Path planning and decision support method for underwater unmanned vehicle observation missions

Abstract

The present invention provides a path planning and decision-making support method for underwater unmanned vehicle observation missions, belonging to the field of underwater path planning and decision-making support, and mainly includes the following steps: obtaining sea area information and observation mission content, analyzing the mission content to select the order of visiting observation points of interest and intermediate path points, calculating the global path and local path based on the location of the path points, and changing the observation point when the risk of the observation point location increases. After completing the observation mission, calculate the hidden path to leave the current sea area. Compared with single-scale zoning, the method of the present invention uses dual-scale zoning to improve path accuracy and reduce the amount of calculation. At the same time, compared with a single-scale path planning system, it can automatically make decisions, select appropriate access paths to points of interest, observation points, sea area entrances, etc., and select the appropriate time to evacuate, thereby improving the observation efficiency and navigation safety of underwater unmanned vehicles.

Description

Technical Field

[0001] The present invention belongs to the field of underwater path planning and auxiliary decision-making, and specifically provides a path planning and auxiliary decision-making method for underwater unmanned vehicle observation tasks. Background Art

[0002] The navigation safety of underwater unmanned vehicles (UUVs) is of vital importance in modern naval operations. UUV operations have become a crucial means of naval warfare. As covert underwater combat platforms, they perform reconnaissance, attack, and defense missions, and their success is directly related to national security and the implementation of military strategy. UUVs must operate in a vast and complex ocean environment. Faced with changing ocean conditions and potential threats, navigation safety is particularly important. Ensuring the navigation safety of UUVs involves not only the accuracy of the navigation system but also real-time monitoring of the ocean environment, prediction and avoidance of potential threats, and intelligent decision-making in route planning. Any negligence in navigation safety can lead to mission failure and even serious loss of personnel and equipment. Therefore, research on technical means and strategies to improve the navigation safety of UUVs is an important component of safeguarding naval combat capabilities and national security.

[0003] The marine risk rasterization method is a technology used to improve the navigation safety of underwater unmanned vehicles. It divides the marine environment into multiple small grids, each grid representing a specific geographical area and environmental conditions. Various risk factors are evaluated and labeled in each grid, such as water depth, ocean currents, temperature, fish activity, and other potential threats. Through this refined rasterization method, the navigation system of the underwater unmanned vehicle can more accurately identify and assess the risk levels of different areas, thereby formulating safer and more efficient navigation routes. However, the traditional marine risk rasterization zoning method also has some shortcomings. The traditional rasterization method usually uses a fixed grid size and cannot be flexibly adjusted to adapt to the detailed requirements of different areas, which may lead to insufficiently detailed risk assessments in some key areas. In addition, the traditional method mainly relies on static data, has insufficient response capabilities to real-time environmental changes, and cannot promptly reflect new risk factors in the dynamic marine environment.

[0004] Underwater UAV observation missions are highly complex and include various forms, including single-point-of-interest observation, multi-point-of-interest observation, and regional observation. Single-point-of-interest observation requires the UAV to covertly approach and monitor or attack a specific target, requiring precise path planning and concealment strategies. Multi-point-of-interest observation involves multiple targets, and the UAV must move between multiple locations, increasing complexity and requiring efficient path selection and dynamic adjustment. Regional observation requires the UAV to operate in a specific area for a long time, requiring a comprehensive risk assessment and environmental adaptation for the entire area to ensure continuous concealment and flexibility in mission execution. These mission forms require the UAV to have excellent navigation, concealment, and real-time decision-making capabilities. This places strict demands on the path planning and decision-making of the UAV during its observation voyage.

[0005] In recent years, research on path planning for underwater unmanned vehicles has made significant progress, particularly with the introduction of intelligent algorithms. Ant colony algorithms, genetic algorithms, and deep learning techniques have optimized path selection, improving planning accuracy and efficiency. For example, ant colony algorithms simulate the foraging behavior of ants to find the shortest path and avoid high-risk areas; genetic algorithms select optimal paths through natural selection; and deep learning techniques analyze historical and real-time data to predict environmental changes and dynamically adjust paths. However, these intelligent algorithms also have drawbacks. They are computationally complex and may lack real-time performance when processing large-scale ocean data. Furthermore, intelligent algorithms rely on large amounts of training data and computing resources, and their applications may be limited by data acquisition and processing capabilities. Furthermore, the "black box" nature of deep learning models reduces the transparency and interpretability of path decisions.

