A path planning method for unmanned boat based on geographic information system and multi-criteria analysis

Abstract

The present invention discloses a method for unmanned boat path planning based on geographic information system and multi-criteria analysis, S1: obtain ocean risk factor data and convert it into ocean risk factor grid data; S2: quantitatively analyze the danger level of each grid in the ocean risk factor grid data; S3: analyze the danger level of each grid and generate a single factor risk field; S4: determine the weight of the ocean risk factor data and generate an ocean environment risk field; S5: based on the ocean environment risk field and using an improved A* algorithm, the safest unmanned boat navigation path is planned. The present invention effectively solves the efficiency and safety problems that may be encountered when unmanned boats perform path planning in complex sea areas. Through this technology, more accurate and safe route planning can be provided for unmanned boats, ensuring their navigation safety in complex marine environments, thereby promoting the development of the marine economy and the construction of a strong marine nation.

Description

Technical Field

[0001] The invention relates to the technical field of unmanned boat path planning, and in particular to an unmanned boat path planning method based on geographic information system and multi-criteria analysis. Background Art

[0002] Unmanned boats have shown great potential in many marine fields such as port patrol, maritime rescue and resource exploration due to their small size, shallow draft, easy deployment and high maneuverability. Path planning, as one of the core technologies for autonomous navigation of unmanned boats, directly affects their navigation efficiency, safety and task completion quality. However, most algorithms only focus on the shortest path or the lowest energy consumption when planning paths, often ignoring safety, and the planned path results are prone to fall into the problem of local optimal solutions. In addition, the complexity of the marine environment is often ignored. Unmanned boats are easily affected by water depth and shallow waters during navigation, increasing the risk of grounding and collision. At present, there are some deficiencies in the research on environmental risk factors and path planning algorithms for unmanned boat path planning in complex sea areas. For example, for environmental risk factors, existing studies usually simplify or ignore marine environmental characteristics and potential risk factors, and fail to fully consider the dangers and safety issues brought by the real marine environment, resulting in inaccurate and incomplete path planning results. For path planning algorithms, existing studies mostly use path length as an evaluation function, or only consider single factors such as minimum energy consumption, without fully considering safety issues during navigation, which causes the planned path to be too close to the edge of the non-navigable area, thereby increasing the risk of collision. Summary of the invention

[0003] The present invention provides an unmanned boat path planning method based on geographic information system and multi-criteria analysis, so as to overcome the technical problems that the prior art simplifies or ignores the characteristics of the marine environment and potential risk factors, fails to fully consider the dangers and safety issues brought by the real marine environment, and leads to inaccurate and incomplete path planning results.

[0004] In order to achieve the above object, the technical solution of the present invention is: A method for unmanned boat path planning based on geographic information system and multi-criteria analysis, the specific steps include: S1: Acquire marine risk factor data based on a geographic information system, pre-process the marine risk factor data, and obtain marine risk factor raster data; Said ocean risk factor data include ocean waves, ocean currents, ocean winds and water depth; S2: quantitatively analyzing the danger level of each grid in the marine risk factor grid data; S3: Analyze the risk level of each grid by using a single factor risk measurement method and a spatial analysis method to generate a single factor risk field; S4: Determine the weights of marine risk factor data based on a multi-criteria analysis method, and generate a marine environmental risk field based on the weights and the single factor risk field; S5: Based on the marine environmental risk field and using an improved A* algorithm, the safest unmanned boat navigation path is planned.

[0005] Further, in S2, the danger level of each grid in the marine risk factor grid data is quantitatively analyzed, and the formula for the quantitative analysis includes: Wave risk The quantitative formula is: (9) in, W is the actual wave height of a single grid cell, W min is the lowest dangerous value for waves; Current risk The quantitative formula is: (10) in, Y is the actual flow velocity of a single grid cell, Y min is the lowest risk value for rapids; Sea breeze risk The quantitative formula is: (11) in, Q is the actual wind speed value of a single grid cell, Q min It is the lowest dangerous value for strong winds; Water depth danger The quantitative formula is: (12) in, D For draft; C For the surplus water depth; H w For wave height.

