Ship path planning method and related equipment

Abstract

The embodiment of the invention provides a ship path planning method and related equipment, and belongs to the technical field of ship control. The method comprises the following steps: constructing a preset navigation domain environment model; performing path search through a path node search unit according to the preset navigation domain environment model to obtain global path data; wherein the path node search unit is constructed based on an A * algorithm; performing node screening according to the global path data, and determining a sub-target sequence; performing path planning through a path planning unit according to the sub-target sequence to obtain a target path planning result; wherein the path planning unit is constructed based on potential field optimization and steering optimization of an APF algorithm. According to the embodiment of the invention, the problem of local optimum can be effectively relieved, the reliability of ship path planning is effectively improved, and then support is provided for actual navigation of the ship.

Description

Technical Field

[0001] This application relates to the field of ship control technology, and in particular to a ship path planning method and related equipment. Background Technology

[0002] In recent years, intelligent ships have become an important development direction in the shipping industry. Intelligent ships utilize various sensors and technologies to achieve autonomous navigation, autonomous collision avoidance, and autonomous control, thereby improving shipping safety and efficiency. However, among related technologies, current path planning algorithms for intelligent ships are prone to getting trapped in local optima, resulting in generally low reliability. For example, the generated paths are often non-optimal, making it difficult to provide support for actual ship navigation.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a ship path planning method and related equipment, which can effectively alleviate the local optimum problem, effectively improve the reliability of ship path planning, and thus provide support for actual ship navigation.

[0005] To achieve the above objectives, one aspect of this application proposes a ship route planning method, the method comprising: Construct a pre-defined airspace environment model; Based on the preset airspace environment model, a path search is performed using a path node search unit to obtain global path data; wherein, the path node search unit is constructed based on the A* algorithm; Based on the global path data, node filtering is performed to determine the sub-target sequence; The path planning is performed by a path planning unit based on the sub-target sequence to obtain the target path planning result; wherein the path planning unit is constructed based on potential field optimization and turning optimization of the APF algorithm.

[0006] In some embodiments, the step of performing path search through the path node search unit according to the preset airspace environment model to obtain global path data includes: Obstacle expansion processing is performed based on the preset airspace environment model to obtain the first grid map data; The first raster map data is scaled to obtain the second raster map data; Based on the second grid map data, path planning is performed using the A* algorithm to obtain path node data; The path node data is inversely scaled to obtain the global path data.

[0007] In some embodiments, the step of performing path planning based on the second raster map data using the A* algorithm to obtain path node data includes: Set preset node movement domain parameters; wherein, the preset node movement domain parameters include 16 domain movement spaces; Based on the 16 domain mobile spaces and the second grid map data, the A* algorithm is used to perform path planning to obtain the path node data.

[0008] In some embodiments, the step of filtering nodes based on the global path data to determine the sub-target sequence includes: Traverse the global path data. When it is determined that there are no obstacles between the first and last nodes of any three consecutive path nodes in the path node sequence, connect the first and last nodes and delete the intermediate nodes to obtain the sub-target sequence.

[0009] In some embodiments, before performing path planning based on the sub-target sequence through a path planning unit to obtain the target path planning result, the method further includes: A gravitational potential field model is constructed based on the maximum gravitational force parameter and a preset Euclidean distance parameter; wherein, the preset Euclidean distance parameter includes the Euclidean distance between the ship's position and the gravitational point; A repulsive potential field model is constructed based on preset ship repulsive force parameters; wherein, the preset ship repulsive force parameters include the repulsive force at the front of the ship, the repulsive force at the left front of the ship, and the repulsive force at the right front of the ship; A ship turning angle model is constructed based on the ship's yaw rate parameters, ship's heading parameters, and potential force parameters. Then, dynamic constraints on ship turning are constructed based on the ship turning angle model. The APF algorithm is optimized based on the gravitational potential field model, the repulsive potential field model, and the ship steering dynamic constraints to construct the path planning unit.

[0010] In some embodiments, the step of performing path planning through a path planning unit based on the sub-target sequence to obtain the target path planning result includes: Based on a preset region radius, several sub-target regions are constructed with each path node in the sub-target sequence as the center. The sub-target region is used as the sub-target point of the path planning unit for path planning to obtain the target path planning result.