[0006] When performing observation missions, underwater unmanned vehicles (UUVs) face highly complex and dynamic environments, making decision-making extremely challenging. For example, during observation, an UUV may encounter an unusually high-risk situation, such as a sudden change in weather that threatens the vehicle's safety. In such situations, the UUV must quickly change its observation point and replan its path to avoid the threat. These decisions must be made extremely quickly; any hesitation could result in mission failure or expose the UUV. The complexity of these decisions necessitates a highly intelligent and real-time path planning system for UUVs during observation missions to ensure mission success while maximizing their own safety. Summary of the Invention

[0007] In order to cope with the computational complexity and decision-making complexity of path planning in underwater unmanned vehicle observation missions, the present invention proposes a path planning and auxiliary decision-making method for underwater unmanned vehicle observation missions. The path planning is divided into two steps, which ensures the calculation speed and path accuracy. At the same time, systematic decision-making suggestions are made for the underwater unmanned vehicle observation mission and covert evacuation until the underwater unmanned vehicle completes the observation mission and leaves the dangerous sea area.

[0008] The present invention divides the target sea area into two-scale risk zones, uses a small block path planning strategy combining node-to-node path planning and dynamic planning to perform fast and accurate path planning, and makes systematic decision-making recommendations for underwater unmanned vehicle observation missions. The specific steps include:

[0009] Step 1: Obtaining observation mission information and risk zoning;

[0010] The observation mission information includes the current position of the underwater unmanned vehicle, mission content, longitude and latitude range of the target sea area, and the concealment risk and security risk value information corresponding to the target sea area;

[0011] Perform dual-scale risk zoning within the target sea area, the dual-scale risk zoning including local zoning and global zoning, the global zoning consisting of a number of global blocks, each of which consists of a number of local blocks; and add corresponding concealment risk values ​​and security risk values ​​to each local block and each global block;

[0012] Step 2: According to different observation mission types, the observation point position of the underwater unmanned vehicle is determined in the local division, and a global path planning is performed from the current position of the underwater unmanned vehicle to the observation point position in the global division, and the path with the lowest path cost is selected as the global path;

[0013] Step 3: Use inter-node path planning and dynamic programming to plan local block paths. Specifically, multiple local paths are planned within each global block in the global path. Corresponding local blocks are selected between global blocks for connection. Dynamic programming is used to select the continuous path with the lowest risk from all local paths as the precise path.

[0014] Step 4: The underwater unmanned vehicle follows a precise path to the observation point, conducts observations, and obtains the surrounding concealment risk value;

[0015] Step 5: Detect the hidden risk value Rc of the current observation point i Is it greater than or equal to the danger identification threshold Rc0? If so, it means that the surrounding risk is increasing, and go to step 6 to change the observation point. If less than, it means that the surrounding risk is normal. After completing the observation task of the current observation point, go to step 7 to continue the observation task.

[0016] Step 6: Reselect the local block with the lowest risk around the current observation point as the new observation point and plan the path, then go to step 4 to continue executing the replanned path;

[0017] Step 7: Get the current observation task status. If the current observation point is not the last observation point, that is, all observation tasks have not been completed, go to the next observation point to continue the current observation task and go to step 4. If the current underwater unmanned vehicle position is the last observation point, that is, all observation tasks have been completed, go to step 8 to covertly evacuate the current sea area.

[0018] Step 8. When performing a covert evacuation, specify the latitude and longitude boundaries of the area to be passed and the evacuation direction. Use the path planning algorithm to generate a path from the location of the underwater unmanned vehicle, select the global path with the lowest total path cost, use inter-node path planning and dynamic planning to perform local block path planning to accurately calculate the evacuation path, and then execute the evacuation.

