Polar region ship path planning method and device and electronic equipment

Abstract

The invention discloses a polar region ship route planning method and device and electronic equipment, and can be applied to the technical field of automatic route planning. After sea ice thickness data, sea ice concentration data and spatial data are obtained, a first passable value of a navigation area is calculated according to the sea ice data, a three-dimensional navigation risk map is constructed according to the first passable value and the spatial data, the three-dimensional navigation risk map is sliced to obtain a two-dimensional plane grid map, and the two-dimensional plane grid map is mapped to the sea ice concentration data. Performing path search on each layer of two-dimensional plane grid map by using a first path search algorithm to obtain a first node on a corresponding path, constructing a node route map according to the first node, searching in the node route map by using a second path search algorithm to obtain a coarse path, and optimizing the coarse path by using an optimal control method to obtain the optimal path. Therefore, the functions of high calculation speed, short planning path, low planning risk and meeting of ship operation can be realized, the navigation risk of the polar region ship in the navigation process is effectively reduced, and the navigation efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of automatic route planning, and in particular to a polar ship path planning method and device and electronic equipment. Background Art

[0002] In the related technology, in the complex navigation environment of the polar regions, ship route planning is particularly important. At present, the path search algorithms based on the grid map include the Dijkstra algorithm and the A* algorithm. However, the Dijkstra algorithm is a path search algorithm that traverses the entire map. Although the A* algorithm has the idea of ​​heuristic search, it will also search for multiple paths with the same cost value. Both algorithms will search for a large number of useless nodes, resulting in very low search efficiency, which is difficult to meet the requirements of fast and efficient intelligent ships. In addition, there is the Jump Point Search algorithm (JPS algorithm), but the JPS algorithm can only determine whether a certain area is passable, and cannot selectively pass according to the weight value of the area, that is, it cannot selectively navigate according to the level of navigation risk. It can be seen from this that the existing algorithms cannot simultaneously meet the two requirements of short navigation paths and low navigation risks, which leads to low efficiency and high risk of ships sailing in the polar regions.

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

[0004] The main purpose of the embodiments of the present application is to propose a polar ship path planning method and device and electronic equipment, which can effectively improve the efficiency of ship navigation in the polar regions and reduce navigation risks.

[0005] To achieve the above object, an embodiment of the present application provides a polar ship path planning method, the method comprising the following steps:

[0006] Acquire sea ice data and spatial data of all navigation areas of polar ships, wherein the polar sea ice data includes sea ice thickness data and sea ice density data;

[0007] Calculating a first passable value of the navigation area according to the sea ice data;

[0008] constructing a three-dimensional navigation risk map according to the first passable value and the spatial data;

[0009] Slicing the three-dimensional navigation risk map to obtain a plurality of two-dimensional plane grid maps;

[0010] Performing a path search on each layer of the two-dimensional plane grid map by using a first path search algorithm to obtain a first node on the corresponding path;

[0011] Building a node roadmap according to the first node;

[0012] Searching in the node route graph by a second path search algorithm to obtain a coarse path;

[0013] The rough path is optimized by an optimal control method to obtain a target path, and the target path is used to guide the navigation track of the polar ship.

[0014] In some embodiments, calculating the first passability value of the navigation area according to the sea ice data includes:

[0015] Classifying the sea ice in the navigation area according to the sea ice thickness data to obtain a plurality of sea ice types;

[0016] Obtaining a risk index value corresponding to the sea ice type;

[0017] A first passability value of the navigation area is calculated according to the risk index value and the sea ice concentration data.

[0018] In some embodiments, constructing a three-dimensional navigation risk map according to the first passable value and the spatial data includes:

[0019] Obtaining the maximum passable value among all the first passable values ​​as the second passable value;

[0020] Calculating a navigation risk value corresponding to the navigation area according to the first passable value and the second passable value;

[0021] Constructing a two-dimensional plane grid map according to the spatial data;

[0022] Normalizing the navigation risk value according to the two-dimensional plane grid map to obtain a normalized risk value;

[0023] The three-dimensional navigation risk map is constructed according to the two-dimensional plane grid map and the normalized risk value.

