CN112859864A - Unmanned ship-oriented geometric path planning method - Google Patents
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Abstract
The invention discloses a geometric path planning method facing an unmanned ship, which adopts a backtracking queue to store feasible path planning points in a planning process, firstly, an obstacle in a ship navigation area is subjected to swelling treatment, an initial starting point is set as a current starting point and added into the backtracking queue and a planned path, then whether a connecting line between the initial starting point and a target point has the obstacle or not is judged, and if no obstacle exists, the connecting line is the planned path; if an obstacle exists, adding the current starting point into the planned path, deleting the current starting point from the backtracking queue, determining the first obstacle appearing on the connecting line of the current starting point and the target point, planning path points around the current obstacle at the periphery of the obstacle according to algorithm rules, selecting one path point as a new starting point to be added into the backtracking queue and the planned path according to certain rules, and repeating the steps to find new path points until no obstacle exists on the connecting line of the new path points and the obstacle, namely finishing path planning.
Description
Technical Field
The invention relates to the field of ship navigation control, in particular to a geometric path planning method for an unmanned ship.
Background
At present, economic development of all countries around the world is more and more closely connected, and ships have a great position in economic development as an important transportation tool. With the gradual decrease of land resources, countries in the world shift the eyes to oceans which account for about two thirds of the earth, the exploitation and use of ocean resources cannot be separated from the development of intelligent marine transportation, and the development of intelligent marine transportation determines the utilization rate of ocean resources. China has vast ocean resources as a big ocean country, which also puts requirements on the development of intelligent traffic on the sea, and the automation of ships is the most basic part and the most urgent part to be solved.
In the research of ship automation, path planning is one of the most important parts. The path planning of the unmanned ship needs to rapidly plan a safe and effective path under the condition of the known ship navigation environment to help the ship avoid a dangerous area to reach a target point. Path planning has two main parts: global path planning and local path planning. The global path planning is mainly used for finding a path with the shortest navigation distance and shortest time for a ship according to the detected obstacles in the known environment. The local path planning is to sense and avoid dynamic/static obstacles suddenly appearing during the navigation of the unmanned ship, and can autonomously return to the next waypoint after the local path planning is completed.
For the path planning of ships, domestic and foreign scholars propose a plurality of methods related to the path planning of ships, and the methods can be divided into two types of traditional unmanned ship path planning algorithms and bionic intelligent algorithms. The traditional unmanned ship path planning algorithm includes an artificial potential field method (APF), A, rapid expanding random tree (RRT), PRM and the like. The APF establishes a virtual repulsive field to avoid a static barrier by converting the navigation limiting area into a virtual barrier area, so that the unmanned ship is guided to drive to a target navigation point. However, when there is an equal repulsion and attraction force or when the repulsion at the target point is large, the ship will be stalled and fall into local optimum. The algorithm divides the area to be searched into square grids, checks adjacent grids from the starting point, then expands around until the target is found, and finally finds the path with the minimum moving cost from the feasible grids. However, the function of planning a path of the method depends on a grid map, and the size and the number of the intervals of the grids directly influence the calculation speed and the accuracy of the algorithm. The RRT algorithm guides the search to a blank area through random sampling points of a state space, so that a planned path from a starting point to a target point is found. The method can effectively solve the problem of path planning of high-dimensional space and complex constraint, but the path planned by the method is not optimal. The PRM is a method based on graph search, which converts a continuous space into a discrete space, and then searches a path on a road graph by utilizing search algorithms such as A x and the like so as to improve the search efficiency. This method can find a solution with relatively few random sampling points, which is enough to cover most of the feasible space and has a probability of finding a path of 1 for most problems, but the PRM algorithm is difficult to plan a path when the sampling points are too few or distributed unreasonably.