[0019] Furthermore, in step 1, the dual-scale risk zoning is divided as follows:

[0020] The size of the local block is determined according to the size of the underwater unmanned vehicle, and risk zoning is performed using large and small grids. The large grid corresponds to the global block, and the small grid corresponds to the local block. The small grid is nested in the large grid, and the concealment risk and security risk information of each local block and global block is obtained to generate a multi-dimensional risk map.

[0021] Furthermore, in step 2, the observation task types include single point of interest observation tasks, multiple point of interest observation tasks and regional observation tasks.

[0022] Furthermore, for the single point of interest observation task, the global path planning method is as follows:

[0023] S101, obtaining the most concealed local block around the point of interest in the local division as the end point of the path, that is, the observation point location;

[0024] S102. Select multiple entrances on the boundary of the area where the current underwater unmanned vehicle is located for path planning. Use the path planning algorithm to calculate the distance from each entrance to the path endpoint and the path risk as the path cost; select the entry point with the lowest path cost as the sea area entrance S1, and obtain the planned path R1 {S1, P1, P2, P3...} in the global scale division, where P1, P2, P3... are the middle points on the path.

[0025] Furthermore, for the multi-point-of-interest observation task, the global path planning method is as follows:

[0026] S201, obtaining the most concealed local block around each point of interest in the local division as the observation point position of each point of interest;

[0027] S202. Plan paths between all observation points on the global region and construct a fully connected graph containing all observation points. Based on the fully connected graph, solve the order of visiting points of interest as a classic TSP problem to obtain a node visit order L{n1,n2,n3...}, where n1,n2,n3,... are the observation points visited in sequence by the underwater unmanned vehicle.

[0028] S203. Select multiple entrances on the boundary of the area where the current underwater unmanned vehicle is located to perform path planning towards n1. Through the path planning algorithm, select the entry point with the lowest path cost as the sea area entrance S2, and obtain the path R2 {S2, P1, P2, P3...} planned in the global division, where P1, P2, P3... are the middle points on the path.

[0029] Furthermore, for regional observation tasks, the global path planning method is as follows:

[0030] S301, selecting the best-concealed local block in the best-concealed global block as the end point of the path, i.e., the observation point location;

[0031] S302. Select multiple entrances on the boundary of the area where the current underwater unmanned vehicle is located to plan the path to the end point of the path. Through the path planning algorithm, select the entry point with the lowest path cost as the sea area entrance S3, and obtain the planned path R3 {S3, P1, P2, P3...} on the global division, where P1, P2, P3... are the middle points on the path.

[0032] Furthermore, the specific steps of step three are as follows:

[0033] Step 1) At each path point P in the global path R n Generate multiple possible local paths in the corresponding local block, each local path starts from the current path point P n Go to the next waypoint P n+1 , calculate the risk of each path;

[0034] Step 2), for each global path point P n , select 3 out-edge candidate points and 3 in-edge candidate points to form several possible path combinations, use the dynamic programming algorithm to evaluate these local paths, and select the path with the lowest comprehensive risk among all possible local path combinations as the exact path.

[0035] Compared with traditional methods, this invention uses dual-scale zoning for path planning, uses a path evaluation algorithm to automatically select path points, and dynamically changes the path according to the current sea area risk, realizing automatic planning and automatic decision-making of the underwater unmanned vehicle's navigation path. This invention improves the accuracy of path planning, reduces the amount of calculation, and at the same time enhances the automatic decision-making ability, optimizing the observation efficiency and navigation safety of the underwater unmanned vehicle.

[0036] 1) The dual-scale risk zoning can accurately describe the risk status of the target sea area and improve computing efficiency. The small block path planning strategy of inter-node path planning + dynamic planning can refine the navigation path of the underwater unmanned vehicle. The system provides a large number of decision-making directions for underwater unmanned vehicle observation tasks. Therefore, the present invention can fully adapt to the complexity of current underwater unmanned vehicle observation tasks.