[0006] Further, in S4, the weights of the marine risk factor data are determined based on the hierarchical analysis method in the multi-criteria analysis method, and the marine environmental risk field is generated based on the weights and the single factor risk field, including: S41: Using the comparison matrix of the two-by-two comparisons between the indicators in equation (1) A , and based on expert advice, determine the factors i Relative to factors j The relative importance of the factors is calculated according to the scale values ​​in the set Satty scale table. aij Assignment, the comparison matrix A It is expressed as: (1) Comparison Matrix A The following conditions must be met: , in, a ij Indication factors i Relative to factors j the importance of m is the number of evaluation factors; a ii Indication factors i Relative to their own importance, the factors i , j for waves, currents, winds or water depth; S42: Judgment Matrix A Each column element of is normalized, and the processing formula is: (3) S43: Calculate the sum of the elements of each row of the normalized judgment matrix, expressed as: (4) S44: Yes Normalize it and get i The weight vector of marine risk factor data is: (5) S45: Calculate the maximum eigenvalue , expressed as: , where is the judgment matrix A With the weight vector No. i The sum of products of elements; S46: Pass consistency ratio CR The value of the verification comparison matrix A Consistency, consistency ratio CR The calculation formula is: (6) Among them, the consistency index CI for: (7) in, q is the order of the judgment matrix; RI is the random consistency index value; S47: According to CR The value of the judgment matrix A Is there consistency? CR ≥0.1, then the judgment matrix is ​​reconstructed based on the content of S41 A , and repeat S42-S46. If CR <0.1, execute S48; S48: Calculate the comprehensive risk value of each grid in turn according to the weight and the single factor risk field, so as to generate a marine environmental risk field. The calculation formula is: ,in, For the i A single factor risk field with a factor in a single grid; R It is the comprehensive risk value of a single grid, ranging from 0 to 1.

[0007] Furthermore, in S5, based on the marine environmental risk field and using the improved A* algorithm, the safest unmanned boat navigation path is planned, including: S51: Use the improved A* algorithm as the path evaluation function to evaluate the path from the starting point to the end point in the marine environmental risk field n Total cost , expressed as: ,in, It is from the starting point to the end point in the running environment n the actual cost of is the weight of the path length, is the weight of path safety, It is the sum of the risk costs; The Euclidean distance is used as the estimation function of the improved A* algorithm. When searching, the current node moves in its eight adjacent directions to obtain the distance from the current node to the end point. n Estimated distance , expressed as: ,in, is the coordinate of the target point, is the coordinate of the current node; S52: Introduce safety distance constraints, including: Assume that the current node is , the neighbor nodes to be expanded are , the obstacle position is , then the distance from the neighbor node to be expanded to the nearest obstacle It is expressed as: , , in, Represents the neighbor node x coordinate, Represents the neighbor node y coordinate, Indicates the safety distance threshold; S53: After searching the end point according to the improved A* algorithm and the safety distance constraint condition, n Backtrack to the parent node chain to generate the initial path; S54: For the three consecutive nodes in the initial path , and Perform a collinearity check operation to obtain the planned path; S55: Using a cubic Bezier curve method to smooth the planned path, the formula is: ,in, , , and It is the key position point used to determine the shape and direction of the curve; t Indicates a parameter.

[0008] Further, in S54, three consecutive nodes, and in the initial path are sequentially subjected to a collinearity check operation, and the planned path obtained includes: Check the three consecutive nodes in the initial path one by one , and Are they collinear? If so, execute 1); otherwise, execute 2) to finally obtain the planned path; 1) If they are collinear, then: , Defining vectors and for: , , then the vector and Collinear, because and There are public spots B ,so A , B , C Three points on a line, indicating a node B If it is a redundant node, delete the redundant nodes on the same line. B ; 2) If they are not collinear, then: , Perform a safety distance check on the distance between the current node and the obstacle. The detection formula is: ,in, P It is a straight line Any point on are the coordinates of the obstacle; If the distance between the current node and the obstacle meets the safety distance, and A, B, C The three points satisfy the following formula, indicating The path length is less than , then delete the redundant inflection points B , the formula is: .

[0009] Furthermore, in S3, the spatial analysis method includes resampling, raster calculation and Kriging interpolation methods.

