CN113961004A - Pirate area ship route planning method and system, electronic equipment and storage medium - Google Patents
Pirate area ship route planning method and system, electronic equipment and storage medium Download PDFInfo
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- CN113961004A CN113961004A CN202111137498.2A CN202111137498A CN113961004A CN 113961004 A CN113961004 A CN 113961004A CN 202111137498 A CN202111137498 A CN 202111137498A CN 113961004 A CN113961004 A CN 113961004A
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Abstract
The invention discloses a method and a system for planning a ship route in a pirate area, electronic equipment and a storage medium, and belongs to the technical field of ship route planning. The method comprises the following steps: s1, performing clustering analysis on the historical pirate activity points, and constructing a grid map of the barrier area and the navigable area; s2, randomly scattering points in the grid map, and then connecting the nodes through a local planner to form a path network graph; s3, connecting the starting point and the end point of the route with the path graph, and searching an initial path from the starting point to the end point of the route through an A-star search algorithm; s4, extracting key nodes in the initial path nodes, and connecting the key nodes to form an optimized path with fewer inflection points; and S5, smoothing the optimized path with few inflection points to obtain a smooth optimized path. The invention uses the PRM algorithm to plan the ship route, improves the utilization rate of sampling points, reduces the number of nodes on the original path, shortens the length of the path and improves the smoothness of the path.
Description
Technical Field
The invention belongs to the technical field of ship route planning, and particularly relates to a ship route planning method and system in a pirate area, electronic equipment and a storage medium.
Background
When the ship sails on the sea, the ship sometimes passes through a pirate activity high-incidence area, so that the research on the course planning method in the pirate activity area has very deep practical significance and theoretical significance for ensuring the sailing safety of the ship.
The ship sails on the sea, route planning is the core content of the ship, and at present, the common methods for planning the route of the ship mainly comprise an A-star algorithm, a genetic algorithm, a simulated annealing algorithm, a particle swarm algorithm, an ant colony algorithm, a PRM algorithm and the like.
The PRM (Probalistic roadmap) algorithm is a method based on graph search and comprises two steps of a learning stage and an inquiring stage, a completely random sampling strategy is adopted when a path network graph is constructed by the traditional PRM algorithm, when the number of sampling points is fixed, some sampling points fall in an obstacle space, the number of the sampling points in a free space is reduced, the planning of a path can not be completed, the path searched in the inquiring stage is not optimal, and the problems of excessive path nodes and over steep turning angles of partial nodes exist.
Therefore, for planning a ship route by using a PRM algorithm, how to improve the utilization rate of sampling points, reduce the number of nodes on an original path, shorten the length of the path and improve the smoothness of the path is an urgent problem to be solved.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a pirate area ship route planning method, a pirate area ship route planning system, electronic equipment and a storage medium based on an improved PRM algorithm, which are used for solving the problems of low utilization rate of sampling points of the PRM algorithm, excessive path nodes and over-steep corners of partial nodes.
In order to achieve the aim, the invention provides a method for planning a ship route in a pirate area, which comprises the following steps:
s1, performing clustering analysis on the historical pirate activity points, and constructing a grid map of the barrier area and the navigable area;
s2, randomly scattering points in the grid map, and then connecting the nodes through a local planner to form a path network graph;
s3, connecting the starting point and the end point of the route with the path graph, and searching an initial path from the starting point to the end point of the route through an A-star search algorithm;
s4, extracting key nodes in the initial path nodes, and connecting the key nodes to form an optimized path with fewer inflection points;
and S5, smoothing the optimized path with few inflection points to obtain a smooth optimized path.
In some alternative embodiments, step S1 includes:
s11, carrying out clustering analysis on historical pirate activity points of the airline planning sea area by using a K-means clustering algorithm, and extracting a clustering center coordinate of each cluster;
s12, constructing a Voronoi diagram according to the central coordinates, and extracting the boundary of the pirate activity area in the Voronoi diagram as the boundary of the obstacle;
and S13, constructing a grid map of the obstacle area and the navigable area according to the obstacle boundary.
In some alternative embodiments, step S2 includes:
randomly scattering points in the grid map;
and generating a new node in the navigable area for the sampling point falling in the obstacle area so as to replace the node falling in the obstacle area.