[0024] In some embodiments, slicing the three-dimensional navigation risk map to obtain a plurality of two-dimensional plane grid maps includes:

[0025] Obtaining the three-dimensional height in the three-dimensional navigation risk map;

[0026] The three-dimensional navigation risk map is sliced ​​according to the three-dimensional height to obtain a plurality of the two-dimensional plane grid maps.

[0027] In some embodiments, performing a path search on each layer of the two-dimensional plane grid map by using a first path search algorithm to obtain a first node on a corresponding path includes:

[0028] Performing path search on each layer of the two-dimensional plane grid map in turn;

[0029] When a path is found, the second node in the path is saved;

[0030] Projecting all the second nodes onto a two-dimensional plane;

[0031] The second node is screened according to the projection point on the two-dimensional plane to obtain the first node, and the information of the first node includes the plane coordinate position, the plane coordinate position of the parent node and the layer information.

[0032] In some embodiments, constructing a node roadmap according to the first node includes:

[0033] The node roadmap is constructed according to the parent-child relationship of the first node and the level information of the first node.

[0034] In some embodiments, searching for a coarse path in the node roadmap using a second path search algorithm includes:

[0035] Obtaining the plane position coordinates and the normalized risk value of each node in the node roadmap;

[0036] Calculating an inter-node cost value according to the plane position coordinates and the normalized risk value;

[0037] Searching from the starting point to the end point in the node route map according to the inter-node cost value;

[0038] Calculate the total cost of the search path;

[0039] The coarse path is determined from the plurality of search paths according to the total cost value.

[0040] In some embodiments, the optimizing the coarse path by using an optimal control method includes:

[0041] constructing a navigation safety corridor in the navigation area according to the rough path;

[0042] Constructing a boundary value constraint function according to the navigation safety corridor;

[0043] Constructing a ship maneuverability model;

[0044] Constructing a ship dynamics constraint function according to the ship maneuverability model;

[0045] Construct optimization objective function;

[0046] The coarse path is optimized according to the boundary value constraint function and the optimization objective function.

[0047] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides a polar ship path planning device, the device comprising:

[0048] The first module is used to obtain sea ice data and spatial data of all navigation areas of polar ships, wherein the polar sea ice data includes sea ice thickness data and sea ice density data;

[0049] A second module is used to calculate a first passable value of the navigation area according to the sea ice data;

[0050] A third module is used to construct a three-dimensional navigation risk map according to the first passable value and the spatial data;

[0051] The fourth module is used to slice the three-dimensional navigation risk map to obtain a plurality of two-dimensional plane grid maps;

[0052] A fifth module is used to perform a path search on each layer of the two-dimensional plane grid map by using a first path search algorithm to obtain a first node on the corresponding path;

[0053] A sixth module is used to construct a node roadmap according to the first node;

[0054] A seventh module is used to search for a coarse path in the node route map by using a second path search algorithm;

[0055] The eighth module is used to optimize the rough path by an optimal control method to obtain a target path, and the target path is used to guide the navigation track of the polar ship.

[0056] To achieve the above object, another aspect of an embodiment of the present application provides an electronic device, including:

[0057] at least one processor;

[0058] at least one memory for storing at least one program;

[0059] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0060] The embodiments of the present application include at least the following beneficial effects: The present application provides a polar ship path planning method and device and electronic equipment, which obtains sea ice thickness data, sea ice density data and spatial data, calculates a first passable value of the navigation area according to the sea ice data, constructs a three-dimensional navigation risk map according to the first passable value and the spatial data, slices the three-dimensional navigation risk map to obtain a number of two-dimensional plane grid maps, uses a first path search algorithm to perform path search on each layer of the two-dimensional plane grid map to obtain the first node on the corresponding path, and then constructs a node route map according to the first node, searches the node route map through a second path search algorithm to obtain a coarse path, and then optimizes the coarse path through an optimal control method to obtain a target path, thereby achieving fast calculation speed, short planning path, low planning risk and meeting the functions of ship operation, effectively reducing the navigation risk of polar ships during navigation and improving navigation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flow chart of a polar ship path planning method provided in an embodiment of the present application;