[0037] 2) During the execution of the mission, the present invention can obtain and analyze sea area information in real time, optimize the selection of observation points of interest and intermediate path points, and dynamically adjust observation points to cope with increased risks, thereby ensuring the efficiency and safety of the mission. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flow chart of a path planning and decision-making assistance method for underwater unmanned vehicle observation tasks according to the present invention;

[0039] Figure 2 This is a schematic diagram of the dual-scale grid division;

[0040] Figure 3 is the outer grid (global) risk distribution map;

[0041] Figure 4 This is the inner grid (local) risk distribution map;

[0042] Figure 5 This is a schematic diagram of the global path planning;

[0043] Figure 6 Schematic diagram for selecting adjacent points between global blocks to connect with corresponding local blocks in local path planning;

[0044] Figure 7 In local path planning, after the adjacent points are determined, the local block is further refined to generate an accurate path diagram. Specific embodiments

[0045] The present invention provides a path planning and decision-making assistance method for underwater unmanned vehicle observation tasks. The method of the present invention analyzes the needs of three tasks, performs path planning and decision-making assistance, and completes the task and leaves the sea area, thus realizing the whole process planning of the observation task. It aims to improve the real-time performance of underwater unmanned vehicles in dealing with risks and responding. The flow chart of the method of the present invention is as follows: Figure 1 shown.

[0046] This embodiment provides a path planning and decision-making assistance method for underwater unmanned vehicle observation tasks, including the following steps:

[0047] Step 1: Obtain observation mission information and risk zoning.

[0048] 1) Obtaining observation mission information, including the current position of the underwater unmanned vehicle, mission content, longitude and latitude of the target sea area, and the concealment risk and security risk value information corresponding to the target sea area;

[0049] Assume that the underwater unmanned vehicle is currently located at Q (25.1°N, 121.5°E) and its mission is to monitor and conduct reconnaissance in the target sea area between 24.5000°N and 25.5000°N, and 101.0000°E and 122.0000°E. Based on intelligence data, the concealment risk and security risk of each area within the sea area are calculated. For example, the concealment risk of the area covered by enemy radar is 0.8, and the security risk is 0.9.

[0050] 2) Risk zoning:

[0051] A dual-scale risk zoning is performed within the target sea area. The dual-scale risk zoning includes local zoning and global zoning. The global zoning consists of several global blocks, and each global block consists of several local blocks. Corresponding concealment risk values ​​and security risk values ​​are added to each local block and each global block. The division method of the dual-scale risk zoning is as follows:

[0052] The size of the local block is determined based on the size of the underwater unmanned vehicle. Risk zoning is performed using two grid sizes: the large grid corresponds to the global block, and the small grid corresponds to the local block. The small grid is nested within the large grid, and concealment and security risk information is obtained for each local block and the global block to generate a multi-dimensional risk map. For example, if the size of the local block is set to 1.5 times the length of the underwater unmanned vehicle, each global block is set to contain 10*10 local blocks.

[0053] Assuming the length of the underwater unmanned vehicle is 90 meters, a dual-scale risk zoning is performed based on the length of the underwater unmanned vehicle. The size of the local block is set to 1.5 times the length of the underwater unmanned vehicle, that is, 135 meters × 135 meters. 10 × 10 local blocks form a global block (1350 meters × 1350 meters). Each block is assigned a concealment risk value and a safety risk value to support path planning. The dual-scale risk zoning diagram is shown below. Figure 2-Figure 3 As shown, Figure 2-Figure 3 In the example, each color block represents the cost of the current block, which is calculated by combining the concealment risk and security risk. Figure 2 This is a schematic diagram of the dual-scale grid division method. Figure 3 Map the outer grid (global) risk zones, Figure 4 Map the inner grid (local) risk zones.

[0054] Step 2: According to different observation mission types, determine the observation point location of the underwater unmanned vehicle in the local division, and perform global path planning from the current underwater unmanned vehicle location to the observation point location in the global division, and select the path with the lowest path cost as the global path. The global path planning diagram is shown in the figure below. Figure 5 shown.

[0055] According to the task type, such as single point of interest observation task, multiple point of interest observation task or regional observation task, select the corresponding path planning process.

[0056] 1) For the single point of interest observation task, the global path planning method is as follows:

[0057] S101. Obtain the local block with the highest concealment in the area with a distance of 1 around the point of interest on the local division as the end point of the path, that is, the observation point location; this process ensures that the underwater unmanned vehicle can select a point with the best concealment as the final observation point location, thereby minimizing the risk of being discovered.