[0011] To achieve the above objectives, another aspect of this application provides a ship route planning device, the device comprising: The first module is used to construct a preset airspace environment model; The second module is used to perform path search through the path node search unit according to the preset airspace environment model to obtain global path data; wherein, the path node search unit is constructed based on the A* algorithm; The third module is used to filter nodes based on the global path data and determine the sub-target sequence; The fourth module is used to perform path planning based on the sub-target sequence through a path planning unit to obtain the target path planning result; wherein the path planning unit is constructed by performing potential field optimization and turning optimization on the APF algorithm.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, the electronic device comprising: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor performs the method described above.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0014] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method. The embodiments of this application include at least the following beneficial effects: This application provides a ship path planning method, apparatus, electronic device, storage medium, and program product. This solution constructs a preset navigation environment model, and then performs path search based on the preset navigation environment model using a path node search unit constructed using the A* algorithm to obtain global path data. Subsequently, based on the global path data, nodes are filtered to obtain a sub-target sequence. Next, the embodiments of this invention perform path planning based on the sub-target sequence using a path planning unit constructed using potential field optimization and steering optimization of the APF algorithm to obtain the target path planning result, thus realizing ship path planning. It is readily understood that the embodiments of this invention construct a path node search unit based on the A* algorithm to perform path search and filter to obtain a sub-target sequence. Then, using the sub-target sequence as a guide, path planning is performed using a path planning unit constructed using the APF algorithm after potential field optimization and steering optimization. This effectively alleviates the local optimum problem, effectively improves the reliability of ship path planning, and thus provides support for actual ship navigation. Attached Figure Description

[0015] Figure 1This is a flowchart of the ship route planning method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the map zooming process provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the mobile space of a ship in the field 16 provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a three-dimensional repulsive potential field provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the motion of a ship under dynamic constraints provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of ship steering angle calculation provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the sub-target region provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the overall process of ship route planning provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of the path planning result of the traditional A* algorithm provided in the embodiment of the present invention; Figure 10 This is a schematic diagram of the path planning results of the improved A* algorithm provided in the embodiments of the present invention; Figure 11 This is a schematic diagram of the change in ship heading angle provided in an embodiment of the present invention; Figure 12 This is a schematic diagram of the change in ship rudder angle provided in an embodiment of the present invention; Figure 13 This is a schematic diagram of the initial path planning result based on the IPRM algorithm provided in an embodiment of the present invention; Figure 14 This is a schematic diagram of the initial path planning results based on the improved A* algorithm provided in an embodiment of the present invention; Figure 15 This is a schematic diagram of the path planning results based on the IPRM-IAPF algorithm provided in an embodiment of the present invention; Figure 16 This is a schematic diagram of the path planning results based on the AAPF algorithm provided in an embodiment of the present invention; Figure 17 This is a schematic diagram of the heading angle change based on the IPRM-IAPF algorithm provided in an embodiment of the present invention; Figure 18 This is a schematic diagram of heading changes based on the IPRM-IAPF algorithm provided in an embodiment of the present invention; Figure 19 This is a schematic diagram of the heading angle change based on the AAPF algorithm provided in an embodiment of the present invention; Figure 20This is a schematic diagram of heading changes based on the AAPF algorithm provided in an embodiment of the present invention; Figure 21 This is a schematic diagram of the structure of the ship route planning device provided in the embodiments of this application; Figure 22 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0017] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0018] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit the scope of this application.

[0020] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0021] The A* algorithm (A-Star Algorithm) is a heuristic search algorithm used to find the shortest path from the starting point to the target point in a graph. It combines the completeness of Dijkstra's algorithm with the efficiency of the greedy algorithm. By guiding the search direction through a heuristic function, it can significantly reduce the amount of computation.

[0022] APF (Artificial Potential Field) algorithm: It is a classic local path planning algorithm that guides a robot or moving body to avoid obstacles and move toward the target point by simulating the attraction and repulsion forces in a physical potential field, thus realizing the path planning process.

[0023] In recent years, intelligent ships have become an important development direction in the shipping industry. Intelligent ships utilize various sensors and technologies to achieve autonomous navigation, autonomous collision avoidance, and autonomous control, thereby improving shipping safety and efficiency. However, among related technologies, current path planning algorithms for intelligent ships are prone to getting trapped in local optima, resulting in generally low reliability. For example, the generated paths are often non-optimal, making it difficult to provide support for actual ship navigation.