[0062] Figure 2 is a specific flow chart of the polar ship path planning method provided in an embodiment of the present application;

[0063] Figure 3 is a schematic diagram of map slicing provided in an embodiment of the present application;

[0064] Figure 4 This is a first schematic diagram of a JPS algorithm for finding a node provided in an embodiment of the present application;

[0065] Figure 5 This is a second schematic diagram of the JPS algorithm for finding a node provided in an embodiment of the present application;

[0066] Figure 6 is a connection diagram of a screening node provided in an embodiment of the present application;

[0067] Figure 7 It is a schematic diagram of the Dijkstra algorithm for finding a path provided in an embodiment of the present application;

[0068] Figure 8 is a schematic diagram of a safety corridor provided in an embodiment of the present application;

[0069] Fig. 9 is a schematic diagram of an optimized path provided in an embodiment of the present application;

[0070] Fig.10 It is a structural schematic diagram of a polar ship path planning device provided in an embodiment of the present application;

[0071] Fig.11It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application, they are only examples of devices and methods consistent with some aspects of the embodiments of the present application.

[0073] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0074] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0075] Dijkstra's algorithm is a breadth-first search algorithm that solves the single-source shortest path problem for weighted directed graphs. There are many variations of the algorithm; Dijkstra's original version finds the shortest path between two vertices, but more common variations fix a vertex as the source node and then find the shortest paths from that vertex to all other nodes in the graph, producing a shortest path tree. The algorithm is often used in routing algorithms or as a submodule of other graph algorithms.

[0076] JPS (Jump Point Search) is a path search algorithm that uses a graph-based heuristic search method. Its design goal is to reduce unnecessary search steps as much as possible, thereby improving search efficiency.

[0077] The present application provides a polar ship path planning method and device and electronic equipment. After obtaining sea ice thickness data, sea ice density data and spatial data, the present application calculates a first passable value of the navigation area according to the sea ice data, constructs a three-dimensional navigation risk map according to the first passable value and the spatial data, slices the three-dimensional navigation risk map to obtain a plurality of two-dimensional plane grid maps, uses a first path search algorithm to perform path search on each layer of the two-dimensional plane grid map to obtain a first node on the corresponding path, and then constructs a node route map according to the first node, searches the node route map through a second path search algorithm to obtain a coarse path, and then optimizes the coarse path through an optimal control method to obtain a target path, thereby achieving fast calculation speed, short planning path, low planning risk and meeting the functions of ship operation, effectively reducing the navigation risk of polar ships during navigation and improving navigation efficiency.

[0078] The polar ship path planning method provided in the embodiment of the present application relates to the technical field of automatic route planning. The polar ship path planning method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The cloud server, the server can also be a node server in a blockchain network; the software can be an application that implements the polar ship path planning method, etc., but is not limited to the above forms.

[0079] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0080] Figure 1 is an optional flow chart of the polar ship path planning method provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0081] Step S110, obtaining sea ice data and spatial data of all navigation areas of polar ships, wherein the polar sea ice data includes sea ice thickness data and sea ice density data;

[0082] Step S120, calculating a first passable value of the navigation area according to the sea ice data;

[0083] Step S130, constructing a three-dimensional navigation risk map according to the first passable value and the spatial data;

[0084] Step S140, slicing the three-dimensional navigation risk map to obtain a plurality of two-dimensional plane grid maps;

[0085] Step S150: performing a path search on each layer of the two-dimensional plane grid map by using a first path search algorithm to obtain a first node on the corresponding path;

[0086] Step S160: construct a node roadmap according to the first node;

[0087] Step S170, searching for a coarse path in the node route map using a second path search algorithm;

[0088] Step S180: Optimize the coarse path by an optimal control method to obtain a target path, wherein the target path is used to guide the navigation track of the polar ship.

