Unmanned ship global path planning navigation method based on sea chart data

Abstract

The invention relates to an unmanned ship global path planning navigation method based on nautical chart data, which comprises the following steps: firstly, receiving longitudes and latitudes of a starting point and a target point, calculating and expanding a search area, acquiring and processing hindering object information from nautical chart data, then converting coordinates into Mercator XY coordinates and inputting the Mercator XY coordinates into an improved A star algorithm, according to the algorithm, a path is planned through gridding processing and grid boundary expansion considering a safety distance, in path planning, node expansion and collision detection are carried out by adopting a new cost function in combination with a dynamically adjusted cost expansion factor, path safety is ensured, the planned path is simplified, and finally grid path points are converted back to latitude and longitude coordinates, so that the path planning precision is improved. Forming a global navigation path of the unmanned ship; the sea chart data and the intelligent algorithm are effectively combined, an efficient and safe navigation scheme is provided for the unmanned ship, target category information in the sea chart is fully utilized, navigation obstruction data are extracted, a sea safety grid map is constructed, and the accuracy and efficiency of path planning are improved.

Description

Technical Field

[0001] The present invention relates to the field of global path planning for unmanned ships at sea, and in particular to a global path planning and navigation method for unmanned ships based on nautical chart data. Background Art

[0002] With the increase in maritime traffic density and the rapid development of unmanned ship technology, the autonomous navigation capability of unmanned ships in the marine environment has become particularly important. Path planning is one of the key technologies for unmanned ships to achieve autonomous navigation. Its accuracy and efficiency directly affect the navigation safety and operating efficiency of unmanned ships. Traditional unmanned ship path planning methods usually face problems such as insufficient use of chart data, low path planning accuracy, and long time consumption, and fail to fully utilize the characteristics of chart data;

[0003] (1) A method for unmanned ship path planning based on an improved A* algorithm, as disclosed in national patent application number 202210712060.0; comprising the following steps: Step S1: Using the environmental information obtained from the semantic segmentation results to establish a grid map, each grid is marked as a feasible area or an obstacle area, and the starting point B and target point of the path planning are given; Step S2: Introducing the azimuth cost to optimize the cost function f(n), reducing the total search path points and improving the search efficiency; Step S3: Pruning and B-spline curve optimization of the improved A* algorithm path finding results, reducing the total path length while making the driving path smoother; Step S4: Adding a collision detection function to reserve sufficient safety distance between the unmanned ship and the obstacle. The present invention introduces the azimuth cost to optimize the cost function of the traditional A* algorithm, prunes and optimizes the improved A* algorithm path finding results with B-spline curves, reducing the total path length while making the driving path smoother.

[0004] (2) As for the unmanned ship path planning method based on the fusion of Astar and DWA algorithms, the national patent application number 202311870105.8 includes: step S1, establishing a grid map of the unmanned ship's navigation, and setting the starting point and end point of the unmanned ship in the grid map; step S2, using the Astar algorithm, planning a global path from the starting point to the end point, and reducing the number of nodes in the global path through optimization strategies; step S3, using the DWA algorithm, based on the optimized global path, performing dynamic local planning between adjacent nodes, and finally planning an optimal dynamic obstacle avoidance path for the unmanned ship. The present invention can perform overall optimization and local optimization on the global path, and finally plan an optimal dynamic obstacle avoidance path.

[0005] The problems with the above-mentioned patents (1) and (2) are:

[0006] 1. It does not disclose how to obtain environmental information and how to build a grid map: that is, it does not take into account the real nautical chart information;

[0007] 2. The source of the algorithm coordinate system is not considered: the real ocean longitude and latitude coordinate system is not connected. Summary of the invention

[0008] The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and provide a global path planning and navigation method for an unmanned ship based on nautical chart data, which can obtain nautical chart data of the current waters to be navigated, make full use of the category information of each object in the nautical chart, extract obstruction data, construct a maritime safety grid map, and improve the accuracy and efficiency of path planning.