[0024] In view of this, this application provides a ship path planning method, apparatus, electronic device, storage medium, and program product. This solution constructs a preset navigation environment model, and then performs path search based on the preset navigation environment model using path node search units constructed using the A* algorithm to obtain global path data. Subsequently, nodes are filtered based on the global path data to obtain a sub-target sequence. Next, this embodiment of the invention performs path planning based on the sub-target sequence using path planning units constructed based on potential field optimization and steering optimization of the APF algorithm, obtaining the target path planning result and realizing ship path planning. This effectively alleviates the local optimum problem, significantly improves the reliability of ship path planning, and thus provides support for actual ship navigation.

[0025] The ship path planning method provided in this application relates to the field of ship control technology. The ship path planning method provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the ship path planning method, but is not limited to the above forms.

[0026] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0027] Figure 1 This is an optional flowchart of the ship routing method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S110 to S140.

[0028] Step S110: Construct a preset airspace environment model.

[0029] Step S120: Based on the preset airspace environment model, perform path search using the path node search unit to obtain global path data. The path node search unit is constructed based on the A* algorithm.

[0030] Step S130: Filter nodes based on global path data to determine the sub-target sequence.

[0031] Step S140: Based on the sub-target sequence, perform path planning using the path planning unit to obtain the target path planning result. The path planning unit is constructed based on potential field optimization and turning optimization of the APF algorithm.

[0032] In this specific embodiment, the present invention first constructs a preset navigation area environment model, and then performs path search using a path node search unit based on the preset navigation area environment model to obtain global path data. Specifically, the present invention performs environmental modeling based on relevant environmental information of the navigation area where the ship needs to plan its path, such as obstacle information, to obtain a preset navigation area environment model. Correspondingly, the present invention constructs a path node search unit based on the A* algorithm to perform global path planning based on the ship's starting and ending points, combined with the constructed preset navigation area environment model, to generate initial global path data. Next, the present invention performs node filtering based on the global path data to determine a sub-target sequence, and then performs path planning based on the sub-target sequence using a path planning unit to obtain the target path planning result. Specifically, the global path data of the present invention contains nodes of the entire path, which are difficult to use directly as sub-targets. Therefore, the present invention filters each path node in the global path data to optimize and retain key turning nodes, obtaining a sub-target sequence to guide subsequent path planning. Accordingly, this embodiment of the invention constructs a path planning unit based on potential field optimization and steering optimization of the APF algorithm. Potential field optimization alleviates the local optima problem of the APF algorithm, and steering optimization introduces ship steering constraints, ensuring the planned path meets the ship's navigation requirements and improving the reliability of ship path planning. Simultaneously, this embodiment of the invention combines path planning with sub-objective sequences. It uses paths generated based on the A* algorithm to create sub-objective sequences, which are then used as sub-objective points of the path planning unit. This guides the path planning unit away from local optima, thereby generating the target path planning result and providing support for actual ship navigation.

[0033] In some embodiments of the present invention, global path data is obtained by performing path search through a path node search unit based on a preset airspace environment model, including but not limited to the following steps: Obstacle expansion processing is performed based on a preset airspace environment model to obtain the first grid map data.

[0034] The first raster map data is scaled to obtain the second raster map data.

[0035] Path planning is performed using the A* algorithm based on the second grid map data to obtain path node data.

[0036] The path node data is inversely scaled to obtain the global path data.

[0037] In this specific embodiment, such as Figure 2 As shown, this embodiment of the invention first performs obstacle expansion processing based on a preset navigation environment model to obtain first grid map data. Specifically, this embodiment generates first grid map data by expanding the geometric boundaries of obstacles in the preset navigation environment model. For example, this embodiment draws circles with each point on the obstacle boundary as the center and a preset expansion radius (safe distance) as the radius. The envelope of all circles forms the expanded obstacle boundary, thereby reserving a safe distance for ship navigation and avoiding collisions caused by positioning errors, control errors, or dynamic obstacles. Next, this embodiment scales the first grid map data to obtain second grid map data. Specifically, this embodiment scales the high-precision map after obstacle expansion, i.e., the first grid map data, thereby reducing the rasterization degree of the map and reducing the number of grids to obtain second grid map data. For example, this embodiment reduces the pixel density of the raster image in the first grid map data to reduce the complexity of the map. Furthermore, in this embodiment of the invention, path planning is performed using the A* algorithm based on the second raster map data to obtain path node data. Then, the path node data is inversely scaled to obtain global path data. Specifically, this embodiment of the invention plans feasible paths on the scaled map (second raster map data) using the A* algorithm to obtain corresponding path node data. Finally, this embodiment of the invention performs inverse scaling on the planned path nodes to obtain the paths in the high-precision map, i.e., the global path data. This embodiment of the invention optimizes the path planning process of the A* algorithm by employing a scaling strategy, effectively reducing the computation time of path planning and improving path planning efficiency.