The bionic intelligent algorithm mainly comprises a Genetic Algorithm (GA), a particle swarm algorithm (PSO), an ant colony Algorithm (ACO) and the like. Such algorithms transform the target vessel path planning problem into a path optimization problem. For example, the ant colony algorithm uses the collision risk and the flight loss as the objective functions to solve the optimal path, but the method is very computationally intensive and requires a very high performance of the system. The genetic algorithm divides the fitness function into two parts which are respectively used for judging the length and the smoothness degree of the path, a roulette method is used as a selection method, and the optimal path is found through selection, intersection and mutation operators. But this method relies on a grid environment and when the grid is large, the computation time is long and tends to fall into local optima.
At present, researches on unmanned ship path planning algorithms are few, main algorithms are traditional algorithms such as A-x and APF, the A-x algorithms need to depend on grid environments, paths planned by the basic A-x algorithms are tightly attached to obstacles, risks are brought to actual ship navigation, the APF and other algorithms do not depend on the grid environments, however, the algorithms can fall into local optimization if repulsion and attraction are not properly set, the evolutionary algorithms are more intelligent algorithms, but easily fall into local optimization, the calculation time is longer, and corners of the planned paths are more. In summary, the path planning of the current unmanned ship mainly has the following disadvantages: dependent on the grid environment; the calculation time is long, and the method is not suitable for emergency; the corners of the planned path are more; partial algorithms tend to fall into local optima.
Disclosure of Invention
According to the problems in the prior art, the invention discloses a geometric path planning method for an unmanned ship, which considers the specific reality of ship navigation, firstly, carrying out bulking treatment on obstacles in a ship navigation area, setting an initial starting point as a current starting point, and adding the current starting point into a backtracking queue and a planned path; the following planning is then performed: judging whether a connecting line between the current starting point and the target point has an obstacle or not, if not, adding the target point to the tail of the planned path, and stopping planning; if an obstacle exists, deleting the current starting point from the backtracking queue, finding the first obstacle appearing on the connecting line of the current starting point and the target point, finding out two path points which bypass the current obstacle at the periphery of the obstacle according to an algorithm rule, adding the path point closest to the last node in the planned path as a new starting point into the backtracking queue and the planned path, if the other path point is not at the same side of the obstacle as the new starting point, adding the other path point into the backtracking queue, otherwise, deleting the other path point; then calculating the distance from the current starting point to the center of all the obstacles in the obstacle queue, if the current starting point is too close to a certain obstacle or even the current starting point is in a certain obstacle circle, deleting the current starting point from the planned path, finding the point closest to the last node in the planned path from the backtracking queue, taking the point as the current starting point and adding the point into the planned path; and repeating the steps to find a new path point until no obstacle exists on the connecting line of the new path point and the obstacle, namely finishing the path planning. Compared with other algorithms, the algorithm does not need to explore the whole environment when planning the path, so the time required by planning is shorter, and the algorithm can be suitable for global path planning and local path planning, and the adopted technical scheme comprises the following steps:
s1, acquiring the ship navigation environment to be planned and converting the ship navigation environment into an experimental planning environment;
s2: acquiring initial starting point and target point coordinates, and initializing a backtracking queue and a planning path;
s3: sorting all the obstacles in ascending order according to the distances between the obstacles and the current starting point;
s4: judging whether an obstacle exists on a connecting line between the current starting point and the target point;
s5: finding two new planning points M and N which bypass the current barrier;
s6: judging whether the two planning points M and N are positioned on the same side of the current barrier circle, if so, removing points far away from the circle center, setting the rest new planning points as new current starting points, and adding the points into a backtracking queue and a planning path; otherwise, two points are reserved, a point which is close to the last node of the planned path in the two points is set as a new current starting point and added into the planned path, and then the two points are added into a backtracking queue;
s7: calculating the distances from the current starting point to the centers of all the obstacles in the obstacle queue, deleting the current starting point from the planned path if the current starting point is too close to a certain obstacle or even the current starting point is in a certain obstacle circle, finding the point closest to the last node in the planned path from the backtracking queue as the current starting point, adding the current starting point into the planned path, and then turning to S3;
s8: and connecting the path points obtained in the planned path according to the sequence to form a final planned path.
