring the path near the obstacle, so that the planned path can avoid missing narrow areas.

[0075] This method allows the planned path to advance rapidly with larger step sizes in open areas with low vorticity and high current speeds; it enables more detailed exploration with smaller step sizes in areas with obstacles or high vorticity and current speeds. This method effectively improves search efficiency by using different step sizes in different areas to rapidly cover space while maintaining path accuracy and quality. This method combines the rapid search capabilities of long step sizes with the detailed exploration advantages of short step sizes, making the algorithm more flexible and adaptable to complex environments.

[0076] In step 500, an energy cost function is constructed according to the relative current speed of the target unmanned ship and the draft depth of the unmanned ship. Then, based on the Informed-RRT* algorithm, the energy cost function is used as the cost function, and an elliptical sampling area is determined according to the path node set.

[0077] The calculation formula of the energy cost function is:

[0078]

[0079] Among them, C represents the energy cost value; h represents the draft depth of the unmanned ship, v sc represents the speed of the unmanned ship relative to the current; e represents a natural constant; L represents the arc length, q(0) = q init , q(L)=q goal ;q init represents the starting node, q goal Indicates the target node; q: [0, L] is the connection q init and q goal Parameterize continuous curves with arc lengths.

[0080] When the speed of the ocean current relative to the ground approaches 0, Indicates the still water energy consumption of the unmanned boat.

[0081] By establishing an energy cost function, the proposed method allows the planned path to take into account the impact of ocean currents and distance on the energy consumption of the unmanned vessel, effectively reflecting the energy consumption of the unmanned vessel. This adaptive adjustment of the unmanned vessel's speed relative to the ground based on the ocean current environment can minimize the unmanned vessel's energy consumption.

[0082] For the elliptical sampling area, specifically: according to the actual energy cost of each iterative path, the maximum energy cost, the energy cost of each node in the path to the starting node q init and target node qgoal The maximum value of the sum of the distances is used to define the range of the elliptical sampling area in the next Informed-RRT* algorithm, which enables the elliptical area of ​​the algorithm to completely contain the path and according to the actual energy cost C real and the maximum energy cost C max The ratio of determines the size of the sampling area. A larger ratio indicates a less energy-efficient path, and the elliptical sampling area should be expanded to find a more energy-efficient path. A smaller ratio indicates a more energy-efficient path, and the elliptical sampling area should be reduced to accelerate convergence. Limiting the sampling area can improve algorithm efficiency and accelerate convergence, while customizing the sampling area size can prevent the algorithm from reaching a local optimum.

[0083] like Figure 8 As shown, the major axis of the elliptical sampling area can be expressed as the following function:

[0084]

[0085]

[0086]

[0087]

[0088] Among them, Major_Axes represents the major axis of the elliptical sampling area, path_x refers to the xth path; i is the current iteration number; C real is the energy cost corresponding to the entire path; D1 and D2 are the energy costs from point Furthest_Point to point q init and q goal The Euclidean distance. The Furthest_Point is the point in the path where D1+D2 is the largest.

[0089] The minor axis can be expressed as:

[0090] Among them, 2c is the starting node q init and target node q goal The Euclidean distance.

[0091] The present invention adopts the Informed-RRT* algorithm and uses the energy cost function as the cost function to expand the nodes between the starting point and the target point. The planned path can reduce the energy consumption of the unmanned ship.

[0092] In step 600, the working area is updated to the elliptical sampling area, and then the process returns to the step of selecting random coordinate points within the working area to perform path expansion until a preset termination condition is met, thereby obtaining a final path node set; the final path node set includes multiple standby paths consisting of nodes.

[0093] Step 700: Select a path with the lowest energy cost from multiple standby paths and determine it as the final planned path.

[0094] like Figure 9 and Figure 10 As shown, this is a simulation diagram without obstacles. Figure 9 The circle in the middle indicates the starting point of the path, and the triangle indicates the end point of the path. Figure 10 It can be seen that in areas with high current speed and low vorticity, the expansion step is longer, and the planned route basically follows the current; in areas with high current speed and high vorticity, the expansion step is significantly shorter; in areas with low current speed, the path planning is mainly based on Euclidean distance. In addition, the planned path is relatively smooth. Figure 11 and Figure 12 The figure below shows the path planning in the presence of obstacles. It can be seen that the planned path can explore obstacles with a smaller step size and can pass through narrow areas well.