[0058] Assume the current position Q of the underwater vehicle is (35.0°N, 120.0°E), and the mission requires observation near point of interest I. Calculate the concealment risk within the local area around point of interest I. Assume that the local area with the lowest concealment risk is selected as observation point d1, located at (34.8°N, 120.2°E).

[0059] S102: After determining the path end point d1, perform global path planning on the global region.

[0060] On the boundary of the area where the underwater unmanned vehicle's current position Q is located, 10 entry points are randomly selected, such as S1, S2, ..., S 10, path planning is performed. These entry points are used to find the optimal path for the underwater unmanned vehicle from its current position Q to the path endpoint d1. The path planning algorithm calculates the distance from each entry point to the path endpoint and the path risk as the path cost. The entry point with the lowest path cost is selected as the sea area entrance S1. Finally, the global path R1 {S1, P1, P2, P3, ...} from Q to d1 is determined, where P1, P2, P3, ... are intermediate points on the path.

[0061] Use the path planning algorithm to calculate the distance from each entrance to the end point of the path and the path risk as the path cost Path_cost. The specific algorithm process is as follows:

[0062] The path planning algorithm needs to consider both the safety risk Rs and the concealment risk Rc in the sea area, and integrate these risk information to construct the cost of a certain block. i,j . Its cost can be expressed as:

[0063] cost i,j =w s ·Rs i,j +w c ·Rc i,j

[0064] Among them, w s and w c Rc is the risk weight, which indicates the importance of concealment risk and security risk. i,j Represents the hidden risk of the block with subscript (i, j), Rs i,j Indicates the security risk of the block with subscript (i, j). When calculating the global path, subscripts i, j represent the index of the global block. When calculating the local path, subscripts i, j represent the index of the local block. The global block and the local block have their own security risk Rs and hidden risk Rc. The specific risk zoning method is as follows: Figure 2-Figure 4 shown.

[0065] Therefore, the overall path cost Path_cost of a path can be expressed as:

[0066]

[0067] 2) For multi-point-of-interest observation tasks, it is necessary to plan paths for multiple observation points and ensure that the underwater unmanned vehicle can effectively move between the observation points to complete the task. The global path planning method for multi-point-of-interest observation tasks is as follows:

[0068] S201. Determine the optimal observation point location for each point of interest at a local scale. Specifically, within an area with a distance of 1 around each point of interest, calculate the concealment risk of each local block and select the point with the lowest concealment risk as the observation point. The selection of each observation point ensures that the underwater unmanned vehicle completes its observation mission under the safest conditions.

[0069] S202. Then, plan the paths between all observation points on the global division and construct a fully connected graph containing all observation points. In this graph, nodes represent observation points and edges represent possible paths between observation points. In order to optimize the order in which the underwater unmanned vehicle visits each observation point, the path planning problem between observation points is converted into the classic traveling salesman problem (TSP). TSP is a problem of finding the shortest path. The goal is to find a path that minimizes the total path cost of the underwater unmanned vehicle visiting all observation points. By solving TSP, the visit order L{n1,n2,n3,…} of the observation points is obtained, where n1,n2,n3,… are the observation points visited in sequence by the underwater unmanned vehicle.

[0070] Suppose an underwater unmanned vehicle (UUV) is tasked with observing three points of interest (I1, I2, and I3). First, the concealment risk is calculated within a region with a distance of 1 around each point of interest. The local block with the lowest concealment risk is selected as the observation points d1, d2, and d3 for the three points of interest. Assume that the coordinates of these three observation points are d1 = (34.8°N, 120.2°E), d2 = (35.2°N, 120.4°E), and d3 = (34.9°N, 120.6°E). Next, a fully connected graph containing d1, d2, and d3 is constructed on a global scale. The cost of each path between the observation points is calculated, transforming the observation point visit order problem into a TSP problem. The optimal order L{n1 = d2, n2 = d1, n3 = d3} for the UUV to visit the observation points is obtained, i.e., the UUV first visits d2, then d1, and finally d3.

[0071] The path cost calculation method is the same as the cost calculation method in S102.