Further, in S1: acquiring coordinate information such as the position of a barrier point, an initial starting point and a target point of a ship from an electronic chart, and converting the coordinate information into coordinates under a rectangular coordinate system by adopting an ink card support conversion method; scaling the coordinates of the barrier into a target environment by taking the converted initial starting point and the converted target point as the size of the target environment; and finally, solving the circumcircle of the obstacle by using the obstacle point information and performing proper expansion treatment, wherein the expanded circle is regarded as the obstacle to be avoided.
Further, in S2, an initial start point and a target point coordinate are first input, and the initial start point is added to a trace-back queue and a planned path and is used as a current start point, where the trace-back queue is used to store path coordinate points selected in the path planning process, and the planned path is used to store path points in the planned path.
Further, when it is determined in S4 whether there is an obstacle: if not, the target point is added to the end of the planned path, and the process goes to S8, if an obstacle exists, the current starting point is deleted from the backtracking queue, and the obstacle which is the first obstacle to appear on the line between the current starting point and the target point is set as the current obstacle.
Due to the adoption of the technical scheme, the geometric path planning method for the unmanned ship provided by the invention adopts the backtracking queue to store feasible planning path points in the planning process. Firstly, performing bulking treatment on obstacles in a ship navigation area, setting an initial starting point as a current starting point, and adding the current starting point into a backtracking queue and a planned path; the following planning is then performed: judging whether a connecting line between the current starting point and the target point has an obstacle or not, if not, adding the target point to the tail of the planned path, and stopping planning; if an obstacle exists, deleting the current starting point from the backtracking queue, determining the first obstacle appearing on the connecting line between the current starting point and the target point, finding out two path points bypassing the current obstacle at the periphery of the obstacle according to an algorithm rule, adding the path point closest to the last node in the planned path as a new starting point into the backtracking queue and the planned path, if the other path point and the new starting point are not on the same side of the obstacle, adding the other path point into the backtracking queue, otherwise deleting the other path point; and then calculating the distance from the current starting point to the center of all the obstacles in the obstacle queue, deleting the current starting point from the planned path if the current starting point is too close to a certain obstacle or even the current starting point is in a certain obstacle circle, finding the point closest to the last node in the planned path from the backtracking queue, taking the point as the current starting point, and adding the point into the planned path. And repeating the steps to find a new path point until no obstacle exists on the connecting line of the new path point and the obstacle, namely finishing the path planning.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a planning point on the same side of a barrier circle in the embodiment of the present invention;
FIG. 3 is a schematic diagram of the planning points on both sides of the obstacle circle in the embodiment of the present invention;
FIG. 4 is an initial chart of an embodiment of the present invention;
FIG. 5 is a path diagram planned by the algorithm of the present invention in an embodiment of the present invention;
FIG. 6 is a path diagram of APF planning in an embodiment of the present invention;
fig. 7 shows a route planned by a in an embodiment of the present invention;
FIG. 8 is a diagram of a RRT planned route in an embodiment of the present invention;
fig. 9 is a diagram of a RRT-planned route according to an embodiment of the present invention;
FIG. 10 is a path diagram of BUG planning in an embodiment of the present invention;
FIG. 11 is a schematic diagram of a GA planning route in an embodiment of the present invention.
Detailed Description
In order to make the technical solutions and advantages of the present invention clearer, the following describes the technical solutions in the embodiments of the present invention clearly and completely with reference to the drawings in the embodiments of the present invention:
as shown in fig. 1, a geometric path planning method for an unmanned ship specifically includes the following steps:
step 1: and acquiring the ship navigation environment to be planned, and converting the ship navigation environment into an experimental planning environment.