[0095] In another embodiment, Figure 13 As shown in the figure, based on the Informed-RRT* algorithm, a current model and energy cost function are established, and the expansion node step size and the range of the elliptical sampling area are adaptively adjusted to finally plan a low-energy driving path for the unmanned ship. The specific implementation process is as follows:

[0096] 1) Initialize the parameters so that T_Point=0, T_Path=0.

[0097] 2) Establish the ocean current velocity model and ocean current vortex model of the unmanned ship in the working area.

[0098] 3) Number of iterations i=1.

[0099] 4) Determine whether the path is empty; if the path is empty, select a random coordinate point q in the configuration space of the unmanned ship's working area (i.e., the unmanned ship's working area) rand , and find the point with the closest Euclidean distance as the nearest point q nearest .q rand to q nearest The direction vector is U rand .

[0100] 5) Calculate the relative speed of the unmanned ship at the nearest point; specifically, set a minimum value V min, while ensuring the relative ground speed v of the unmanned ship cg Not less than V min Under the premise of Figure 5 、 Figure 6 and Figure 7 As shown in the figure), the speed of the unmanned ship relative to the ocean current is v sc As small as possible to reduce energy consumption. nearest The vector field velocity and vorticity at the point are used to obtain the node expansion step size customize_step.

[0101] 6) Generate a new node and use q nearest As the parent node, if the node connection collides with the obstacle and it is the first collision of the sampling point, the iteration will not be abandoned directly, but the expansion step size will be readjusted, the expansion step size will be reduced and q will be regenerated. new If the sampling point is not the first collision, the iteration is abandoned. It should be noted that the specific number of collisions after which the iteration is abandoned can be set by the relevant personnel.

[0102] 7) If the node connection line does not collide with the obstacle, reset to q new Select the parent node, reconnect, T_Point = {T_Point, q new}.

[0103] 8) Determine q new Whether it is in the target area.

[0104] 9) If q new In the target area, q new The path is put into the set path_i, T_Path = {path_i, T_Path}. Then determine whether i has reached the maximum number of iterations. If so, the path with the lowest energy cost in T_Path is used as the final planned path; if not, then i = i+1, and the actual energy cost and maximum energy cost of each previous iterative path are determined based on the final path. Adaptively adjust the range of the elliptical sampling area in the Informed-RRT* algorithm, and then select a random coordinate point q within the elliptical area. rand ; Find the point with q in the set of all points of the expanded random tree rand The point with the closest Euclidean distance is q nearest ; Then return to step 5) above and continue iterating.

[0105] 10) If q newIf the target is not in the target area, the system determines whether i has reached the maximum number of iterations. If so, the system determines whether the path is empty. If so, path planning fails. If not, the path with the lowest energy cost in T_Path is selected as the final planned path. If i has not reached the maximum number of iterations, the system returns to step 4 above to continue iterating.

[0106] In summary, the present invention has the advantages of low energy consumption, fast convergence speed, short calculation time, reasonable exploration method, and not easy to fall into local optimal solution.

[0107] Example 2

[0108] In order to achieve the same technical effects as in Example 1, the present invention further provides a low-energy path planning system for an unmanned vessel under the influence of a vector field environment, comprising:

[0109] The step length adjustment function determination module is used to establish the ocean current velocity model and the ocean current vortex model of the unmanned ship in the working area, and then construct a step length adaptive adjustment function based on the ocean current velocity model and the ocean current vortex model.

[0110] A path node determination module is used to select a random coordinate point in the working area and then determine the nearest path node corresponding to the random coordinate point from a path node set; the direction vector from the nearest path node to the random coordinate point is the current path planning direction; the path node set includes at least a starting node and a target node.

[0111] The speed calculation module is used to adaptively adjust the speed of the unmanned ship based on the current path planning direction and the ocean current speed model, with the goal of minimizing the speed of the unmanned ship relative to the ocean current, to obtain the target unmanned ship relative to the ocean current speed.

[0112] The node expansion module is used to determine the node expansion step size according to the relative current speed of the target unmanned ship and the step size adaptive adjustment function, and then determine a new path node according to the node expansion step size and the nearest path node, and add the new path node to the path node set.