[0038] In some embodiments of the present invention, path node data is obtained by path planning using the A* algorithm based on the second grid map data, including but not limited to the following steps: Set the preset node movement domain parameters. These preset node movement domain parameters include 16 domain movement spaces.

[0039] Based on the 16-domain mobile space and the second grid map data, path planning is performed using the A* algorithm to obtain path node data.

[0040] In this specific embodiment, the present invention first sets node movement domain parameters, and then performs path planning using the A* algorithm based on the set preset node movement domain parameters and second grid map data to obtain path node data. Specifically, in this embodiment, the preset node movement domain parameters refer to the movement range parameters of the ship at each node. In this embodiment, the preset node movement domain parameters include 16 domain movement spaces, such as... Figure 3As shown, the ship's movement range at each node is 16 domains. Within these 16 domains, each node has more possible paths. Based on this 16-domain movement space, the embodiment of the present invention uses the A* algorithm to perform path planning processing on the second raster map data, which allows for more flexible path planning, reduces path turning amplitude, and improves path smoothness.

[0041] In some embodiments of the present invention, node filtering is performed based on global path data to determine the sub-target sequence, including but not limited to the following steps: Traverse the global path data. When it is determined that there are no obstacles between the first and last nodes of any three consecutive path nodes in the path node sequence, connect the first and last nodes and delete the intermediate nodes to obtain the sub-target sequence.

[0042] In this specific embodiment, the present invention traverses global path data to determine whether there is an obstacle between the first and last nodes of any three consecutive path nodes in the path node sequence. Specifically, when it is determined that there is no obstacle between the first and last nodes, the first and last nodes are connected, and the intermediate node in these three consecutive path nodes is deleted, thereby generating a sub-target sequence. In order to obtain important turning nodes on the path, the present invention performs trajectory optimization on the planned path, deleting redundant nodes and retaining important turning nodes. For example, the trajectory optimization process in the present invention is as follows: traverse all nodes on the path; if there is no obstacle between node i and node i+2, connect node i and node i+2 and delete node i+1, and so on, traversing the entire path to finally obtain the sub-target sequence.

[0043] In some embodiments of the present invention, before performing path planning based on the sub-target sequence through a path planning unit to obtain the target path planning result, the ship path planning method provided by the embodiments of the present invention further includes, but is not limited to, the following steps: A gravitational potential field model is constructed based on the maximum gravitational force parameter and a preset Euclidean distance parameter. The preset Euclidean distance parameter includes the Euclidean distance between the ship's position and the gravitational point.

[0044] A repulsive potential field model is constructed based on preset ship repulsive force parameters. These preset parameters include the repulsive force directly in front of the ship, the repulsive force to the port front of the ship, and the repulsive force to the starboard front of the ship.

[0045] A ship steering angle model is constructed based on the ship's yaw rate parameters, ship's heading parameters, and potential force parameters. Then, dynamic constraints on ship steering are constructed based on the ship steering angle model.

[0046] The APF algorithm is optimized based on the gravitational potential field model, the repulsive potential field model, and the dynamic constraints of ship turning to construct a path planning unit.

[0047] In this specific embodiment, the present invention first constructs a gravitational potential field model based on the maximum gravitational force parameter and a preset Euclidean distance parameter. Specifically, the preset Euclidean distance parameter in this embodiment includes the Euclidean distance between the ship's position and the gravitational point. To avoid excessive gravity during planning, resulting in the ship being too close to obstacles, the present invention sets a maximum gravitational force parameter... With restrictions imposed, the improved gravitational potential field (i.e., the gravitational field model) is shown in the following equation:

[0048] Where, in the formula This represents the gravitational coefficient, which is a constant. Indicates the ship's position; express and Euclidean distance; Indicates the point of gravity.