Firstly, acquiring position coordinate information of an obstacle point from an electronic chart, and converting the coordinates of the obstacle point, an initial starting point and a target point into coordinates under a rectangular coordinate system by using a mercator conversion method, wherein a coordinate conversion formula is shown as a formula (1); then scaling the transformed coordinates, namely scaling the coordinates of the obstacle into a target environment by taking an initial starting point and a target point as the size of the target environment; and finally, solving the circumcircle of the barrier by using the information of the barrier points, performing proper swelling treatment, and regarding the swelled circle as the barrier to be avoided. Equation (2) is a calculation equation of the center of a circle surrounded by a polygon, where (x, y) is the coordinates of the center of the circle surrounded by the polygon, (xi，yi) And n is the vertex number of the obstacle, wherein i is 1,2, 3.
Wherein a is the earth long axis, b is the earth short axis, the longitude of a certain point on the earth is theta which belongs to (-pi, + pi), the latitude is alpha which belongs to (-pi/2, + pi/2),
is the first eccentricity of the earth ellipsoid and (x, y) is the transformed rectangular coordinate.
Step 2: and acquiring initial starting point and target point coordinates, and initializing a backtracking queue and a planning path. And taking the initial starting point as the current starting point, adding a backtracking queue and a planning path, wherein the backtracking queue is used for storing path coordinate points selected in the path planning process, and the planning path is used for storing path points in the planning path.
And step 3: all obstacles are sorted in ascending order of their distance from the current starting point.
And 4, step 4: and (4) judging whether an obstacle exists on a connecting line between the current starting point and the target point, if not, adding the target point to the end of the planned path, and turning to the step 8. And if the obstacle exists, deleting the current starting point from the backtracking queue, and setting the obstacle which is firstly appeared on the connecting line between the current starting point and the target point as the current obstacle. And 5: two path points M, N are planned around the current obstacle to bypass the obstacle. Let the current starting point be S (x)1,y1) The target point is E (x)2,y2) The center and radius of the current obstacle circle are O (x)0,y0) And r. Tangent line l of circle O passing through point S1、l2The passing point E is taken as a tangent line l of the circle O3、l4To find l1、l3Point of intersection M (M)1,n1) And l2、l4Cross point N (m)2,n2) And M and N are possible planning path points. If the vertical coordinates of the current starting point and the target point are not equal, namely, the current starting point and the target point are not at the same height, y2≠y1Substituting the current starting point coordinate, the target point coordinate, the obstacle circle center coordinate and the radius length of the obstacle circle into a formula (3), calculating coefficients a, b and c required by solving the planning point, and solving the M and N coordinates according to the formula (4).
If the ordinate of the current starting point is equal to the ordinate of the target point, i.e. at the same height, y2＝y1The current starting point coordinate and eye are combinedSubstituting the coordinate of the punctuation point, the coordinate of the center of the obstacle circle and the radius length of the obstacle circle into a formula (5), calculating coefficients a, b and c required by solving the planning point, and solving the M and N coordinates according to the formula (6).
Step 6: judging whether the two planning points M and N are positioned at the same side of the current barrier circle, if so (as shown in figure 2), removing points far away from the circle center, setting the rest new planning points as new current starting points and adding the points into a backtracking queue and a planning path; if not (as shown in fig. 3), two points are reserved, a point which is close to the current starting point in the two points is set as a new current starting point and is added into the planning path, and then the two points are added into the backtracking queue.
And 7: and (3) calculating the distances from the current starting point to the centers of all the obstacles in the obstacle queue, if the current starting point is too close to a certain obstacle or even the current starting point is in a certain obstacle circle, deleting the current starting point from the backtracking queue and the planned path, obtaining the point closest to the last node in the planned path from the backtracking queue, taking the point as the current starting point and adding the point into the planned path, and then turning to the step 3.
And 8: and connecting the path points obtained in the planned path according to the sequence to form a final planned path.