[0009] The technical problem to be solved by the present invention is achieved through the following technical solution: a global path planning and navigation method for an unmanned ship based on nautical chart data, comprising the following steps:

[0010] Step S1: receiving the latitude and longitude information of the starting point S and the target point G of the navigation route issued by the local or remote control software of the unmanned ship;

[0011] Step S2: Based on the latitude and longitude information of the starting point S and the target point G, calculate the square area R with the midpoint of the line connecting the two points as the center point C of the rectangle and the length of the line as the side length, and expand appropriately. The coordinates of the center point C are given by the formula C lon =0.5*(S lon +G lon ) and C lat =0.5*(S lat +G lat ) is calculated, where S lon , S lat is the latitude and longitude coordinates of the starting point S, G lon , G lat is the latitude and longitude coordinates of the target point G, C lon , C lat Represents the latitude and longitude coordinates of the center point C. The side length RL of the square area R is calculated by the formula RL=N*L, where L is the distance between the two points. N is the expansion coefficient, ranging from 1.5 to 2.0;

[0012] Step S3: Access the chart data interface, obtain the chart obstruction data within the square area R, screen the obstructions that affect navigation and classify them into safety levels T, perform linear interpolation on the linear and surface data, ensure that the interval between two points after interpolation is less than the grid resolution RESO, and obtain the obstruction data latitude and longitude coordinate list Obs, which includes the latitude and longitude coordinates and the safety level classification T;

[0013] Step S4: convert the coordinates of the starting point S, the target point G and the obstacle Obs from longitude and latitude to Mercator XY coordinates, and input them into the improved global path planning A-star algorithm;

[0014] Step S5: The A-star algorithm performs grid processing on the starting point, target point and obstacles according to the grid resolution RESO to generate grid coordinates S ij , G ij and Obs ij , while considering the safety distance SafeDis of the unmanned ship to expand the obstacle grid boundary, and retain the highest safety level T;

[0015] Step S6: Use the improved A-star algorithm for global path planning, and perform node expansion and collision detection through the new cost function f(n)=g(n)+(E*r+1)*h(n), where E is the cost expansion factor determined according to the obstacle grid level T around the current node n; r is a constant 0.0-1.0; where g(n) is the actual cost value from the starting node to the current node n, h(n) is the estimated cost value from the current node n to the target node, and E is the cost expansion factor from the current node n to the target node, and its value is:

[0016] E = T, when there are obstacle grids around the current node n, the maximum level of the obstacle grid is T;

[0017] E = 0, there is no obstacle grid around the current node n;

[0018] Step S7: List P of the planned path grid point coordinates ij Simplify the process, detect and adjust the path points through a double loop mechanism to avoid collision with the obstruction grid, and obtain the simplified grid path points PS ij ;

[0019] Step S8: Simplify the grid path points PS ij Convert to actual latitude and longitude coordinates to get the final global navigation path PathLonLat ij .

[0020] Preferably, in step S3, the specific implementation method of linear / surface data interpolation is:

[0021] Calculate the longitude and latitude differences diff_lon and diff_lat of the two points A and B to be interpolated, calculate the interpolation step diff_step according to the grid resolution RESO, and calculate the interpolation number interpolation_number based on this, and then calculate the longitude and latitude coordinates of the intermediate interpolation point.

[0022] Preferably, the cost expansion factor E in the improved A-star algorithm is dynamically adjusted according to whether there is an obstacle grid around the current node n and its safety level T, so as to optimize the path planning result.

[0023] Preferably, the safety level classification T is 1-4, the higher the level, the more dangerous it is, where level 1 includes anchorage areas and deep water areas; level 2 includes land areas and shoreline structures; level 3 includes obstacles and offshore operating areas; level 4 includes reefs / flooded reefs and sunken ships.

[0024] Preferably, in step S3, the access method of the nautical chart data interface is a websocket interface.

[0025] Preferably, in step S6, the global path planning of star A is performed, and when the extended node performs collision detection, it only needs to be compared with the obstacle grid list Obs. ij Checking does not require traversing the entire grid, which improves detection efficiency and finally obtains the coordinate list P of the planned path grid points. ij .

[0026] Preferably, the simplified processing in step S7 adopts a double loop mechanism, and adjusts the path point position by detecting whether the grid passed by the line connecting the current point and the traversal point collides with the obstacle grid, so as to ensure the safety of the path.

[0027] Preferably, in step S7, the planned path grid point coordinate list P is input. ij And the obstruction grid coordinate list Obs ij , using a double loop mechanism, traverse the current point to all the remaining points in turn, and detect whether the path grid formed by the line connecting the current point and the next traversed point collides with the obstacle grid. If so, move the current point to the point before the point and record it. If not, continue to detect the next point until the traversal is completed; finally, the simplified grid path point PS is obtained ij , which contains the path starting point and target point.

[0028] Preferably, in step S8, the simplified grid path points PS obtained in the end are ij , first convert it to the actual Mercator XY coordinates according to the grid resolution, and then convert it to the longitude and latitude form to obtain the final planned global navigation path PathLonLat ij .