[0049] Furthermore, this embodiment of the invention constructs a repulsive potential field model based on preset ship repulsive force parameters. Specifically, the preset ship repulsive force parameters in this embodiment include the repulsive force directly in front of the ship, the repulsive force to the left front of the ship, and the repulsive force to the right front of the ship. Correspondingly, in the traditional APF algorithm, all obstacles are treated as a solid circle, and the repulsive force is calculated with the center of the circle as the center of the obstacle. However, in real-world environments, the shapes of obstacles are varied, and the shape of obstacles also affects the path planning results. Therefore, this embodiment of the invention constructs a three-dimensional repulsive potential field to achieve path planning for ships in complex environments. For example, if the ship's heading... Given the forces, calculate the repulsive forces in the directions directly ahead, to the left, and to the right of the ship, respectively. Figure 4 As shown. The ship will experience a repulsive force from directly in front. Left front repulsive force and right front repulsive force Accordingly, embodiments of the present invention calculate the total repulsive force by calculating the resultant force of these three repulsive forces. This invention provides an example of a method that can more accurately describe the impact of obstacles on ships, improving the accuracy and reliability of path planning. The improved repulsive potential field model is shown in the following equation:

[0050]

[0051] Where, in the formula Indicates the repulsion coefficient; This indicates calculating the gradient; express and The square of the Euclidean distance. Correspondingly, in the formula... hour, These represent the distances from the ship's front, left front, and right front to obstacles, respectively.

[0052] Furthermore, this embodiment of the invention constructs a ship turning angle model based on the ship's yaw rate parameters, heading parameters, and potential field force parameters, and then constructs dynamic constraints on ship turning based on the ship turning angle model. Specifically, due to the characteristics of large inertia and large time delay of ships, their turning is subject to significant dynamic constraints during navigation in water. To ensure the feasibility of actual navigation, the planned path must fully consider the ship's initial heading state and kinematic model. For example, Figure 5 As shown, solid lines represent paths considering dynamic constraints, while dashed lines represent paths without dynamic constraints. It is evident that paths considering dynamic constraints are more realistic. Therefore, this embodiment of the invention imposes restrictions on the ship's steering, assuming the ship's yaw rate is... The formula for calculating the maximum rudder angle of a ship is as follows:

[0053] Where, in the formula This indicates the control interval time. This invention reconstructs the method for selecting the steering angle based on the maximum rudder angle. In this invention, the ship's steering angle is determined by the virtual potential field forces, the ship's current speed, and its heading, such as... Figure 6 As shown, the calculation formula is as follows:

[0054] Where, in the formula Indicates the ship's turning angle. Indicates the ship's current course. Indicates the direction of the force in the potential field. It represents the potential force.

[0055] Next, based on this, in order to ensure that the planned path meets the dynamic constraints of the vessel, the vessel's turning angle is limited to the maximum rudder angle range, and the calculation formula is as follows:

[0056] Furthermore, this embodiment of the invention optimizes the APF algorithm based on the gravitational potential field model, the repulsive potential field model, and ship steering dynamic constraints to construct a path planning unit. Specifically, this embodiment of the invention optimizes the APF algorithm using the gravitational potential field, constructs a three-repulsive potential field, and introduces ship dynamic constraints to construct a path planning unit. This effectively alleviates the local optima problem of the APF algorithm and improves the accuracy and reliability of path planning.

[0057] In some embodiments of the present invention, path planning is performed based on the sub-target sequence through a path planning unit to obtain the target path planning result, including but not limited to the following steps: Based on the preset region radius, several sub-target regions are constructed with each path node in the sub-target sequence as the center.

[0058] By treating the sub-target regions as sub-target points of the path planning unit, path planning is performed to obtain the target path planning result.

[0059] In this specific embodiment, the present invention first constructs several sub-target regions centered on each path node in the sub-target sequence according to a preset region radius. Then, these sub-target regions are used as sub-target points of the path planning unit for path planning, resulting in the target path planning result. Specifically, in complex waters, the APF algorithm is prone to getting trapped in local optima. To address this issue, the present invention uses turning nodes in the global path planned based on the A* algorithm as sub-target points of the APF algorithm, guiding the algorithm away from local optima. In this embodiment, the sub-target node does not require the ship to completely reach or pass through it; rather, it provides the algorithm with an attraction to escape local optima. Therefore, to improve the continuity and smoothness of the path, the present invention constructs a radius of... The system defines sub-target regions, and switches to the next sub-target region whenever the ship reaches a certain sub-target region. For example, in a simulation environment, if the sub-target region length is set to 5, the constructed sub-target regions would look like this: Figure 7 As shown.