The distance of the air route and the number of corners of the air route in the actual ship sailing process determine the quality of the air route, and the time for planning the air route also determines whether the air route can be used in time, so the algorithm of the patent is compared with two major algorithms, namely a traditional non-intelligent algorithm and a bionic intelligent algorithm respectively from three angles of path length, the number of corners and the time for the algorithm, of the planned air route, and the beneficial effect of the algorithm is described.
In the experiment, the traditional non-intelligent algorithm comprises an A-star algorithm, an RRT-star algorithm, a BUG algorithm and an APF, and the bionic intelligent algorithm mainly comprises a GA, a PSP and the like.
In the navigation process of a ship, the whole path is divided into a plurality of waypoints, the path planning needs to plan the path between one waypoint and the next waypoint, and the paths among the waypoints are connected to form the whole path. In the experimental simulation environment process, the actual environment size is 90 nautical miles × 60 nautical miles, so the experimental environment size is set to 900 pixels × 600 pixels. As shown in FIG. 4, the A, B two points are two waypoints, the line connecting the A, B two points is the initial route, and the length is 903 pixels. The circle represents an obstacle newly appearing in the navigation process of the ship, the dark color part of the circle represents the obstacle, the light color part represents a dangerous area, and the blank part is a navigable area.
The algorithm and the A-algorithm, APF, RRT, BUG and GA algorithm are respectively used for path planning on the same machine, the path planned by the algorithm is shown as figure 5, the path planned by the APF algorithm is shown as figure 6, the path planned by the A-algorithm is shown as figure 7, the path planned by the RRT algorithm is shown as figure 8, the path planned by the RRT algorithm is shown as figure 9, the path planned by the BUG algorithm is shown as figure 10, and the path planned by the GA algorithm is shown as figure 11. The path pair ratios for the various algorithms are shown in table 1.
TABLE 1 comparison of paths planned by various algorithms
As can be seen from table 1, the shortest path planned by the algorithm is about 915 pixels, the path planned by the RRT algorithm is about 941 pixels, the path planned by the a algorithm is about 943 pixels, the paths planned by the APF algorithm and the RRT algorithm are also below 1000 pixels, and the paths planned by the BUG algorithm and the GA algorithm exceed 1000 pixels, wherein the longest path of the BUG pixel is about 1012 pixels. The algorithm has a minimum of 2 corners in the corner aspect, and the RRT algorithm has 3 corners. The BUG algorithm requires that obstacle information in the environment is a polygon in planning, so that the obstacles are filled into a circumscribed square of a circle in an experiment, and when a path is planned, the algorithm needs to go around the obstacles for one circle to determine a point closest to a target point, so that the planned path has edges and corners. The number of iterations of the GA algorithm in the path planning process determines the quality of the planned path, when the number of iterations is too small, the planning may fail or the planned path is not optimal, when the number of iterations is too large, the planning may fall into local optimization or the time taken for planning is too long, and fig. 11 is a path planned by the GA algorithm after 100 iterations. The paths planned by the A-x algorithm and the APF algorithm are radian and are not suitable for the actual condition of the ship, and the paths planned by the RRT algorithm are not smooth and are not suitable for the actual route planning of the ship.
As can also be seen from Table 1, the time used by the algorithm is the shortest, and is only 0.002 s; next, the shortest is the RRT algorithm and the APF algorithm, and the use time is 0.023s and 0.225s respectively; the A-algorithm needs to step through the full path, so the time is longer to 9.47 s; the RRT algorithm depends on the number and the step length of random points, so the time is longer than 10.68 s; the BUG algorithm needs to walk around the obstacle for one circle to determine the point closest to the target point in the road searching process, so that the time of the BUG algorithm is 20.5 s; whereas the GA algorithm takes longer to find the optimal solution because of the need for iteration, fig. 11 shows that the time taken for 100 iterations is 55.4 s.