[0072] This step ensures that the underwater unmanned vehicle can visit all observation points in the optimal path sequence in the multi-point-of-interest observation mission, thereby minimizing the risk and increasing the success rate of the mission.

[0073] S203: Similar to S102, assume the UUV's current position Q is (35.0°N, 120.0°E). The mission requires the UUV to first reach observation point n1, whose coordinates are (34.8°N, 120.2°E). Five entry points are randomly selected on the boundary of the region where Q is located: S1, S2, S3, S4, and S5. For each entry point, a path planning algorithm is used to calculate the path cost from that entry point to n1. For example, the path cost of entry point S1 might be 10, the path cost of S2 might be 8, and so on. Assuming that entry point S2 has the lowest cost, S2 is selected as the optimal entry point. Ultimately, the global scale path from S2 to n1 is determined to be R2{S2, P1, P2, P3, …}, which includes several intermediate points P1, P2, P3, … to ensure that the UUV can successfully reach n1. Then, n2, n3, … are visited sequentially.

[0074] Use the path planning algorithm to calculate the global path with the lowest path cost under the L access order, such as n1→n2, n2→n3.

[0075] The path cost calculation method is the same as the cost calculation method in S102.

[0076] 3) For regional observation tasks, the global path planning method is as follows:

[0077] S301: In a regional observation mission, the UUV's final observation location must be determined so it can conduct covert operations within the designated sea area. To this end, the best concealment area is selected globally. Within this area, further refinement is performed to select the best concealment local block as the final path endpoint d3.

[0078] Specifically, first, the hidden risk Rc of the entire sea area is calculated on a global scale. i,j The sea area is divided into multiple global blocks, and the concealment risk Rc of each global block is calculated. i,j , and select the global block with the smallest concealment risk (i.e. the strongest concealment). Then, within the selected global block, further concealment risk analysis is performed on the local blocks. The system calculates the concealment risk Rc of each local block. i,j , and select the local block with the lowest concealment risk as the final observed path end point d3.

[0079] Suppose the mission requires an underwater unmanned vehicle to perform an area observation mission within a certain sea area between (34.5°N, 119.5°E) and (35.5°N, 120.5°E). The system first divides the entire sea area into multiple global blocks and calculates the concealment risk of each global block. Assume that after analysis, the system finds that the global block with the lowest concealment risk in the sea area is G3, which has the lowest concealment risk. Next, the system further divides G3 into multiple local blocks and calculates the concealment risk of these local blocks. Assume that within G3, local block d3 has the lowest concealment risk, so d3 is selected as the final observation location. Finally, the location of d3 is determined as the path end point d3 = (34.8°N, 120.3°E). The underwater unmanned vehicle will perform the covert observation mission at this location, ensuring that the most concealed location in the area observation mission is selected, effectively reducing the risk of enemy detection.

[0080] S302: Similar to the path planning for the single point of interest observation task in S102, the path planning algorithm is used to select the most suitable sea area entrance S3 and obtain the path R3 {S3, P1, P2, P3…} planned on a global scale, where P1, P2, P3… are the intermediate points on the path.

[0081] Step 3: Use inter-node path planning and dynamic planning to perform local block path planning, such as Figure 6 and Figure 7 As shown, Figure 6 Select a schematic diagram for the adjacent points, Figure 7 Generate a schematic diagram for an exact path.

[0082] Multiple local paths {r1, r2, r3...} are planned within each global block in the global path. Corresponding local blocks are selected between global blocks for connection. A dynamic programming algorithm is used to select the path with the lowest continuous path cost from all local paths as the precise path.

[0083] During observation missions, the global path needs to be further refined to generate a precise path that ensures the underwater unmanned vehicle can safely and stealthily pass through each global block area. The core of this step is to select a continuous path with the lowest overall risk through local path planning.

[0084] The specific steps of step three are as follows:

[0085] Step 1), first, at each path point P in the global path R n Generate multiple possible local paths in the corresponding local block. Each local path starts from the current path point P n Go to the next waypoint P n+1, and the risk of each path is determined by the calculation method of the path cost in S102.