[0089] In the embodiment of the present application, when calculating the first passable value, the sea ice in the navigation area can be classified according to the sea ice thickness data to obtain several sea ice types, and then the risk index value of the corresponding sea ice type can be found in the POLARIS parameter table according to the ship ice class, and then the first passable value of the navigation area can be calculated according to the risk index value and the sea ice density data. The calculation formula of the first passable value is shown in formula (1):

[0090]

[0091] In formula (1), C i is the sea ice density data of type i ice, V i is the risk index value corresponding to the ith type of ice, and RIO is the passable value for calculating the navigation area.

[0092] In the embodiment of the present application, when constructing a three-dimensional navigation risk map, the maximum passable value among all first passable values ​​can be first obtained as the second passable value, and then the navigation risk value of the corresponding navigation area is calculated according to the first passable value and the second passable value. The calculation process of the navigation risk value is shown in formula (2) and formula (3):

[0093] RIO s = {RIO1, RIO2, …, RIO N} Formula (2);

[0094] R s,j =max(RIO s )-RIO j Formula (3);

[0095] In formula (2), RIO N Indicates the passable value of N area, RIO s represents the set of passable values ​​of all navigation areas; in formula (3), max(RIO s ) represents the largest RIO value in the set (the second most accessible value), RIO j represents the first passable value of region j, R s,j Represents the navigation risk value of area j.

[0096] Then, a two-dimensional plane grid map is constructed based on the spatial data, where the horizontal and vertical coordinates in the two-dimensional plane grid map can be either longitude and latitude or two perpendicular lines. However, the length and width of the single grid must be equal, that is, it must be a square grid, and all grids in the area are the same size as a single grid. Then, the navigation risk value is normalized according to the two-dimensional plane grid map to obtain the normalized risk value. The calculation process of the normalized risk value is shown in formula (4):

[0097]

[0098] In formula (4), W represents the width of the navigation area, L represents the length of the navigation area, and R j represents the normalized risk value of region j.

[0099] Then, a three-dimensional navigation risk map is constructed based on the two-dimensional plane grid map and the normalized risk value. Specifically, the normalized risk value is matched with the geographic spatial location, that is, the navigation risk value R j The third dimension added to the j area of ​​the two-dimensional grid is used as the third dimension height value to construct a three-dimensional grid map, namely the three-dimensional navigation risk map. The x and y planes in the three-dimensional navigation risk map belong to the geographical location plane, and the three-dimensional height z is the size of the navigation risk. The navigation risk is regarded as an obstacle in the three-dimensional grid map.

[0100] In the embodiment of the present application, the process of slicing the three-dimensional navigation risk map to obtain a plurality of two-dimensional plane grid maps can be to cut the three-dimensional navigation risk map with a plane parallel to the two-dimensional geographic space according to the three-dimensional height in the three-dimensional navigation risk map, and the three-dimensional navigation risk map can be sliced ​​with a unit height of 1 or sliced ​​with other basis to cut the three-dimensional navigation risk map into a plurality of two-dimensional plane maps, and the effect is as follows: Figure 3 As shown. It can be understood that if the navigation risk of a grid is higher than the plane being cut, the grid on this two-dimensional plane is considered an obstacle; if the navigation risk of a grid is lower than the plane being cut, the grid on this two-dimensional plane is considered a passable area. Thus, several two-dimensional plane grid maps are formed.