The method is not compared with a deep learning algorithm (a neural network algorithm and a deep reinforcement learning algorithm) in an experiment, and the experiment is not compared with the deep learning algorithm because a large amount of data is needed to train in the early stage of the deep learning algorithm, and the quality of the training data and the training times directly determine the quality of the later-stage track planning.
Most of the traditional path planning algorithms rely on a grid environment, the time for planning the path is long and is not suitable for emergency, and the corners of the planned path are more and are not suitable for path planning of ships.
The invention converts the path planning problem into the circular solution tangent problem by expanding the barrier, has simple calculation, does not need to traverse the whole map, has less corners of the planned path and short time for planning the path, and is suitable for emergency local path planning and global path planning.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (4)
1. A geometric path planning method for an unmanned ship is characterized by comprising the following steps:
s1, acquiring the ship navigation environment to be planned and converting the ship navigation environment into an experimental planning environment;
s2: acquiring initial starting point and target point coordinates, and initializing a backtracking queue and a planning path;
s3: sorting all the obstacles in ascending order according to the distances between the obstacles and the current starting point;
s4: judging whether an obstacle exists on a connecting line between the current starting point and the target point;
s5: finding two new planning points M and N which bypass the current barrier;
s6: judging whether the two planning points M and N are positioned on the same side of the current barrier circle, if so, removing points far away from the circle center, setting the rest new planning points as new current starting points, and adding the points into a backtracking queue and a planning path; otherwise, two points are reserved, a point which is close to the last node of the planned path in the two points is set as a new current starting point and added into the planned path, and then the two points are added into a backtracking queue;
s7: calculating the distances from the current starting point to the centers of all the obstacles in the obstacle queue, deleting the current starting point from the planned path if the current starting point is too close to a certain obstacle or even the current starting point is in a certain obstacle circle, finding the point closest to the last node in the planned path from the backtracking queue as the current starting point, adding the current starting point into the planned path, and then turning to S3;
s8: and connecting the path points obtained in the planned path according to the sequence to form a final planned path.
2. The unmanned-vessel-oriented geometric path planning method according to claim 1, further characterized by: in S1: acquiring coordinate information such as the position of a barrier point, an initial starting point and a target point of a ship from an electronic chart, and converting the coordinate information into coordinates under a rectangular coordinate system by adopting an ink card support conversion method; scaling the coordinates of the barrier into a target environment by taking the converted initial starting point and the converted target point as the size of the target environment; and finally, solving the circumcircle of the obstacle by using the obstacle point information and performing proper expansion treatment, wherein the expanded circle is regarded as the obstacle to be avoided.
3. The unmanned-vessel-oriented geometric path planning method according to claim 1, further characterized by: in S2, an initial starting point and a target point coordinate are first input, and the initial starting point is added to a backtracking queue and a planned path and is used as a current starting point, where the backtracking queue is used to store path coordinate points selected in a path planning process, and the planned path is used to store path points in the planned path.
4. The unmanned-vessel-oriented geometric path planning method according to claim 1, further characterized by:
when it is determined at S4 whether there is an obstacle: if not, the target point is added to the end of the planned path, and the process goes to S8, if an obstacle exists, the current starting point is deleted from the backtracking queue, and the obstacle which is the first obstacle to appear on the line between the current starting point and the target point is set as the current obstacle.
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| CN114115239A (en) * | 2021-11-03 | 2022-03-01 | 中国科学院重庆绿色智能技术研究院 | Robot path planning method, system, equipment and medium |
| CN114378834A (en) * | 2022-03-23 | 2022-04-22 | 季华实验室 | Mechanical arm obstacle avoidance path planning method and device, electronic equipment and storage medium |
| CN115328122A (en) * | 2022-08-02 | 2022-11-11 | 柳州城市职业学院 | Unmanned ship obstacle avoidance method and system based on multi-radar sensing |
| CN117130393A (en) * | 2023-10-26 | 2023-11-28 | 成都时代星光科技有限公司 | Unmanned aerial vehicle no-fly zone around-the-fly analysis method and system |
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