[0029] Preferably, a global path planning system for an unmanned ship comprises:

[0030] A data receiving module is used to receive the latitude and longitude information of the starting point S and the target point G;

[0031] A region generation module is used to generate a square region R according to longitude and latitude information;

[0032] The data acquisition and processing module is used to obtain the navigation obstruction data of the nautical chart in the area R and perform screening, classification and interpolation processing;

[0033] Coordinate conversion module, used to convert longitude and latitude coordinates into Mercator XY coordinates;

[0034] Path planning module, including the improved A-star algorithm, for global path planning and collision detection;

[0035] Path simplification module, used to simplify the planned path grid points;

[0036] The coordinate inverse conversion module is used to convert the simplified grid path points into longitude and latitude coordinates to obtain the final global navigation path.

[0037] Compared with the prior art, the beneficial technical effects of the present invention are:

[0038] (1) Make full use of nautical chart information: access the nautical chart interface of the specified area, obtain the navigation obstruction data in the area, filter, classify and interpolate, and restore the real marine environment information;

[0039] (2) Improve path planning efficiency: Use the improved A* algorithm to perform global path search, convert all obstacles into obstacle grid lists, and remove duplicates to reduce the amount of calculation and improve the convergence speed. At the same time, the safety distance of the unmanned ship is considered, and the cost calculation function f(n) is improved to better stay away from obstacles.

[0040] (3) Simplify the number of navigation path points: After obtaining the global path P ij Finally, the path points are simplified and smoothed to reduce unnecessary turning points and obtain the economic benefits of unmanned ship navigation;

[0041] (4) A global path planning system for unmanned ships based on nautical chart data was formed: it was connected to the maritime navigation system, from the coordinates of the starting point and the target point issued to the final planned global navigation path PathLonLat ij They are all in the form of longitude and latitude coordinates, which conforms to the marine navigation data format and control process;

[0042] (5) This method takes the global path planning / navigation of unmanned ships at sea as the research object. By obtaining the nautical chart data of the current waters to be navigated, making full use of the information of various object categories in the nautical chart, extracting the obstruction data, and constructing a maritime safety grid map, the accuracy and efficiency of path planning are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a system flow chart of the present invention;

[0044] Figure 2 Schematic diagram of a rectangular area R calculated according to a starting point S and a target point G in the present invention;

[0045] Figure 3The present invention accesses the nautical chart data interface to obtain the required obstruction latitude and longitude coordinate list Obs;

[0046] Figure 4 This is a schematic diagram of original nautical chart data of a certain sea area in the present invention;

[0047] Figure 5 for Figure 4 The schematic diagram of the original chart data is only interpolated with the encrypted data;

[0048] Figure 6 for Figure 4 Schematic diagram of the original chart data after screening and interpolation of encrypted data;

[0049] Figure 7 This is a schematic diagram of original nautical chart data of another sea area of ​​the present invention;

[0050] Figure 8 for Figure 7 The schematic diagram of the original chart data is only interpolated with the encrypted data;

[0051] Fig. 9 for Figure 7 Schematic diagram of the original chart data after screening and interpolation of encrypted data;

[0052] Fig.10 The grid path point P is planned for a certain sea area in the present invention. ij and the simplified grid path points PS ij Schematic diagram of

[0053] Fig.11 The grid path point P is planned for another sea area in the present invention. ij and the simplified grid path points PS ij Schematic diagram of

[0054] Fig.12 This is a simplified and smoothed schematic diagram of the grid path points of the present invention. DETAILED DESCRIPTION

[0055] The specific technical solutions of the present invention are further described below with reference to the accompanying drawings, so as to facilitate further understanding of the present invention by those skilled in the art, and do not constitute a limitation on the rights thereof.