[0101] In the embodiment of the present application, the first path search algorithm may be a JPS algorithm. Figure 2 As shown in the figure, the first path search algorithm is used to search for the path of each layer of the two-dimensional plane grid map to obtain the first node on the corresponding path. The JPS algorithm can be used to find the path from the starting point to the end point in each layer of the cut two-dimensional plane grid map. The starting point and the end point at this time are the initial starting point and the end point that do not consider the z value. During the search process, if the path is not found, the search is continued in the next layer of the two-dimensional plane grid map; if the path is found, the node on the path during the search process (the second node) is saved until all plane searches are completed. The schematic diagram of all second nodes in the plane is shown in the figure. Figure 4 and Figure 5 As shown. The node information saved at this time includes the plane coordinate position, the plane coordinate position of the parent node and the layer information. Then all nodes are projected to the same two-dimensional plane to determine whether the nodes overlap. If there is overlap, the bottom node (the first node) is retained and the rest of the nodes are discarded. If there is no overlap, the current node (the first node) is saved.

[0102] In the embodiment of the present application, the information stored in each node (first node) after screening is (x, y, z, x parent ,y parent ,z parent ). In this embodiment, when constructing a node route map based on the first node, the node is connected according to the parent-child relationship, and z and z are not considered at this time. parent , start traversing from the end point and find its parent node P1(x parent ,y parent ) and connect them, then take the P1 node as the child node and continue to look for its parent node P2, and iterate continuously until all nodes are connected according to the parent-child relationship; secondly, connect the nodes of different layers directly and perform collision detection. If the node connection collides with an obstacle, the connection is cancelled, and the roadmaps formed by the two relationships are merged to construct a node roadmap.

[0103] It can be understood that when connecting nodes according to the node layer relationship, z needs to be considered at this time. First, take the starting point as the base point and traverse each subsequent node from the starting point. If the z value of the starting point is not equal to the z value of the next node P3, that is, the starting point and the P3 node are not on the same cut-out level, then determine whether the connection between the starting point and the P3 node crosses the land. If it crosses, cancel the connection. If not, connect. If the z value of the starting point is equal to the z value of the next node P3, continue to determine the layer relationship between the starting point and the next node P4. When the starting point and all nodes are traversed, continue to use the P3 node as the base point to traverse the layer relationship between the P3 node and subsequent nodes. When the end point is used as the base point, the traversal is completed, forming a node route map composed of layer relationships.

[0104] Then, the two node roadmaps formed by connecting nodes according to the node parent-child relationship and the node layer number relationship are merged to form a Figure 6 The complete node roadmap is shown.

[0105] In the embodiment of the present application, the second path search algorithm may be a Dijkstra algorithm. The second path search algorithm is used to search for a rough path in the node route map. The plane position coordinates and normalized risk value of each node in the node route map may be obtained first, and the inter-node cost value may be calculated based on the plane position coordinates and normalized risk value. The calculation formula of the inter-node cost value is shown in formula (5):

[0106]

[0107] In formula (5), x and y represent the location coordinates of the node, R represents the normalized navigation risk value of the node, and n and n-1 represent the current node and the previous node connected to it.

[0108] Then, based on the cost between nodes, search from the starting point to the end point in the node route map and calculate the total cost of the search path. When the search reaches the end point, the path with the smallest total cost is selected as the rough path (original path). The specific effect is as follows: Figure 7 shown.

[0109] After the original path is obtained, the rough path is optimized by the optimal control method. Specifically, after constructing a navigation safety corridor in the navigation area according to the rough path, a boundary value constraint function is constructed according to the navigation safety corridor, and then a ship maneuverability model is constructed. The ship dynamics constraint function is constructed according to the ship maneuverability model, and after constructing the optimization objective function, the rough path is optimized according to the boundary value constraint function and the optimization objective function. It can be understood that the construction process of the navigation safety corridor is to take the starting point as the base point, and expand the unit distance of Δs in a certain direction in a certain order, for example, the north, west, south, and east are the expansion order. First, Δs is expanded northward at the location of the starting point, and it is determined whether the expanded area is in contact with obstacles or boundaries. If it is in contact, the area expanded to the north is eliminated. If it is not in contact, the area expanded to the north is retained; then Δs is continued to be expanded westward on the basis of the northward expansion, and it is determined whether it is in contact as in the northward expansion process; similarly, it is continued to expand southward and eastward. If an expansion direction is eliminated, skip this expansion direction and continue to expand in the next direction, and ensure that the area formed after each expansion is a parallelogram, until all directions are expanded, and finally form a safe navigation area around the starting point. Then select the intersection of the starting point safe navigation area and the original path as a key node, and use this key node as the base point to continue to repeat the starting point expansion steps. This step is repeated iteratively, and the final end point expansion is completed, and the construction of the safe navigation corridor of the original path is completed. Figure 8 shown.