[0060] The following section provides a detailed introduction and explanation of the solutions in this embodiment of the invention, using a specific ship routing scenario as an example: For example, such as Figure 8 As shown, Figure 8This is a schematic diagram of the overall process architecture for ship path planning provided in this embodiment of the invention. Specifically, this embodiment first performs environmental modeling of the ship's navigation area and initializes the improved A* algorithm, i.e., the path node search unit. Next, this embodiment performs environmental scaling, then performs path planning using the improved A* algorithm, and performs inverse scaling on the planned path nodes to generate a sub-target sequence. Then, this embodiment combines the ship's initial navigation and the sub-target sequence, and performs path planning using the improved APF algorithm, i.e., the path planning unit, to obtain the target path planning result. It is easy to understand that while the A* algorithm can guarantee finding the shortest path, in ship path planning, this algorithm does not consider the ship's kinematic model, and the generated path is not conducive to ship tracking. Correspondingly, the APF algorithm can generate smoother and more continuous path planning results, but it is prone to getting trapped in local optima. This embodiment of the invention combines the A* algorithm and the APF algorithm to construct the AAPF algorithm, i.e., the ship path planning method provided in this embodiment, which can fully utilize the advantages of both algorithms to improve the efficiency and practicality of path planning. Therefore, this embodiment of the invention integrates the improved A* and improved APF algorithms, using the path planned by the A* algorithm to generate a sequence of sub-targets, which is then used to guide the APF algorithm out of local optima. Simultaneously, to make the path suitable for intelligent ship tracking, this embodiment of the invention considers the ship's dynamic constraints and initial heading to generate a path that conforms to the actual navigation trajectory.

[0061] For example, the present invention conducts a simulation experiment on the improved A* algorithm, setting the map zoom level to 2. The path planning results of the traditional A* algorithm and the improved A* algorithm (path node search unit) are as follows: Figure 9 and Figure 10 As shown. Among them, in Figure 10 In the diagram, the red path represents an optimization of the blue path's trajectory. (Comparison) Figure 9 and Figure 10 It can be seen that the improved A* algorithm plans a path with a larger distance from obstacles and fewer turns. The simulation data for the two A* algorithms are shown in Table 1 below: Table 1

[0062] As shown in Table 1, the improved A* algorithm achieves a shorter path length (reduced by 4.6%) and a 67.1% reduction in search time. While the A* algorithm guarantees the shortest path and is optimal from a distance perspective, it does not fully consider the dynamic navigation performance constraints of intelligent ships, especially the drastic changes in heading angle. Therefore, the ship cannot effectively track this path during navigation.

[0063] Furthermore, to verify the effectiveness of the ship path planning method provided in this embodiment of the invention, this embodiment uses an unmanned surface vessel (USV) kinematic model as the simulation object of the proposed ship path planning method, and conducts comparative simulation experiments and analyses on a real-world map and in a complex environment. Specifically, this embodiment selects the Nomoto model as the manipulation constraint in path planning, and does not consider external interference. The first-order nonlinear Nomoto ship model is described as follows:

[0064] Where, in the formula Represents the ship's rudder angle, This indicates the ship's yaw rate. and Indicates the ship's maneuverability factor. This represents a nonlinear constant. Accordingly, this embodiment of the invention selects relevant unmanned surface vessel (USV) parameters for simulation experiments and uses the fourth-order Runge-Kutta method to solve the nonlinear equations. The USV parameters are shown in Table 2 below: Table 2

[0065] Accordingly, in this embodiment of the invention, the real-world map is rasterized into a 500×500 matrix using MATLAB software. The starting point is set to (400, 50), and the target point is set to (50, 470). The scaling factor of the A* algorithm is set to 5. The gravity factor of the path planning unit is... and repulsive factor Set to 100. Set to 2, range of repulsive force The speed is set to 50. The ship's speed is set to 5 m / s, and the initial heading is... , the maximum rudder angle The initial heading angle is 20 degrees. First, an improved A* algorithm is used to plan the initial path, and then the initial path is optimized to obtain a sequence of sub-objectives. Then, the obtained sub-objective sequence is used as input to the path planning unit, and the improved APF algorithm is used to plan the local path. Finally, a path considering the ship's dynamic constraints and the initial heading angle is obtained. The initial heading angle of the ship at the starting position is... In this embodiment of the invention, the initial heading of the ship is taken into account when planning the route, therefore a curve to the left will appear. Figure 11 and Figure 12 The diagram shows the changes in the ship's heading angle and rudder angle. As can be seen from the diagram, the ship's heading angle at time 0 is 0.5236 radians. The rudder angle is 0 radians. At t=3, the ship reaches its maximum rudder angle of 0.5236 radians, and at other times the rudder angle fluctuates around 0. Combining the path planning results and the above analysis, it can be seen that the ship path planning method provided by this embodiment of the invention takes into account the ship's initial course well, and the planned path conforms to the ship's dynamic constraints, making it more suitable for ship tracking and navigation. It has important theoretical value and practical significance for the development of path planning for intelligent ships.