[0010] Beneficial effects: The present invention can identify various factors that affect navigation safety by quantitatively analyzing the danger level of each grid in the marine risk factor grid data, and analyze the danger level of each grid through a single factor risk measurement method and a spatial analysis method to generate a single factor risk field; the weight of the marine risk factor data is determined based on a multi-criteria analysis method, and a marine environmental risk field is generated based on the weight and the single factor risk field, thereby realizing the visualization of the risk factor area and ensuring a better understanding and prediction of the risk situation in the navigation area. At the same time, the present invention uses an improved A* algorithm to plan the safest unmanned boat navigation path, which helps decision makers identify and avoid potential threats in advance, not only providing navigation information and auxiliary decision support for ships, but also improving the efficiency and safety of unmanned boat path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0012] Figure 1 It is a flow chart of a method for unmanned boat path planning based on geographic information system and multi-criteria analysis in the present invention; Figure 2 This is a schematic diagram of a single factor risk field in an embodiment of the present invention; Figure 3 A schematic diagram of a marine environmental risk field in an embodiment of the present invention; Figure 4 Schematic diagram of an eight-neighborhood search method in an embodiment of the present invention; Figure 5 Schematic diagram of the minimum safety distance in an embodiment of the present invention; Figure 6 A schematic diagram of deleting a redundant node in an embodiment of the present invention; Figure 7 Schematic diagram of path planning for an unmanned boat in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] This embodiment provides a method for unmanned boat path planning based on geographic information system and multi-criteria analysis. Figure 1 As shown, the specific steps include: S1: Acquire marine risk factor data based on a geographic information system, pre-process the marine risk factor data, and obtain marine risk factor raster data; Specifically, in this embodiment, geographic information data is also acquired based on the geographic information system, and the geographic information data is preprocessed to obtain geographic information raster data; The marine risk factor data include ocean waves, ocean currents, ocean winds and water depth, and the geographic information data include land and coastline data; Specifically, in this embodiment, high-precision data on ocean, meteorology and terrain are integrated through the geographic information system, and high-precision weather and environmental data are used to conduct a comprehensive risk assessment of the study area and visualize the risk factor areas, so as to facilitate subsequent better understanding and prediction of the risk situation in the navigation area.

[0015] S2: quantitatively analyzing the danger level of each grid in the marine risk factor grid data; S3: Analyze the risk level of each grid by using a single factor risk measurement method and a spatial analysis method to generate a single factor risk field; S4: Determine the weights of marine risk factor data based on a multi-criteria analysis method, and generate a marine environmental risk field based on the weights and the single factor risk field; S5: Based on the marine environmental risk field and using an improved A* algorithm, the safest unmanned boat navigation path is planned.

[0016] In a specific embodiment, in S1, preprocessing the marine risk factor data and geographic information data includes: The marine risk factor data were converted from NetCDF format to raster data format using ArcGIS software to facilitate spatial analysis, map making and other operations. The data and parameters involved are shown in Table 1:

[0017] In order to perform raster space calculations, the Kriging interpolation method is used to unify the resolution of the marine risk factor data and geographic information data in the raster data format.

[0018] In a specific embodiment, in S2, the danger level of each grid in the marine risk factor grid data is quantitatively analyzed, and the formula for quantitative analysis includes: Wave risk refers to the intensity of the impact of waves on ships. The intensity of waves is expressed by wave height. The higher the waves, the greater the wave intensity. The quantitative formula is: (9) in, W is the actual wave height of a single grid cell, W min is the lowest dangerous value for waves; The ocean current risk refers to the intensity of the ocean current's impact on the ship. The intensity of the ocean current is expressed by the current velocity. The greater the current velocity, the more difficult it is for the ship to maneuver, and the greater the ocean current risk. The quantitative formula is: (10) in, Y is the actual flow velocity of a single grid cell, Y min is the lowest risk value for rapids; Sea breeze risk refers to the intensity of the sea breeze's impact on ships. The sea breeze intensity is expressed by wind speed. The greater the wind speed, the greater the sea breeze risk. The quantitative formula is: (11) in, Q is the actual wind speed value of a single grid cell, Q min It is the lowest dangerous value for strong winds; The water depth danger is usually expressed by the depth of the waters you pass through. The shallower the water depth, the higher the navigation danger. The quantitative formula is: (12) in,D For draft; C For the surplus water depth; H w For wave height.

[0019] The specific parameters of the unmanned boat studied in this invention are shown in Table 2:

[0020] In a specific embodiment, in S3, the spatial analysis method includes resampling, grid calculation, and Kriging interpolation.