[0086] Step 2), for each global path point P n , selecting three candidate outbound and three candidate inbound paths to form several possible path combinations. These local paths are then evaluated using a dynamic programming algorithm, aiming to select a continuous path with the lowest overall risk. This means that the risk of not just a single local path but the cumulative risk of all connected local paths in the entire path chain is considered. Ultimately, the path with the lowest overall risk among all possible local path combinations is selected as the refined path. This refined path r starts from the initial entry point, passes through all planned pathpoints, and ultimately reaches the underwater unmanned vehicle's observation endpoint d3.

[0087] For example, the global path R of the underwater unmanned vehicle includes the following path points: P1 = (34.9°N, 120.1°E), P2 = (34.8°N, 120.2°E), and P3 = (34.7°N, 120.3°E). Within the local grid between path points P1 and P2, the system generates three local paths r1, r2, and r3. Similarly, three local paths are generated between path points P2 and P3. Using a dynamic programming algorithm, all possible path combinations from P1 to P3 are evaluated. For example, the following possible path combinations are evaluated:

[0088] 1.

[0089] 2.

[0090] 3.

[0091] Select the combined path with the lowest comprehensive risk. Assuming that the combination r2+r5 is selected, this path will be used as the final precise path for the underwater unmanned vehicle.

[0092] Step 4: Execute the planned precise path, conduct observations after arriving at the observation point, use the passive equipment of the underwater unmanned vehicle to obtain surrounding information and calculate the concealment risk value Rc of the local block where the underwater unmanned vehicle is currently located i .

[0093] Step 5: Detect the hidden risk value Rc of the current observation point i Is it greater than or equal to the danger identification threshold Rc0? If so, it means that the surrounding risk is abnormally increasing, and go to step 6 to change the observation point; if less than, it means that the surrounding risk is normal, and after completing the observation task of the current observation point, go to step 7 to continue the observation task.

[0094] Step 6: Reselect the local block with the lowest risk around the current observation point as the new observation point and plan the path. Go to step 4 to continue executing the replanned path.

[0095] Step 7. Get the current observation mission status. If the current underwater unmanned vehicle position is not the last observation point, that is, all observation tasks have not been completed, go to step 4 and go to the next observation point to continue the current observation mission. If the current underwater unmanned vehicle position is the last observation point, that is, all observation tasks have been completed, go to step 8 and secretly evacuate the current sea area.

[0096] Step 8: Execute covert evacuation. Specify the longitude and latitude boundaries of the area to be passed and the evacuation direction. From the current position of the underwater unmanned vehicle, use the path planning algorithm to generate the global block path R4 with the lowest overall path cost. Use the small block path planning strategy of inter-node path planning + dynamic planning to accurately calculate the evacuation path and execute the evacuation.

[0097] As described above, although the present invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the present invention itself. Various changes may be made to the form and details without departing from the spirit and scope of the present invention.

Claims

1. A path planning and decision-making support method for underwater unmanned vehicle observation tasks, characterized in that: The specific steps are as follows: Step 1: Obtaining observation mission information and risk zoning; The observation mission information includes the current position of the underwater unmanned vehicle, mission content, longitude and latitude range of the target sea area, and the concealment risk and security risk value information corresponding to the target sea area; Perform dual-scale risk zoning within the target sea area, the dual-scale risk zoning including local zoning and global zoning, the global zoning consisting of a number of global blocks, each of which consists of a number of local blocks; and add corresponding concealment risk values ​​and security risk values ​​to each local block and each global block; Step 2: According to different observation mission types, the observation point position of the underwater unmanned vehicle is determined in the local division, and a global path planning is performed from the current position of the underwater unmanned vehicle to the observation point position in the global division, and the path with the lowest path cost is selected as the global path; Step 3: Use inter-node path planning and dynamic programming to plan local block paths. Specifically, multiple local paths are planned within each global block in the global path. Corresponding local blocks are selected between global blocks for connection. Dynamic programming is used to select the continuous path with the lowest risk from all local paths as the precise path. Step 4: The underwater unmanned vehicle follows a precise path to the observation point, conducts observations, and obtains the surrounding concealment risk value; Step 5: Detect the hidden risk value Rc of the current observation point i Is it greater than or equal to the danger identification threshold Rc0? If so, it means that the surrounding risk is increasing, and go to step 6 to change the observation point. If less than, it means that the surrounding risk is normal. After completing the observation task of the current observation point, go to step 7 to continue the observation task. Step 6: Reselect the local block with the lowest risk around the current observation point as the new observation point and plan the path, then go to step 4 to continue executing the replanned path; Step 7: Get the current observation task status. If the current observation point is not the last observation point, that is, all observation tasks have not been completed, go to the next observation point to continue the current observation task and go to step 4. If the current underwater unmanned vehicle position is the last observation point, that is, all observation tasks have been completed, go to step 8 to covertly evacuate the current sea area. Step 8. When performing a covert evacuation, specify the latitude and longitude boundaries of the area to be passed and the evacuation direction. Use the path planning algorithm to generate a path from the location of the underwater unmanned vehicle, select the global path with the lowest total path cost, use inter-node path planning and dynamic planning to perform local block path planning to accurately calculate the evacuation path, and then execute the evacuation.