In some optional embodiments, generating the new node in the navigable area comprises:
for the sampling point q (x) falling on the barrier areaq，yq) Generating a node of a new node replacement barrier area by using a random node generation function RandomNode (q, r), wherein q represents a node position, and r represents a radius;
new node B satisfies the radius
And node B is a node within a navigable area.
In some optional embodiments, the step S4 uses a D-P algorithm to extract a key node in the initial path nodes, including:
s41, determining an initial threshold value phi, and connecting an initial point of an initial path node and a target point to form a reference line;
s42, calculating the distance d from all nodes between the initial point and the target point to the reference line to obtain a distance baseThe farthest node of the alignment line is the farthest distance d corresponding to the farthest nodemComparing with an initial threshold value phi;
s43, if dm<Phi, the reference line segment is used as a new path, and the processing of the segment of the path is finished;
s44, if dm>Phi, bringing the node into a key node set, connecting the key node with the initial point and the target point respectively to form two new reference lines, and repeating the steps S42 to S44 on the two reference lines to extract a new key node;
and S45, finally obtaining a key node set, and sequentially connecting key nodes to obtain an optimized path containing fewer nodes.
In some optional embodiments, the initial threshold value Φ is determined according to the distribution of the map obstacles and the number of the initial path nodes, a smaller threshold value is selected in a complex map containing more obstacles, and a larger threshold value is selected for extracting the path key nodes in a map containing fewer obstacles or simple in obstacle distribution.
In some alternative embodiments, step S5 uses euler spiral fitting for path smoothing, including:
s51, setting the coordinate of the extracted key node as Q (x)i，yi) (i ═ 1, 2, …, k), dividing the optimized path containing fewer nodes into k-1 segments;
52. carrying out Euler spiral fitting on the mth segment of the path, wherein the coordinates of key nodes at two ends of the path are (x)m，ym)、(xm+1，ym+1) Two points satisfy:
wherein s ismIs the arc length of the m-th Euler spiral, theta0mAnd k is0mAre respectively (x)m，ym) Tangent angle and curvature at point, cmA parameter representing the sharpness of curvature;
S53、(xm+1，ym+1) Is the m-th Euler screwThe end point of the line and the starting point of the euler spiral of the (m + 1) th segment, wherein all the parameters are satisfied:
wherein, theta0m+1And k is0m+1Respectively representing the initial tangent angle and the initial curvature, theta, of the Euler spiral of the m +1 th segmentmAnd k ismRespectively, the m-th Euler spiral is in (x)m+1，ym+1) Tangent angle and curvature at the point;
and S54, sequentially carrying out Euler spiral fitting on the k-1 section of path according to the step S52 and the step S53 to obtain a smooth optimized path.
The invention also provides a system for planning ship routes in the pirate area, which comprises the following steps:
the grid map module is used for carrying out cluster analysis on the historical pirate activity points and constructing grid maps of the barrier area and the navigable area;
the path network graph module is used for randomly scattering points in the grid map and then connecting the nodes through a local planner to form a path network graph;
the initial path module is used for connecting the starting point and the end point of the route with the path graph and searching an initial path from the starting point to the end point of the route through an A-star search algorithm;
the path optimization module is used for extracting key nodes in the initial path nodes and connecting the key nodes to form an optimized path with fewer inflection points;
and the path smoothing module is used for smoothing the optimized path with less inflection points to obtain a smooth optimized path.
The invention also provides an electronic device comprising one or more processors and a memory;
one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the pirate area ship route planning method described above.
The invention also provides a computer-readable storage medium, in which a program code is stored, wherein the above-mentioned pirate regional ship route planning method is executed when the program code is executed.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention uses the PRM algorithm to plan the ship route, generates new nodes in the navigable area to replace the nodes in the barrier area, improves the utilization rate of sampling points, reduces the number of nodes on the original path, shortens the path length and improves the smoothness of the path.