[0113] The elliptical sampling area determination module is used to construct an energy cost function based on the relative current speed of the target unmanned ship and the draft depth of the unmanned ship, and then determine the elliptical sampling area based on the path node set based on the Informed-RRT* algorithm and the energy cost function as the cost function.

[0114] An iterative expansion module is used to update the working area to the elliptical sampling area, and then return to the step of selecting random coordinate points in the working area to perform path expansion until a preset termination condition is met, thereby obtaining a final path node set; the final path node set includes multiple standby paths composed of nodes.

[0115] The planning path determination module is used to select the path with the lowest energy cost from multiple standby paths and determine it as the final planning path.

[0116] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A low-energy path planning method for an unmanned ship under the influence of a vector field environment, characterized in that the method include: Establishing a current velocity model and a current vortex model of the unmanned ship in the working area, and then constructing a step-size adaptive adjustment function based on the current velocity model and the current vortex model; A random coordinate point is selected within the working area, and then a nearest path node corresponding to the random coordinate point is determined from a set of path nodes; a direction vector from the nearest path node to the random coordinate point is a current path planning direction; the set of path nodes includes at least a starting node and a target node; Based on the current path planning direction and the ocean current speed model, adaptively adjusting the speed of the unmanned ship with the goal of minimizing the speed of the unmanned ship relative to the ocean current, to obtain a target speed of the unmanned ship relative to the ocean current; Determining a node expansion step size according to the relative current speed of the target unmanned ship and the step size adaptive adjustment function, then determining a new path node according to the node expansion step size and the nearest path node, and adding the new path node to the path node set; An energy cost function is constructed according to the relative current speed and draft depth of the target unmanned ship, and then an elliptical sampling area is determined according to the path node set based on the Informed-RRT* algorithm using the energy cost function as a cost function; Updating the working area to the elliptical sampling area, and then returning to the step of selecting random coordinate points within the working area to perform path expansion until a preset termination condition is met, thereby obtaining a final path node set; the final path node set includes multiple standby paths consisting of nodes; The path with the lowest energy cost is selected from multiple standby paths and determined as the final planned path.

2. The low-energy path planning method for an unmanned vessel under the influence of a vector field environment according to claim 1 is characterized in that: The ocean current velocity model is: v cg (p)=(U p ,V p ); Among them, v cg (p) represents the ocean current velocity at node p; F p (x,y) represents the vector field function of the ocean current at node p; U p (x,y) represents the vector field F at node p p (x,y) The velocity component in the horizontal direction; V p (x,y) represents the vector field F at node p p (x,y) is the velocity component in the longitudinal direction; (x,y) represents the position coordinates of the p node in the working area; The ocean current vortex model is: Where curlp(x,y) represents the vorticity of the ocean current at node p.

3. The low-energy path planning method for an unmanned vessel under the influence of a vector field environment according to claim 1 is characterized in that: The ocean current velocity model includes the ocean current relative to the ground velocity; Based on the current path planning direction and the ocean current speed model, the unmanned ship speed is adaptively adjusted with the goal of minimizing the unmanned ship speed relative to the ocean current, to obtain the target unmanned ship speed relative to the ocean current, specifically including: Determining a current path angle based on the current planned path direction and the ocean current relative ground speed; the current path angle is the angle between the path planned direction and the vector direction corresponding to the ocean current relative ground speed; When the ocean current path angle satisfies a first preset condition, calculating the speed of the target unmanned ship relative to the ocean current according to the ocean current speed relative to the ground, a preset minimum speed value of the unmanned ship relative to the ground, and the current path planning direction; When the ocean current path angle satisfies a second preset condition, calculating a planned path projection speed based on the ocean current path angle and the ocean current relative ground speed; the planned path projection speed is a projection of the ocean current relative ground speed in the path planning direction; When the projected speed of the planned path meets a third preset condition, calculating the speed of the target unmanned ship relative to the current according to the speed of the current relative to the ground, the projected speed of the planned path, and the current planned direction of the path; When the projected speed of the planned path meets the fourth preset condition, the speed of the target unmanned ship relative to the current is calculated according to the speed of the current relative to the ground, the preset minimum speed value of the unmanned ship relative to the ground and the current path planning direction.