[0066] Furthermore, this embodiment of the invention verifies the effectiveness and superiority of the ship path planning provided by the present invention through a detailed comparison with the IPRM-IAPF algorithm. The IPRM-IAPF algorithm uses an improved PRM algorithm to solve for sub-target nodes, and then uses the IAPF algorithm for path planning. Accordingly, the environmental simulation parameters are set as follows: the complex map size is 500m × 500m, the starting point coordinates are (30, 30), the target point coordinates are (470, 470), and the ship's speed is set to 5m / s. The simulation parameter settings for the two algorithms are shown in Table 3 below. Table 3

[0067] Accordingly, such as Figure 13 and Figure 14 As shown in Table 4, the initial path planning results of the two algorithms are presented. Table 4

[0068] Among them, the IPRM algorithm and the improved A* algorithm obtained initial paths of similar length, while the improved A* algorithm's running time was only 0.45s, a significant improvement over the IPRM algorithm's 1.72s, demonstrating higher efficiency. The final path planning results of the IPRM-IAPF and AAPF algorithms are shown below. Figure 15 and Figure 16 As shown, both algorithms avoid getting trapped in local optima by utilizing sub-objectives, resulting in smooth paths. According to the data in Table 4, the path planned by the AAPF algorithm is longer than that of the IPRM-IAPF algorithm. This is because the AAPF algorithm fully considers the ship's initial heading angle and dynamic constraints, increasing the path length to some extent. The AAPF algorithm's running time is 0.58s, a 67.05% improvement over the IPRM-IAPF algorithm. By comparing the actual effects of the planned paths, it can be found that although the path planned by the IPRM-IAPF algorithm is also relatively smooth, it is prone to sudden turns in complex obstacle environments, which is not conducive to ship tracking. In contrast, the path planned by the AAPF algorithm provided in this embodiment of the invention has slower turning changes, no large turning points, is smoother, and better reflects the actual navigation conditions of ships. Furthermore, as Figure 17 , Figure 18 , Figure 19 as well as Figure 20 As shown, the ship's heading angle and its change in each iteration were recorded during the path planning process for both algorithms. From the heading angle changes of the two algorithms, it can be seen that the AAPF algorithm results in a relatively gentle change in the heading angle, with a maximum rudder angle of 16.04 degrees. In contrast, the IPRM-IAPF algorithm exhibits a more abrupt change in heading angle, with a significant shift of 35.55 degrees occurring after 40 iterations. Such a rapid change is difficult for a ship to achieve and is unsuitable for ship tracking and control.

[0069] In summary, the ship path planning method proposed in this embodiment of the invention has shown good performance in all stages of ship path planning, which can help intelligent ships plan more reasonable and safe routes in complex environments, thereby improving navigation efficiency and safety.

[0070] Please see Figure 21 This application also provides a ship route planning device that can implement the above-described method. The device includes: The first module 210 is used to construct a preset airspace environment model.

[0071] The second module 220 is used to perform path search using the path node search unit based on a preset airspace environment model to obtain global path data. The path node search unit is constructed based on the A* algorithm.

[0072] The third module 230 is used to filter nodes based on global path data and determine the sub-target sequence.

[0073] The fourth module 240 is used to perform path planning based on the sub-objective sequence through the path planning unit, and obtain the target path planning result. The path planning unit is constructed by optimizing the potential field and turning direction of the APF algorithm.

[0074] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0075] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0076] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0077] Please see Figure 22 , Figure 22 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 310 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 320 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 320 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 320 and is called and executed by the processor 310 using the methods described in the embodiments of this application. Input / output interface 330 is used to realize information input and output; The communication interface 340 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 350 transmits information between various components of the device (e.g., processor 310, memory 320, input / output interface 330, and communication interface 340); The processor 310, memory 320, input / output interface 330 and communication interface 340 are connected to each other within the device via bus 350.