Drawings
Fig. 1 is a flowchart of a method for planning a ship route in a pirate area according to an embodiment of the present invention;
fig. 2 is a flowchart of obstacle grid map construction according to an embodiment of the present invention;
fig. 3 is a diagram of a method for generating a random node according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a D-P algorithm for extracting a path key node according to an embodiment of the present invention;
fig. 5 is a flowchart for planning an initial path by an improved PRM algorithm according to an embodiment of the present invention;
fig. 6 is a flowchart of path optimization according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The planned path has fewer inflection points and smooth path, is close to actual navigation application, and can provide a reasonable and effective route planning method for ships navigating in pirate activity areas.
The embodiment of the invention provides a pirate area ship route planning method based on an improved PRM algorithm, which comprises the following steps as shown in figure 1:
s1: and performing cluster analysis on historical pirate activity points of the sea area planned by the airline to construct a grid map of the barrier area and the navigable area. Specifically, step S1, as shown in fig. 2, includes the following sub-steps:
s11: carrying out clustering analysis on the historical pirate activity points by using a K-means clustering algorithm, and extracting a clustering center coordinate of each cluster;
s12: constructing a Voronoi diagram according to the central coordinates, and extracting the boundary of the pirate activity area of the Voronoi diagram as the boundary of the obstacle;
s13: and constructing a grid map of the obstacle area and the navigable area according to the obstacle boundary.
S2: randomly scattering points in the grid map, and then connecting the nodes through a local planner to form a path network diagram.
Specifically, as shown in fig. 5, for a sampling point falling in an obstacle space, a new node is generated to replace a node in the obstacle using a random node generation function RandomNode (q, r), where q (x)q，yq) Representing the node location and r the radius. As shown in fig. 3, the gray area represents an obstacle, a node a in the obstacle is used as a center of the circle, and an appropriate radius r is used as a dotted circle according to the obstacle situation of the environment, so that each node on the dotted circle belonging to the free space may be a replacement node of a, a node B is randomly generated by a random node generation function, and the node B (x) is generated by a random node generation functionB，yB) The following conditions are satisfied: radius of
Node B is a node within a navigable area.
S3: and the starting point and the end point of the route are connected with the path graph, and the path from the starting point to the end point of the route is searched through an A-star search algorithm, and the path is the initial planning path.
S4: and extracting key nodes, namely extracting the key nodes in the initial path nodes, and connecting the path key nodes to form an optimized path with fewer inflection points. Specifically, the method for extracting the key node in the initial path node by using the D-P algorithm, as shown in FIG. 6, comprises the following sub-steps:
s41: determining an initial threshold phi, and connecting an initial point of an initial path node with a target point to form a reference line;
s42: calculating the distances d from all the nodes between the initial point and the target point to the reference line to obtain the node farthest from the reference line, and calculating the farthest distance dmComparing with an initial threshold value phi;
s43: if d ism<Phi, the reference line segment is used as a new path, and the processing of the segment of the path is finished;
s44: if d ism>Phi, bringing the node into a key node set, connecting the key node with the initial point and the target point respectively to form two new reference lines, and repeating the steps S42 to S44 on the two reference lines to extract a new key node;
s45: and finally, obtaining a key node set, and sequentially connecting key nodes to obtain an optimized path containing fewer nodes.
Specifically, as shown in fig. 4, the initial path node is a1～A8. From FIG. 4(a), a suitable threshold φ is set, connection A1、A8At A1And A8Distance line segment A in the path node between1A8The farthest node is A3，A3And A1The distance of A8 is d and is larger than a preset threshold value phi, and A is3Is considered a key node. Are respectively connected with A1、A3And A3、A8Two new reference line segments A are formed1A3And A3A8. As can be seen from FIG. 4(b), at A1And A3The line segment A1A3The path node farthest away is A2At A3And A8Line segment A of the distance between3A8The farthest path node is A5And respectively comparing with the threshold value to find out new path key nodes. Repeating the steps in sequence to obtain a set of path key nodes, and connecting the path key nodes to obtain a new path B shown in fig. 4(c)1—B2—B3—B4。
The initial threshold phi is determined according to the distribution condition of the map obstacles and the number of initial path nodes, a smaller threshold is selected from a complex map containing more obstacles, and a larger threshold is selected from a map containing fewer obstacles or simple in obstacle distribution for extracting the path key nodes.