4. The low-energy path planning method for an unmanned vessel under the influence of a vector field environment according to claim 3 is characterized in that: The first preset condition is θ∈[90°,180°], and the corresponding calculation formula for the target unmanned ship's relative ocean current speed is: The second preset condition is θ∈[0°,90°); The third preset condition is V projection >V min , the corresponding calculation formula for the target unmanned ship's relative ocean current speed is: The fourth preset condition is V projection ≤V min , the corresponding calculation formula for the target unmanned ship's relative ocean current speed is: Where θ represents the angle of the current path; v sc Indicates the speed of the target unmanned ship relative to the current; v cg Indicates the speed of ocean current relative to the ground; U plan Is the vector representing the current path planning direction; V min represents the preset minimum speed of the unmanned ship relative to the ground, |||| represents the norm; V projection Represents the projected velocity of the planned path.

5. The low-energy path planning method for an unmanned vessel under the influence of a vector field environment according to claim 1 is characterized in that: The calculation formula of the energy cost function is: Among them, C represents the energy cost value; h represents the draft depth of the unmanned ship, v sc represents the speed of the unmanned ship relative to the current; e represents a natural constant; L represents the arc length, q(0) = q init , q(L)=q goal ;q init represents the starting node, q goal Indicates the target node; q:[0,L] is the connection q init and q goal Parameterize continuous curves with arc lengths.

6. The low-energy path planning method for an unmanned vessel under the influence of a vector field environment according to claim 1 is characterized in that: The step size adaptive adjustment function is: Among them, customize_step represents the node expansion step, STEP is the fixed step, rov is the ocean current vorticity, v cg represents the speed of ocean current relative to the ground, θ represents the angle of ocean current path; e represents a natural constant.

7. The low-energy path planning method for an unmanned vessel under the influence of a vector field environment according to claim 1 is characterized in that: Determining a new path node according to the node expansion step and the nearest path node specifically includes: Based on the nearest path node and the node expansion step, expanding along the current path planning direction to obtain an initial path node; Connecting the nearest path node with the initial path node to obtain an inter-node connection; If the line between the nodes does not overlap with the obstacles in the working area, marking the initial path node as a new path node; If the connection line between the nodes overlaps with the obstacle in the working area, the node expansion step size is reduced using a preset step size adjustment function to obtain a new expansion step size; Based on the nearest path node and the new extension step, the path is expanded along the current path planning direction to obtain a new path node.

8. The low-energy path planning method for an unmanned vessel under the influence of a vector field environment according to claim 7 is characterized in that: The preset step length adjustment function is: Among them, new_step represents the new expansion step, customize_step represents the node expansion step, e represents the natural constant, v cg represents the speed of ocean current relative to the ground, and θ represents the angle of ocean current path.

9. A low-energy path planning system for an unmanned vessel under the influence of a vector field environment, characterized in that: The system includes: A step length adjustment function determination module is used to establish a current velocity model and a current vorticity model of the unmanned ship in the working area, and then construct a step length adaptive adjustment function based on the current velocity model and the current vorticity model; a path node determination module, configured to select a random coordinate point within the working area and then determine the nearest path node corresponding to the random coordinate point from a path node set; a direction vector from the nearest path node to the random coordinate point is a current path planning direction; the path node set includes at least a start node and a target node; a speed calculation module, configured to adaptively adjust the speed of the unmanned ship based on the current path planning direction and the ocean current speed model, with the goal of minimizing the speed of the unmanned ship relative to the ocean current, to obtain a target speed of the unmanned ship relative to the ocean current; a node expansion module, configured to determine a node expansion step size based on the relative ocean current speed of the target unmanned ship and the step size adaptive adjustment function, and then determine a new path node based on the node expansion step size and the nearest path node, and add the new path node to the path node set; an elliptical sampling area determination module, configured to construct an energy cost function based on the relative current speed of the target unmanned ship and the draft depth of the unmanned ship, and then determine an elliptical sampling area based on the path node set using the energy cost function as a cost function based on the Informed-RRT* algorithm; an iterative expansion module, configured to update the working area to the elliptical sampling area, and then return to the step of selecting random coordinate points within the working area to perform path expansion until a preset termination condition is met, thereby obtaining a final path node set; the final path node set includes multiple standby paths consisting of nodes; The planning path determination module is used to select the path with the lowest energy cost from multiple standby paths and determine it as the final planning path.

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