[0110] It can be understood that the process of constructing the boundary value constraint function can first construct a ship kinematic model, for example, ignoring the ship's heave, roll and pitch to construct a 3-DOF ship maneuvering model:

[0111]

[0112]

[0113] η=[xy ψ] T Formula (8);

[0114] v=[uvr] T Formula (9);

[0115] τ=[f u f v f r ] T Formula (10);

[0116] Among them, η represents the ship's position and heading angle, v represents the ship's lateral, longitudinal and heading angular velocity, τ represents the ship's lateral thrust, longitudinal thrust and heading angular thrust, M is the inertia matrix, C is the Coriolis centripetal matrix, D is the damping matrix, and J is the coordinate transformation matrix.

[0117] x(t)=(x,y,ψ,u,v,r) T Formula (11);

[0118] u(t)=(f u ,f v ,f r ) T Formula (12);

[0119] Among them, formula (11) represents the ship state quantity, and formula (12) represents the ship control input quantity. Formula (6) can be converted into a state space equation according to the nonlinear time-varying system:

[0120]

[0121] q1=[0 0 1 0 0 0] formula (14);

[0122]

[0123] Among them, q1 and q2 represent the earth-ship coordinate system transformation matrix.

[0124] Specifically, the construction of constraints and optimization objectives requires the construction of an optimization objective equation:

[0125]

[0126]

[0127] In the formula, t f represents the navigation time, w represents the optimization objective equation, Represents the heading angular acceleration, and α is the weight coefficient used for adjustment.

[0128] Specifically, the construction of constraints and optimization objectives requires the construction of constraint equations:

[0129]

[0130] Formula (18) is the ship dynamics constraint, and the specific form of the constraint is the same as formula (13).

[0131] {x0,y0,ψ0,u0,v0,r0}={x s ,y s ,ψ s ,u s ,0,0} formula (19);

[0132] {x end ,y end ,ψ end ,u end ,vend ,r end}={x g ,y g ,ψ g ,u g ,0,0} formula (20);

[0133] Formula (19) and Formula (20) are the state constraints of the starting and ending points of the entire optimization.

[0134] {x end-mode ,y end-node ,ψ end-node ,u end-node ,v end-node ,r end-node}={x npde ,y node ,ψ npde ,u node ,0,0} formula (21);

[0135] {x start-node ,y start-node ,ψ start-node ,u start-node ,v start-node ,r start-node}={x npde ,y node ,ψ npde ,u node ,0,0} formula (22);

[0136]

[0137] Formula (21) and formula (22) are the state constraints for each key node optimization, and the starting state of the next stage optimization is the ending state of the previous stage optimization. Formula (23) is the calculation method of the heading angle constraint at the key node, that is, the heading angle points to the next key node.

[0138] x corridor_low ≤x(t)≤x corridor_upp Formula (24);

[0139] y corridor_low ≤y(t)≤y corridor_upp Formula (25);

[0140] Formula (24) and Formula (25) are the boundary constraints of the constructed navigation safety corridor, which ensure that the optimization is within the navigation safety corridor.

[0141] After the above optimization process, a safe navigation (target path) that meets the ship dynamics constraints can be obtained. The specific effects are as follows: Fig. 9 shown.