[0021] Specifically, the collected topographic, meteorological and hydrological data are selected as the basic data source for risk assessment. Based on the single-factor risk measurement method and spatial analysis method in ArcGIS software, the hazard level of each grid is analyzed and converted into a visual risk expression form to generate a single-factor risk field, which intuitively presents the distribution characteristics of each risk factor in the ocean space and provides key data support for comprehensive risk assessment.

[0022] Table 3 lists the spatial analysis method used in this embodiment when drawing a single factor risk map and the various risk factor parameters that the unmanned boat can withstand:

[0023] Specifically, the present invention takes 9:00 (UTC) on December 30, 2024 as an example, and selects the topographic, meteorological and hydrological data at this time as the basic data source for risk assessment. According to the single factor risk measurement method, the spatial analysis method of GIS can be used to generate single factor risk maps, such as Figure 2 shown.

[0024] In a specific embodiment, in S4, the weights of the marine risk factor data are determined based on the hierarchical analysis method in the multi-criteria analysis method, and the marine environment risk field is generated based on the weights and the single factor risk field, including: S41: Using the comparison matrix of the two-by-two comparisons between the indicators in equation (1) A , and based on expert advice, determine the factors i Relative to factors j The relative importance of the factors is calculated according to the scale values ​​in the Satty scale table shown in Table 4. a ij Assignment, the comparison matrix A It is expressed as: (1)

[0025] The comparison matrix satisfies the following conditions: (2) in, a ij Indication factors i Relative to factors j the importance of m is the number of evaluation factors; a ii Indication factors i Relative to their own importance, the factors i , j It is sea waves, sea currents, sea breeze or water depth; the key indicators and comparison matrix of this embodiment are detailed in Table 5:

[0026] S42: Judgment Matrix A Each column element of is normalized, and the processing formula is: (3) S43: Calculate the sum of the elements of each row of the normalized judgment matrix, expressed as: (4) S44: Yes Normalize it and get i The weight vector of marine risk factor data is: (5) S45: Calculate the maximum eigenvalue , expressed as: , In the formula, is the judgment matrix A With the weight vector No. i The sum of products of elements; S46: Pass consistency ratio CR The value of the verification comparison matrix A Consistency, consistency ratio CR The calculation formula is: (6) Among them, the consistency index CI for: (7) in, q is the order of the judgment matrix. Different matrix orders correspond to random consistency indicators. RI Values, see Table 6: ; S47: According to CR The value of the judgment matrixA Is there consistency? If CR ≥0.1, then the judgment matrix is ​​reconstructed based on the content of S41 A , and repeat S42-S46. If CR <0.1, execute S48; S48: Calculate the comprehensive risk value of each grid in turn according to the weight and the single factor risk field, so as to generate a marine environmental risk field. The calculation formula is: ,in, For the i A single factor risk field with a factor in a single grid; R It is the comprehensive risk value of a single grid, ranging from 0 to 1.

[0027] In this embodiment, it is obtained by calculation CR The final weight of sea breeze is 16.1%, the weight of waves is 9.6%, the weight of current is 27.7%, and the weight of water depth is 46.6%.

[0028] Specifically, the single factor risk map shows the impact of various risk factors on maritime navigation. However, in reality, risk factors such as waves, currents, sea breezes, and water depth interact with each other, increasing the complexity and risk of navigation. Therefore, the comprehensive risk value of each grid is calculated by determining the weights of these four types of data. , and get Figure 3 Marine environmental risk field shown.

[0029] In a specific embodiment, in S5, based on the marine environment risk field and using the improved A* algorithm, planning the safest unmanned boat navigation path includes: S51: Use the improved A* algorithm as the path evaluation function to evaluate the path from the starting point to the end point in the marine environmental risk field Total cost , expressed as: ,in, It is from the starting point to the end point in the running environment n the actual cost of is the weight of the path length, is the weight of path safety, It is the sum of the risk costs; Specifically, in order to ensure the shortest path under the premise of ensuring navigation safety, this embodiment selects is 0.3, is 0.7.