[0056] Example 1, reference Figure 1-12 ,A global path planning and navigation method for an unmanned ship based on nautical chart data comprises the following steps;

[0057] Step S1: receiving the latitude and longitude information of the starting point S and the target point G of the navigation route issued by the local or remote control software of the unmanned ship;

[0058] Step S2: Based on the latitude and longitude information of the starting point S and the target point G, calculate the square area R with the midpoint of the line connecting the two points as the center point C of the rectangle and the length of the line as the side length, and expand appropriately. The coordinates of the center point C are given by the formula C lon =0.5*(S lon +G lon ) and C lat =0.5*(S lat +G lat ) is calculated, where S lon , S lat is the latitude and longitude coordinates of the starting point S, G lon , G lat is the latitude and longitude coordinates of the target point G, C lon , C lat Represents the latitude and longitude coordinates of the center point C. The side length RL of the square area R is calculated by the formula RL=N*L, where L is the distance between the two points. N is the expansion coefficient, which ranges from 1.5 to 2.0. The specific value can be defined by yourself according to different ocean regions or conditions;

[0059] Step S3: Access the chart data interface. The access method of the chart data interface is the websocket interface. Different interfaces can also be selected according to the chart interface provision method. Obtain the chart obstruction data within the square area R, screen the obstructions that affect navigation and perform safety level classification T, perform linear interpolation on the linear and surface data, ensure that the interval between two points after interpolation is less than the grid resolution RESO, and obtain the obstruction data latitude and longitude coordinate list Obs. Obs contains the latitude and longitude coordinates and the safety level classification T. The safety level classification T is 1-4 levels, and the higher the level, the more dangerous it is. Level 1 includes anchorage areas and deep water areas; Level 2 includes land areas and shoreline structures; Level 3 includes obstacles and offshore operation areas; Level 4 includes reefs / suitable for flooding reefs and sunken ships; It should be noted that the categories covered by the 1-4 levels are not limited to this, and can be customized according to the sea conditions of different sea areas, that is, the target of T can be defined according to one's own needs; the specific level classification and the object mark categories included in each level can be adjusted according to actual needs.

[0060] The specific implementation method of linear / surface data interpolation is to calculate the longitude and latitude difference values ​​diff_lon and diff_lat of the two points A and B to be interpolated, calculate the interpolation step diff_step according to the grid resolution RESO, and calculate the interpolation number interpolation_number based on it, and then calculate the longitude and latitude coordinates of the intermediate interpolation point;

[0061] Step S4: convert the coordinates of the starting point S, the target point G and the obstacle Obs from longitude and latitude to Mercator XY coordinates, and input them into the improved global path planning A-star algorithm;

[0062] Step S5: The A-star algorithm performs grid processing on the starting point, target point and obstacles according to the grid resolution RESO to generate grid coordinates S ij , G ij and Obs ij , while considering the safety distance SafeDis of the unmanned ship to expand the obstacle grid boundary, and retain the highest safety level T;

[0063] Step S6: Perform global path planning for star A. When performing collision detection on the extended node, only the obstacle grid list Obs needs to be checked. ij Checking does not require traversing the entire grid, which improves detection efficiency and finally obtains the coordinate list P of the planned path grid points. ij .

[0064] The improved A-star algorithm is used for global path planning, and the new cost function f(n)=g(n)+(E*r+1)*h(n) is used for node expansion and collision detection, where E is the cost expansion factor determined according to the obstacle grid level T around the current node n; r is a constant 0.0-1.0, and the value of r can be adjusted according to different sea conditions; where g(n) is the actual cost from the starting node to the current node n, h(n) is the estimated cost from the current node n to the target node, and E is the cost expansion factor from the current node n to the target node, and its value is:

[0065] E = T, when there are obstacle grids around the current node n, the maximum level of the obstacle grid is T;

[0066] E = 0, there is no obstacle grid around the current node n, and the cost expansion factor E in the improved A-star algorithm is dynamically adjusted according to whether there is an obstacle grid around the current node n and its safety level T to optimize the path planning result;

[0067] It should be noted that this method only provides a basic technical framework or method overview for dynamic adjustment, and does not list or elaborate on the algorithms used to implement the technical framework or method overview. The dynamic adjustment is roughly divided into three aspects:

[0068] (1) Based on the existence of obstacle grids: If there are obstacle grids around the current node n, E will be assigned a non-zero value (specifically, the safety level T of the obstacle grid) to increase the degree of avoidance of these areas in path planning; conversely, if there are no obstacle grids around, E is usually set to 0, indicating that the path can pass through this area without obstacles;

[0069] (2) Safety level T based on obstacle grids: Different obstacle grids may have different safety levels T, which reflect the degree of impact of obstacles on the navigation safety of the unmanned ship. In dynamic adjustment, the value of E will be adjusted accordingly according to the value of T to reflect the different impacts of different obstacles on path planning. The higher the safety level, the larger the value of E, which means that the planning algorithm will tend to avoid these high-risk areas.