[0142] In summary, the method provided in the embodiment of the present application obtains sea ice thickness data, sea ice density data and spatial data of all navigation areas of polar ships, divides a plurality of sea ice types according to the sea ice thickness data, calculates the passable value of the navigation area by using POLARIS, calculates the navigation risk value of the navigation area according to the passable value of a certain navigation area and the maximum passable value calculated in all navigation areas, and then normalizes the navigation risk value and the two-dimensional plane distance, constructs a three-dimensional navigation risk map according to the navigation risk value data of the navigation area obtained after the normalization and the spatial data of the corresponding area, slices the three-dimensional navigation risk map to form a plurality of two-dimensional plane maps, uses the JPS algorithm to search for a path from the starting point to the end point in each layer of the map, saves the nodes on the path, and then projects the nodes to the same two-dimensional plane and filters them, and sorts the filtered nodes according to the parent-child relationship between the nodes and the number of layers. The nodes are connected and collision detection is performed to form a node route map. The Dijkstra algorithm is used to traverse and search all nodes in the route map from the starting point. The cost value is calculated by the plane distance between the nodes and the risk value to search for a coarse path. A navigation safety corridor is constructed on the coarse path. The original path is optimized in the corridor using the optimal control method so that the optimized trajectory can achieve the optimization of a certain goal on the basis of satisfying the maneuverability of the ship. The present invention quantifies the sea ice risk, visualizes the sea ice risk and the actual plane map to form a three-dimensional navigation risk map, which can be used not only for sea ice risk, but also for other risk fields. The proposed fusion JPS-Dijkstra algorithm to search for the coarse path can also be used to find the path in the real three-dimensional space. The final trajectory optimization is also applicable to the real three-dimensional space. The whole set of methods can ultimately achieve fast calculation speed, short planning path, low planning risk and meet the maneuverability requirements of the ship.

[0143] Reference Fig.10 , an embodiment of the present application provides a polar ship path planning device, the device comprising:

[0144] The first module 1010 is used to obtain sea ice data and spatial data of all navigation areas of polar ships, wherein the polar sea ice data includes sea ice thickness data and sea ice density data;

[0145] The second module 1020 is used to calculate a first passable value of the navigation area according to the sea ice data;

[0146] The third module 1030 is used to construct a three-dimensional navigation risk map according to the first passable value and the spatial data;

[0147] The fourth module 1040 is used to slice the three-dimensional navigation risk map to obtain a plurality of two-dimensional plane grid maps;

[0148] The fifth module 1050 is used to perform path search on each layer of the two-dimensional plane grid map by using a first path search algorithm to obtain a first node on the corresponding path;

[0149] A sixth module 1060 is used to construct a node roadmap according to the first node;

[0150] A seventh module 1070 is used to search for a coarse path in the node route map by using a second path search algorithm;

[0151] The eighth module 1080 is used to optimize the coarse path by an optimal control method to obtain a target path, wherein the target path is used to guide the navigation trajectory of the polar ship.

[0152] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0153] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned polar ship path planning method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0154] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0155] See also Fig.11 , Fig.11 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0156] The processor 1110 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0157] The memory 1120 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1120 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1120, and the processor 1110 is called to execute the polar ship path planning method of the embodiment of the present application;

[0158] Input / output interface 1130, used to implement information input and output;

[0159] The communication interface 1140 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0160] bus 1150 , which transmits information between the various components of the device (e.g., processor 1110 , memory 1120 , input / output interface 1130 , and communication interface 1140 );

[0161] The processor 1110 , the memory 1120 , the input / output interface 1130 , and the communication interface 1140 are connected to each other in communication within the device via the bus 1150 .

[0162] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0163] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0164] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0166] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0167] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0168] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0169] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0171] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0172] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A polar ship path planning method, characterized in that: The method comprises the following steps: Acquire sea ice data and spatial data of all navigation areas of polar ships, wherein the polar sea ice data includes sea ice thickness data and sea ice density data; Calculating a first passable value of the navigation area according to the sea ice data; constructing a three-dimensional navigation risk map according to the first passable value and the spatial data; Slicing the three-dimensional navigation risk map to obtain a plurality of two-dimensional plane grid maps; Performing a path search on each layer of the two-dimensional plane grid map by using a first path search algorithm to obtain a first node on the corresponding path; Building a node roadmap according to the first node; Searching in the node route graph by a second path search algorithm to obtain a coarse path; The rough path is optimized by an optimal control method to obtain a target path, and the target path is used to guide the navigation track of the polar ship.