S5: and smoothing the path, namely smoothing the optimized path with less inflection points to obtain a smooth optimized path. Specifically, the method comprises the following steps:
s51: let the coordinate of the key node extracted by the D-P algorithm be Q (x)i，yi) (i ═ 1, 2, …, k), dividing the optimized path containing fewer nodes into k-1 segments;
s52: carrying out Euler spiral fitting on the mth segment of the path, wherein the coordinates of key nodes at two ends of the path are (x)m，ym)、(xm+1，ym+1) Two points satisfy:
wherein s ismIs the arc length of the m-th Euler spiral, theta0mAnd k is0mAre respectively (x)m，ym) Tangent angle and curvature at point, cmA parameter representing the sharpness of curvature;
S53：(xm+1，ym+1) The end point of the m-th Euler spiral and the starting point of the m + 1-th Euler spiral are shown, and the parameters are as follows:
wherein, theta0m+1And k is0m+1Respectively representing the initial tangent angle and the initial curvature, theta, of the Euler spiral of the m +1 th segmentmAnd k ismRespectively, the m-th Euler spiral is in (x)m+1，ym+1) Tangent angle and curvature at the point;
s54: and sequentially carrying out Euler spiral fitting on the k-1 section of path according to the conditions of S52 and S53 to obtain a smooth optimized path.
The invention also provides an electronic device comprising one or more processors and a memory; one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the pirate area ship route planning method described above.
The invention also provides a computer-readable storage medium, in which a program code is stored, wherein the above-mentioned pirate regional ship route planning method is executed when the program code is executed.
In conclusion, the obstacle map is constructed, the historical pirate activity points are subjected to clustering analysis by using a K-means clustering algorithm, the clustering center coordinates of each cluster are extracted, a Voronoi diagram is constructed according to the coordinates, the boundaries of the Voronoi diagram are extracted to serve as the boundaries of the obstacles, and then the grid map of the obstacle area and the navigable area is constructed; using a PRM algorithm to construct a path network map, randomly scattering points in a given grid map, using a random node generating function to generate new nodes to replace nodes in an obstacle, improving the utilization rate of sampling points, and connecting the nodes through a local planner to form the path network map; the PRM algorithm searches an initial path, a given starting point and an end point of the route are connected with a path graph, and the path from the starting point to the end point of the route is found through an A-star search algorithm; path optimization, key node extraction, namely extracting key nodes in the initial path nodes by using a D-P algorithm, and connecting the path key nodes to form an optimized path with fewer inflection points; and (4) smoothing the path, and fitting the optimized path with less inflection points by using Euler spiral to obtain a smooth optimized path. For the flight path planning by using the PRM algorithm, the utilization rate of sampling points is improved, the number of nodes on an original path is reduced, the length of the path is shortened, the smoothness of the path is improved, and the method is close to the actual navigation application.
It should be noted that, according to the implementation requirement, each step/component described in the present application can be divided into more steps/components, and two or more steps/components or partial operations of the steps/components can be combined into new steps/components to achieve the purpose of the present invention.
It will be understood by those skilled in the art that the foregoing is merely a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included within the scope of the present invention.
Claims (10)
1. A pirate area ship route planning method is characterized by comprising the following steps:
s1, performing clustering analysis on the historical pirate activity points, and constructing a grid map of the barrier area and the navigable area;
s2, randomly scattering points in the grid map, and then connecting the nodes through a local planner to form a path network graph;
s3, connecting the starting point and the end point of the route with the path graph, and searching an initial path from the starting point to the end point of the route through an A-star search algorithm;
s4, extracting key nodes in the initial path nodes, and connecting the key nodes to form an optimized path with fewer inflection points;
and S5, smoothing the optimized path with few inflection points to obtain a smooth optimized path.
2. The pirate area ship route planning method according to claim 1, wherein the step S1 comprises:
s11, carrying out clustering analysis on historical pirate activity points of the airline planning sea area by using a K-means clustering algorithm, and extracting a clustering center coordinate of each cluster;
s12, constructing a Voronoi diagram according to the central coordinates, and extracting the boundary of the pirate activity area in the Voronoi diagram as the boundary of the obstacle;
and S13, constructing a grid map of the obstacle area and the navigable area according to the obstacle boundary.