[0030] The traditional method uses Manhattan distance as the estimation function of the A* algorithm. The current node moves in its four adjacent directions as follows: ; This method will produce many turning points, causing the path to appear jagged. Therefore, this embodiment uses the Euclidean distance as the estimation function of the improved A* algorithm. When searching, Figure 4 As shown in the figure, the current node uses the eight-neighborhood search method to move in its eight adjacent directions, so the turning angle of the path will be less, and the path from the current node to the end point can be obtained. Estimated distance , expressed as: ,in, is the coordinate of the target point, is the coordinate of the current node; S52: To ensure that the planned route is far away from the edge of the dangerous area, a safety distance constraint is introduced, including: Assume that the current node is , the neighbor nodes to be expanded are , the obstacle position is , then the distance from the neighbor node to be expanded to the nearest obstacle It is expressed as: , , in, Represents the neighbor node x coordinate, Represents the neighbor node y coordinate, Indicates the safety distance threshold. Set the minimum safety distance. Figure 5 As shown, in this embodiment, the minimum safety distance is set to 1 nautical mile.

[0031] Specifically, this embodiment selects the safest path by setting a safety distance and comprehensively considering the danger level and path length of each grid.

[0032] S53: Searching to the end point according to the improved A* algorithm and the safety distance constraint condition n After that, through the end point n Backtrack to the parent node chain to generate the initial path; S54: For the three consecutive nodes in the initial path , and Perform a collinearity check operation to obtain the planned path; In a specific embodiment, in S54, three consecutive nodes in the initial path are sequentially , and Perform a collinear check operation to obtain the planned path including: 1) If they are collinear, then: , Defining vectors and for: , , Then the vector and Collinear, because and There are public spots B ,so A , B , C Three points on a line, indicating a node B If it is a redundant node, delete the redundant nodes on the same line B , the path is becomes ; 2) If they are not collinear, then: , Perform a safety distance check on the distance between the current node and the obstacle. The detection formula is: , in, P It is a straight line Any point on are the coordinates of the obstacle; If the distance between the current node and the obstacle meets the safety distance, and A, B, C The three points satisfy the following formula, indicating The path length is less than , then delete the redundant inflection points B ,like Figure 6 As shown, the formula is: .

[0033] S55: Using a cubic Bezier curve method to smooth the planned path, the formula is: ,in, , , and The key points used to determine the shape and direction of the curve. and are the starting point and end point of the curve, respectively, and and The change of its position affects the curvature and direction of the curve between the starting point and the end point. t Indicates a parameter, usually with a value range of .

[0034] This embodiment introduces a safe distance for path planning based on full consideration of the risk distribution of the marine environment, and smoothes the important turning points in the path to ensure smooth navigation. The specific planning results are as follows: Figure 7 As shown in the figure, it can be seen that the optimized navigation trajectory (Smoothed path with danger) is smoother than the navigation trajectory before optimization, which improves navigation efficiency and safety.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for unmanned boat path planning based on geographic information system and multi-criteria analysis, characterized in that: The specific steps include: S1: Acquire marine risk factor data based on a geographic information system, pre-process the marine risk factor data, and obtain marine risk factor raster data; Said ocean risk factor data include ocean waves, ocean currents, ocean winds and water depth; S2: quantitatively analyzing the danger level of each grid in the marine risk factor grid data; S3: Analyze the risk level of each grid by using a single factor risk measurement method and a spatial analysis method to generate a single factor risk field; S4: Determine the weights of marine risk factor data based on a multi-criteria analysis method, and generate a marine environmental risk field based on the weights and the single factor risk field; S5: Based on the marine environmental risk field and using an improved A* algorithm, the safest unmanned boat navigation path is planned.

2. The unmanned boat path planning method based on geographic information system and multi-criteria analysis according to claim 1 is characterized in that: In S2, the danger level of each grid in the marine risk factor grid data is quantitatively analyzed, and the formula for quantitative analysis includes: Wave risk The quantitative formula is: （9） in, W is the actual wave height of a single grid cell, W min is the lowest dangerous value for waves; Current risk The quantitative formula is: （10） in, Y is the actual flow velocity of a single grid cell, Y min is the lowest risk value for rapids; Sea breeze risk The quantitative formula is: （11） in, Q is the actual wind speed value of a single grid cell, Q min The lowest dangerous value for strong winds; Water depth danger The quantitative formula is: （12） in, D For draft; C For the surplus water depth; H w For wave height.