2. The method according to claim 1, characterized in that In step 1, the dual-scale risk zoning is divided as follows: The size of the local block is determined according to the size of the underwater unmanned vehicle, and risk zoning is performed using large and small grids. The large grid corresponds to the global block, and the small grid corresponds to the local block. The small grid is nested in the large grid, and the concealment risk and security risk information of each local block and global block is obtained to generate a multi-dimensional risk map.

3. The method according to claim 1, characterized in that In step 2, the observation task types include single point of interest observation tasks, multiple point of interest observation tasks and regional observation tasks.

4. The method according to claim 3, characterized in that For the single point of interest observation task, the global path planning method is as follows: S101, obtaining the most concealed local block around the point of interest in the local division as the end point of the path, that is, the observation point location; S102, selecting multiple entrances on the boundary of the area where the current underwater unmanned vehicle is located for path planning, and using a path planning algorithm to calculate the distance from each entrance to the path endpoint and the path risk as the path cost; The entry point with the lowest path cost is selected as the sea area entrance S1, and the path R1{S1, P1, P2, P3...} planned in the global scale division is obtained, where P1, P2, P3... are the intermediate points on the path.

5. The method according to claim 3, characterized in that For the multi-point-of-interest observation task, the global path planning method is as follows: S201, obtaining the most concealed local block around each point of interest in the local division as the observation point position of each point of interest; S202. Plan paths between all observation points on the global region and construct a fully connected graph containing all observation points. Based on the fully connected graph, solve the order of visiting points of interest as a classic TSP problem to obtain a node visit order L{n1,n2,n3...}, where n1,n2,n3,... are the observation points visited in sequence by the underwater unmanned vehicle. S203. Select multiple entrances on the boundary of the area where the current underwater unmanned vehicle is located to perform path planning towards n1. Through the path planning algorithm, select the entry point with the lowest path cost as the sea area entrance S2, and obtain the path R2 {S2, P1, P2, P3...} planned in the global division, where P1, P2, P3... are the middle points on the path.

6. The method according to claim 3, characterized in that For regional observation tasks, the global path planning method is as follows: S301, selecting the best-concealed local block in the best-concealed global block as the end point of the path, i.e., the observation point location; S302. Select multiple entrances on the boundary of the area where the current underwater unmanned vehicle is located to plan the path to the end point of the path. Through the path planning algorithm, select the entry point with the lowest path cost as the sea area entrance S3, and obtain the planned path R3 {S3, P1, P2, P3...} on the global division, where P1, P2, P3... are the middle points on the path.

7. The method according to claim 1, characterized in that The specific steps of step three are as follows: Step 1) At each path point P in the global path R n Generate multiple possible local paths in the corresponding local block, each local path starts from the current path point P n Go to the next waypoint P n+1 , calculate the risk of each path; Step 2), for each global path point P n , select 3 out-edge candidate points and 3 in-edge candidate points to form several possible path combinations, use the dynamic programming algorithm to evaluate these local paths, and select the path with the lowest comprehensive risk among all possible local path combinations as the exact path.

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