[0078] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0079] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0080] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0081] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0082] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0083] The ship path planning method, apparatus, electronic device, storage medium, and program product provided in this application combine the advantages of the A* algorithm and the APF algorithm in path planning to provide a ship path planning method. First, addressing the problems of low efficiency and large turning points in large maps, a scaling technique and a 16-neighborhood approach are proposed to improve the efficiency of obtaining the initial path. Simultaneously, the initial path obtained by the improved A* algorithm is split into multiple sub-target sequences, guiding the APF algorithm away from local optima. Furthermore, the kinematic model of the ship is considered, resulting in a smoother and more continuous path that conforms to the ship's dynamic constraints. Accordingly, path planning simulations and comparative experiments with the IPRM-IAPF algorithm verify the effectiveness and superiority of the path planning method proposed in this invention. It can plan a smoother and more practical path in a shorter time, effectively considering the ship's dynamic constraints and initial heading. Moreover, in ship autonomous navigation, this method can help intelligent ships effectively plan paths, avoid collisions and dangerous areas, improve navigation safety and heading efficiency, and has broad development prospects in the field of intelligent ships.

[0084] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0085] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0086] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0087] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0088] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0089] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0090] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0091] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0092] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0093] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A ship path planning method, characterized by, The method includes the following steps: Construct a pre-defined airspace environment model; Based on the preset airspace environment model, a path search is performed using a path node search unit to obtain global path data; wherein, the path node search unit is constructed based on the A* algorithm; Based on the global path data, node filtering is performed to determine the sub-target sequence; The path planning is performed by a path planning unit based on the sub-target sequence to obtain the target path planning result; wherein the path planning unit is constructed based on potential field optimization and turning optimization of the APF algorithm.

2. The method of claim 1, wherein, The step of performing path search through the path node search unit based on the preset airspace environment model to obtain global path data includes: Obstacle expansion processing is performed based on the preset airspace environment model to obtain the first grid map data; The first raster map data is scaled to obtain the second raster map data; Based on the second grid map data, path planning is performed using the A* algorithm to obtain path node data; The path node data is inversely scaled to obtain the global path data.

3. The method of claim 2, wherein, The step of performing path planning based on the second raster map data using the A* algorithm to obtain path node data includes: Set preset node movement domain parameters; wherein, the preset node movement domain parameters include 16 domain movement spaces; Based on the 16 domain mobile spaces and the second grid map data, the A* algorithm is used to perform path planning to obtain the path node data.

4. The method according to claim 1, characterized in that, The step of filtering nodes based on the global path data to determine the sub-target sequence includes: Traverse the global path data. When it is determined that there are no obstacles between the first and last nodes of any three consecutive path nodes in the path node sequence, connect the first and last nodes and delete the intermediate nodes to obtain the sub-target sequence.

5. The method according to claim 1, characterized in that, Before performing path planning based on the sub-target sequence through the path planning unit to obtain the target path planning result, the method further includes: A gravitational potential field model is constructed based on the maximum gravitational force parameter and a preset Euclidean distance parameter; wherein, the preset Euclidean distance parameter includes the Euclidean distance between the ship's position and the gravitational point; A repulsive potential field model is constructed based on preset ship repulsive force parameters; wherein, the preset ship repulsive force parameters include the repulsive force at the front of the ship, the repulsive force at the left front of the ship, and the repulsive force at the right front of the ship; A ship turning angle model is constructed based on the ship's yaw rate parameters, ship's heading parameters, and potential force parameters. Then, dynamic constraints on ship turning are constructed based on the ship turning angle model. The APF algorithm is optimized based on the gravitational potential field model, the repulsive potential field model, and the ship steering dynamic constraints to construct the path planning unit.

6. The method according to claim 1, characterized in that, The step of performing path planning based on the sub-target sequence through a path planning unit to obtain the target path planning result includes: Based on a preset region radius, several sub-target regions are constructed with each path node in the sub-target sequence as the center. The sub-target region is used as the sub-target point of the path planning unit for path planning to obtain the target path planning result.

7. A vessel path planning device, characterized by, The device includes: The first module is used to construct a preset airspace environment model; The second module is used to perform path search through the path node search unit according to the preset airspace environment model to obtain global path data; wherein, the path node search unit is constructed based on the A* algorithm; The third module is used to filter nodes based on the global path data and determine the sub-target sequence; The fourth module is used to perform path planning based on the sub-target sequence through a path planning unit to obtain the target path planning result; wherein the path planning unit is constructed by performing potential field optimization and turning optimization on the APF algorithm.

8. An electronic device, comprising: include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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