[0070] (3) Real-time: Dynamic adjustment is performed in real time during the algorithm execution, rather than being set once before the algorithm starts. In other words, as the algorithm executes and the current node n changes, the value of E will be continuously adjusted according to the current environment. In most scenarios, the entire process from chart data acquisition and analysis to planning results only takes 1-3 seconds.

[0071] Step S7: List P of the planned path grid point coordinates ij Simplify the process, detect and adjust the path points through a double loop mechanism to avoid collision with the obstruction grid, and obtain the simplified grid path points PS ij ,The simplified processing adopts a double loop mechanism, which adjusts the path point position by detecting whether the grid passed by the line connecting the current point and the traversal point collides with the obstacle grid to ensure the safety of the path;

[0072] Pass in the planned path grid point coordinate list P ij And the obstruction grid coordinate list Obs ij , using a double loop mechanism, traverse the current point to all the remaining points in turn, and detect whether the path grid formed by the line connecting the current point and the next traversed point collides with the obstacle grid. If so, move the current point to the point before the point and record it. If not, continue to detect the next point until the traversal is completed; finally, the simplified grid path point PS is obtained ij , which contains the path starting point and target point;

[0073] Step S8: The simplified grid path points PS ij , first convert it to the actual Mercator XY coordinates according to the grid resolution, and then convert it to the longitude and latitude form to obtain the final planned global navigation path PathLonLat ij .

[0074] It should be noted that when performing global path planning for A-star, we adopted an efficient collision detection strategy to optimize the path planning process. When performing collision detection on the expanded nodes, we do not traverse the entire grid space, but only collide with the pre-screened and gridded obstacle grid list Obs. ij Doing so can significantly improve the efficiency of collision detection, because Obs ijOnly obstacle meshes that actually affect navigation are included, greatly reducing the number of meshes that need to be checked.

[0075] Embodiment 2: An unmanned ship global path planning system, which is used in the unmanned ship global path planning navigation method based on nautical chart data in Embodiment 1, comprising:

[0076] A data receiving module is used to receive the latitude and longitude information of the starting point S and the target point G;

[0077] A region generation module is used to generate a square region R according to longitude and latitude information;

[0078] The data acquisition and processing module is used to obtain the navigation obstruction data of the nautical chart in the area R and perform screening, classification and interpolation processing;

[0079] Coordinate conversion module, used to convert longitude and latitude coordinates into Mercator XY coordinates;

[0080] Path planning module, including the improved A-star algorithm, for global path planning and collision detection;

[0081] Path simplification module, used to simplify the planned path grid points;

[0082] The coordinate inverse conversion module is used to convert the simplified grid path points into longitude and latitude coordinates to obtain the final global navigation path.

Claims

1. A global path planning and navigation method for an unmanned ship based on nautical chart data, characterized in that: The steps include: Step S1: receiving the latitude and longitude information of the starting point S and the target point G of the navigation route issued by the local or remote control software of the unmanned ship; Step S2: Based on the latitude and longitude information of the starting point S and the target point G, calculate the square area R with the midpoint of the line connecting the two points as the center point C of the rectangle and the length of the line as the side length, and expand appropriately. The coordinates of the center point C are given by the formula C lon =0.5*(S lon +G lon ) and C lat =0.5*(S lat +G lat ) is calculated, where S lon , S lat is the latitude and longitude coordinates of the starting point S, G lon , G lat is the latitude and longitude coordinates of the target point G, C lon , C lat Represents the latitude and longitude coordinates of the center point C. The side length RL of the square area R is calculated by the formula RL=N*L, where L is the distance between the two points. N is the expansion coefficient, ranging from 1.5 to 2.0; Step S3: Access the chart data interface, obtain the chart obstruction data within the square area R, screen the obstructions that affect navigation and classify them into safety levels T, perform linear interpolation on the linear and surface data, ensure that the interval between two points after interpolation is less than the grid resolution RESO, and obtain the obstruction data latitude and longitude coordinate list Obs, which includes the latitude and longitude coordinates and the safety level classification T; Step S4: convert the coordinates of the starting point S, the target point G and the obstacle Obs from longitude and latitude to Mercator XY coordinates, and input them into the improved global path planning A-star algorithm; Step S5: The A-star algorithm performs grid processing on the starting point, target point and obstacles according to the grid resolution RESO to generate grid coordinates S ij , G ij and Obs ij , while considering the safety distance SafeDis of the unmanned ship to expand the obstacle grid boundary, and retain the highest safety level T; Step S6: Use the improved A-star algorithm for global path planning, and perform node expansion and collision detection through the new cost function f(n)=g(n)+(E*r+1)*h(n), where E is the cost expansion factor determined according to the obstacle grid level T around the current node n; r is a constant 0.0-1.0; where g(n) is the actual cost value from the starting node to the current node n, h(n) is the estimated cost value from the current node n to the target node, and E is the cost expansion factor from the current node n to the target node, and its value is: E = T, when there are obstacle grids around the current node n, the maximum level of the obstacle grid is T; E = 0, there is no obstacle grid around the current node n; Step S7: List P of the planned path grid point coordinates ij Simplify the process, detect and adjust the path points through a double loop mechanism to avoid collision with the obstruction grid, and obtain the simplified grid path points PS ij ; Step S8: Simplify the grid path points PS ij Convert to actual latitude and longitude coordinates to get the final global navigation path PathLonLat ij .