3. The unmanned boat path planning method based on geographic information system and multi-criteria analysis according to claim 1 is characterized in that: In S4, the weights of the marine risk factor data are determined based on the hierarchical analysis method in the multi-criteria analysis method, and the marine environmental risk field is generated based on the weights and the single factor risk field, including: S41: Using the comparison matrix of the two-by-two comparisons between the indicators in equation (1) A , and based on expert advice, determine the factors i Relative to factors j The relative importance of the factors is calculated according to the scale values ​​in the set Satty scale table. a ij Assignment, the comparison matrix A It is expressed as: （1） Comparison Matrix A The following conditions must be met: ， in, a ij Indication factors i Relative to factors j the importance of m is the number of evaluation factors; a ii Indication factors i Relative to their own importance, the factors i , j for waves, currents, winds or water depth; S42: Judgment Matrix A Each column element of is normalized, and the processing formula is: （3） S43: Calculate the sum of the elements of each row of the normalized judgment matrix, expressed as: （4） S44: Yes Normalize it and get i The weight vector of marine risk factor data is: （5） S45: Calculate the maximum eigenvalue , expressed as: ， In the formula, is the judgment matrix A With the weight vector No. i The sum of the products of the elements; S46: Pass consistency ratio CR The value of the verification comparison matrix A Consistency, consistency ratio CR The calculation formula is: （6） Among them, the consistency index CI for: （7） in, q is the order of the judgment matrix; RI is the random consistency index value; S47: According to CR The value of the judgment matrix A Is there consistency? If CR ≥0.1, then the judgment matrix is ​​reconstructed based on the content of S41 A , and repeat S42-S46. If CR <0.1, execute S48; S48: Calculate the comprehensive risk value of each grid in turn according to the weight and the single factor risk field, so as to generate a marine environmental risk field. The calculation formula is: ， in, For the i A single factor risk field with a factor in a single grid; R It is the comprehensive risk value of a single grid, ranging from 0 to 1.

4. The unmanned boat path planning method based on geographic information system and multi-criteria analysis according to claim 1 is characterized in that: In S5, based on the marine environmental risk field and using the improved A* algorithm, the safest unmanned boat navigation path is planned, including: S51: Use the improved A* algorithm as the path evaluation function to evaluate the path from the starting point to the end point in the marine environmental risk field n Total cost , expressed as: ， in, It is from the starting point to the end point in the running environment n the actual cost of is the weight of the path length, is the weight of path safety, It is the sum of the risk costs; The Euclidean distance is used as the estimation function of the improved A* algorithm. When searching, the current node moves in its eight adjacent directions to obtain the distance from the current node to the end point. n Estimated distance , expressed as: ， in, is the coordinate of the target point, is the coordinate of the current node; S52: Introduce safety distance constraints, including: Assume that the current node is , the neighbor nodes to be expanded are , the obstacle position is , then the distance from the neighbor node to be expanded to the nearest obstacle It is expressed as: ， ， in, Represents the neighbor node x coordinate, Represents the neighbor node y coordinate, Indicates the safety distance threshold; S53: After searching the end point according to the improved A* algorithm and the safety distance constraint condition, n Backtrack to the parent node chain to generate the initial path; S54: For the three consecutive nodes in the initial path , and Perform a collinearity check operation to obtain the planned path; S55: Using a cubic Bezier curve method to smooth the planned path, the formula is: ， in, , , and It is the key position point used to determine the shape and direction of the curve; t Indicates a parameter.

5. The unmanned boat path planning method based on geographic information system and multi-criteria analysis according to claim 4 is characterized in that: In S54, the three consecutive nodes in the initial path are sequentially , and Perform a collinear check operation to obtain the planned path including: Check the three consecutive nodes in the initial path one by one , and Are they collinear? If so, execute 1); otherwise, execute 2) to finally obtain the planned path; 1) If they are collinear, then: ， Defining vectors and for: ， ， Then the vector and Collinear, because and There are public spots B ,so A , B , C Three points on a line, indicating a node B If it is a redundant node, delete the redundant nodes on the same line B ; 2) If they are not collinear, then: ， Perform a safety distance check on the distance between the current node and the obstacle. The detection formula is: ， in, P It is a straight line Any point on are the coordinates of the obstacle; If the distance between the current node and the obstacle meets the safety distance, and A, B, C The three points satisfy the following formula, indicating The path length is less than , then delete the redundant inflection points B , the formula is: 。 6. The unmanned boat path planning method based on geographic information system and multi-criteria analysis according to claim 1 is characterized in that: In S3, the spatial analysis methods include resampling, grid calculation and Kriging interpolation methods.

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