2. The method according to claim 1, characterized in that The calculating the first passable value of the navigation area according to the sea ice data comprises: Classifying the sea ice in the navigation area according to the sea ice thickness data to obtain a plurality of sea ice types; Obtaining a risk index value corresponding to the sea ice type; A first passability value of the navigation area is calculated according to the risk index value and the sea ice concentration data.

3. The method according to claim 1, characterized in that The constructing of a three-dimensional navigation risk map according to the first passable value and the spatial data includes: Obtaining the maximum passable value among all the first passable values ​​as the second passable value; Calculating a navigation risk value corresponding to the navigation area according to the first passable value and the second passable value; Constructing a two-dimensional plane grid map according to the spatial data; Normalizing the navigation risk value according to the two-dimensional plane grid map to obtain a normalized risk value; The three-dimensional navigation risk map is constructed according to the two-dimensional plane grid map and the normalized risk value.

4. The method according to claim 1, characterized in that: The three-dimensional navigation risk map is sliced ​​to obtain a plurality of two-dimensional plane grid maps, including: Obtaining the three-dimensional height in the three-dimensional navigation risk map; The three-dimensional navigation risk map is sliced ​​according to the three-dimensional height to obtain a plurality of the two-dimensional plane grid maps.

5. The method according to claim 1, characterized in that The performing path search on each layer of the two-dimensional plane grid map by using a first path search algorithm to obtain a first node on a corresponding path includes: Performing path search on each layer of the two-dimensional plane grid map in turn; When a path is found, the second node in the path is saved; Projecting all the second nodes onto a two-dimensional plane; The second node is screened according to the projection point on the two-dimensional plane to obtain the first node, and the information of the first node includes the plane coordinate position, the plane coordinate position of the parent node and the layer information.

6. The method according to claim 5, characterized in that The step of constructing a node roadmap according to the first node includes: The node roadmap is constructed according to the parent-child relationship of the first node and the level information of the first node.

7. The method according to claim 3, characterized in that The step of searching for a coarse path in the node route map by using a second path search algorithm comprises: Obtaining the plane position coordinates and the normalized risk value of each node in the node roadmap; Calculating an inter-node cost value according to the plane position coordinates and the normalized risk value; Searching from the starting point to the end point in the node route map according to the inter-node cost value; Calculate the total cost of the search path; The coarse path is determined from the plurality of search paths according to the total cost value.

8. The method according to claim 1, characterized in that The optimizing the coarse path by using an optimal control method comprises: constructing a navigation safety corridor in the navigation area according to the rough path; Constructing a boundary value constraint function according to the navigation safety corridor; Constructing a ship maneuverability model; Constructing a ship dynamics constraint function according to the ship maneuverability model; Construct optimization objective function; The coarse path is optimized according to the boundary value constraint function and the optimization objective function.

9. A polar ship path planning device, characterized in that: The device comprises: The first module is used to obtain sea ice data and spatial data of all navigation areas of polar ships, wherein the polar sea ice data includes sea ice thickness data and sea ice density data; A second module is used to calculate a first passable value of the navigation area according to the sea ice data; A third module is used to construct a three-dimensional navigation risk map according to the first passable value and the spatial data; The fourth module is used to slice the three-dimensional navigation risk map to obtain a plurality of two-dimensional plane grid maps; A fifth module is used to perform a path search on each layer of the two-dimensional plane grid map by using a first path search algorithm to obtain a first node on the corresponding path; A sixth module is used to construct a node roadmap according to the first node; A seventh module is used to search for a coarse path in the node route map by using a second path search algorithm; The eighth module is used to optimize the rough path by an optimal control method to obtain a target path, and the target path is used to guide the navigation track of the polar ship.

10. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 8.

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