2. The method for global path planning and navigation of an unmanned ship based on nautical chart data according to claim 1, characterized in that: In step S3, the specific implementation method of linear / surface data interpolation is as follows: Calculate the longitude and latitude differences diff_lon and diff_lat of the two points A and B to be interpolated, calculate the interpolation step diff_step according to the grid resolution RESO, and calculate the interpolation number interpolation_number based on this, and then calculate the longitude and latitude coordinates of the intermediate interpolation point.

3. The unmanned ship global path planning and navigation method based on nautical chart data according to claim 1 or 2, characterized in that: The cost expansion factor E in the improved A-star algorithm is dynamically adjusted according to whether there is an obstacle grid around the current node n and its safety level T to optimize the path planning result.

4. The method for global path planning and navigation of an unmanned ship based on nautical chart data according to claim 3 is characterized in that: The safety level classification T is 1-4, and the higher the level, the more dangerous it is. Level 1 includes anchorage areas and deep water areas; Level 2 includes land areas and shoreline structures; Level 3 includes obstacles and offshore operation areas; Level 4 includes reefs / flooded reefs and sunken ships.

5. The method for global path planning and navigation of an unmanned ship based on nautical chart data according to claim 1, characterized in that: In step S3, the access method of the chart data interface is the websocket interface.

6. The unmanned ship global path planning and navigation method based on nautical chart data according to claim 3 is characterized by: In step S6, the global path planning of star A is performed, and when the extended node is used for collision detection, it only needs to be compared with the obstacle grid list Obs ij Checking does not require traversing the entire grid, which improves detection efficiency and finally obtains the coordinate list P of the planned path grid points. ij .

7. The method for global path planning and navigation of an unmanned ship based on nautical chart data according to claim 1, characterized in that: The simplified processing in step S7 adopts a double loop mechanism, and adjusts the path point position by detecting whether the grid passed by the line connecting the current point and the traversal point collides with the obstacle grid, so as to ensure the safety of the path.

8. The method for global path planning and navigation of an unmanned ship based on nautical chart data according to claim 1, characterized in that: In step S7, the planned path grid point coordinate list P is passed in. ij And the obstruction grid coordinate list Obs ij , using a double loop mechanism, traverse the current point to all the remaining points in turn, and detect whether the path grid formed by the line connecting the current point and the next traversed point collides with the obstacle grid. If so, move the current point to the point before the point and record it. If not, continue to detect the next point until the traversal is completed; finally, the simplified grid path point PS is obtained ij , which contains the path starting point and target point.

9. The method for global path planning and navigation of an unmanned ship based on nautical chart data according to claim 1, characterized in that: In step S8, the simplified grid path points PS ij , first convert it to the actual Mercator XY coordinates according to the grid resolution, and then convert it to the longitude and latitude form to obtain the final planned global navigation path PathLonLat ij .

10. An unmanned ship global path planning system, which is used in any one of the unmanned ship global path planning navigation methods based on nautical chart data in claims 1-8, characterized in that: include; A data receiving module is used to receive the latitude and longitude information of the starting point S and the target point G; A region generation module is used to generate a square region R according to longitude and latitude information; The data acquisition and processing module is used to obtain the navigation obstruction data of the nautical chart in the area R and perform screening, classification and interpolation processing; Coordinate conversion module, used to convert longitude and latitude coordinates into Mercator XY coordinates; Path planning module, including the improved A-star algorithm, for global path planning and collision detection; Path simplification module, used to simplify the planned path grid points; The coordinate inverse conversion module is used to convert the simplified grid path points into longitude and latitude coordinates to obtain the final global navigation path.

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