3. The pirate area ship route planning method according to claim 1, wherein the step S2 comprises:
randomly scattering points in the grid map;
and generating a new node in the navigable area for the sampling point falling in the obstacle area so as to replace the node falling in the obstacle area.
4. The pirate region ship route planning method according to claim 3, wherein generating new nodes in the navigable area comprises:
for the sampling point q (x) falling on the barrier areaq，yq) Generating a node of a new node replacement barrier area by using a random node generation function RandomNode (q, r), wherein q represents a node position, and r represents a radius;
new node B satisfies the radius
And node B is a node within a navigable area.
5. The pirate regional ship route planning method according to claim 1, wherein the step S4 uses a D-P algorithm to extract key nodes in the initial path nodes, and comprises:
s41, determining an initial threshold value phi, and connecting an initial point of an initial path node and a target point to form a reference line;
s42, calculating the distances d from all nodes between the initial point and the target point to the reference line to obtain the node farthest from the reference line, and determining the farthest distance d corresponding to the farthest nodemComparing with an initial threshold value phi;
s43, if dm<Phi, the reference line segment is used as a new path, and the processing of the segment of the path is finished;
s44, if dm>Phi, bringing the node into a key node set, connecting the key node with the initial point and the target point respectively to form two new reference lines, and repeating the steps S42 to S44 on the two reference lines to extract a new key node;
and S45, finally obtaining a key node set, and sequentially connecting key nodes to obtain an optimized path containing fewer nodes.
6. The pirate area ship route planning method according to claim 5, wherein an initial threshold phi is determined according to the distribution of the map obstacles and the number of initial path nodes, a smaller threshold is selected from a complex map containing more obstacles, and a larger threshold is selected from a map containing fewer obstacles or simple in obstacle distribution for extracting the path key nodes.
7. The pirate area ship route planning method according to claim 1, wherein the step S5 performs path smoothing processing using euler spiral fitting, comprising:
s51, setting the coordinate of the extracted key node as Q (x)i，yi) (i ═ 1, 2, …, k), dividing the optimized path containing fewer nodes into k-1 segments;
s52, carrying out Euler spiral fitting on the mth segment of the path, wherein the coordinates of key nodes at two ends are (x)m，ym)、(xm+1，ym+1) Two points satisfy:
wherein s ismIs the arc length of the m-th Euler spiral, theta0mAnd k is0mAre respectively (x)m，ym) Tangent angle and curvature at point, cmA parameter representing the sharpness of curvature;
S53、(xm+1，ym+1) The end point of the m-th Euler spiral and the starting point of the m + 1-th Euler spiral are shown, and the parameters are as follows:
wherein, theta0m+1And k is0m+1Respectively representing the initial tangent angle and the initial curvature, theta, of the Euler spiral of the m +1 th segmentmAnd k ismRespectively, the m-th Euler spiral is in (x)m+1，ym+1) Tangent angle and curvature at the point;
and S54, sequentially carrying out Euler spiral fitting on the k-1 section of path according to the step S52 and the step S53 to obtain a smooth optimized path.
8. A pirate area ship route planning system, comprising:
the grid map module is used for carrying out cluster analysis on the historical pirate activity points and constructing grid maps of the barrier area and the navigable area;
the path network graph module is used for randomly scattering points in the grid map and then connecting the nodes through a local planner to form a path network graph;
the initial path module is used for connecting the starting point and the end point of the route with the path graph and searching an initial path from the starting point to the end point of the route through an A-star search algorithm;
the path optimization module is used for extracting key nodes in the initial path nodes and connecting the key nodes to form an optimized path with fewer inflection points;
and the path smoothing module is used for smoothing the optimized path with less inflection points to obtain a smooth optimized path.
9. An electronic device comprising one or more processors and memory;
one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the method for pirate regional ship route planning of any of claims 1-7.
10. A computer-readable storage medium having program code stored therein, wherein the method for pirate regional ship route planning according to any one of claims 1-7 is performed when the program code is executed.
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| PCT/CN2021/127996 WO2023045029A1 (en) | 2021-09-27 | 2021-11-01 | Method and system for ship route planning in pirate region, electronic device and storage medium |
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