CN110703762A - Hybrid path planning method for unmanned surface vehicle in complex environment - Google Patents
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Abstract
The invention discloses a method for planning a hybrid path of an unmanned surface vehicle under a complex environment, which comprises the following steps: establishing a two-dimensional grid map according to known environment information, and performing expansion processing on the map; carrying out global path planning by using a rapid A-x algorithm to generate a pre-planned path; and determining a feasible speed sampling space according to the motion parameter constraint of the unmanned surface vehicle by using a DWA algorithm, sampling a plurality of groups, simulating the track according to the sampling speed, and selecting the optimal track to execute according to the evaluation function of calculating each track. According to the invention, a feasible global path is obtained through a rapid A-x algorithm, and on the basis, DWA is used for local path planning, so that dynamic obstacles can be effectively avoided, the safe navigation of the unmanned surface vehicle in a complex environment is ensured, and the real-time requirement can be met.
Description
Technical Field
The invention belongs to the technical field of ship control engineering and automatic navigation, and particularly relates to a hybrid path planning method for a water surface unmanned ship under a complex environment.
Background
An Unmanned Surface Vessel (USV) is a water surface platform with autonomous navigation and automatic operation capabilities, and different tasks are completed by carrying different task loads. The method has the characteristics of low operation cost, strong environmental adaptability and the like, and can be used in the fields of surveying and mapping, exploration, detection, patrol and the like.
The path planning is to comprehensively consider various navigation constraint conditions and perform global or local planning, so that the USV can navigate safely and efficiently, and is one of key technologies for USV intellectualization and autonomy.
Disclosure of Invention
In order to solve the problems, the invention discloses a hybrid path planning method for an unmanned surface vehicle under a complex environment, which comprises the steps of carrying out global path planning through a quick A-x algorithm, dynamically adjusting search step length according to the distance between the current position and a terminal point to obtain a pre-planned path, then carrying out local path planning by using DWA on the basis, and integrating the global path planning method and the local path planning method to realize the fast path planning and the real-time avoidance of dynamic obstacles so as to ensure the safe navigation of the unmanned surface vehicle under the complex environment.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a planning method for a hybrid path of an unmanned surface vehicle in a complex environment comprises the following steps:
s1: according to the known environmental information and the size of the unmanned surface vehicle, a two-dimensional grid map is established, and the map is subjected to expansion processing;
s2: carrying out global path planning by using a rapid A-x algorithm, dynamically adjusting the search step length according to the distance between the current position and the terminal point, and generating a pre-planned path;
s3: determining a feasible speed sampling space according to the motion parameter constraint of the unmanned surface vehicle, performing a plurality of groups of sampling, simulating the track, calculating an evaluation function of each track, and selecting an optimal track to execute;
s4: judging whether the unmanned surface vehicle reaches the target position, if so, ending the algorithm; if not, the map is updated, and then S3 is executed.
Further, the step S1 specifically includes the following steps:
s1-1: establishing a two-dimensional grid map
Simplifying the working environment of the unmanned surface vehicle into a two-dimensional plane of m multiplied by n meters, wherein m and n are the length and width of the plane respectivelyDegree according to the set grid size sgDivide the plane into
A square grid of the same size, with "0" or "1" representing the grid attribute, "0" representing an unreachable region, and "1" representing an reachable region;
s1-2: inflation processing of map
The expansion treatment method comprises the following steps: and traversing all grids, wherein if the attribute of the grid and 8 adjacent grids is 1, the value is 1, and otherwise, the value is 0. According to the safe radius R of the unmanned surface vehiclesAnd grid size sgThe number of expansions N is determined,
the swelling treatment was repeated N times.
Further, the step S2 specifically includes the following steps:
s2-1: adding the starting point to the open list; the open list is a list of nodes to be searched;
s2-2: traversing the open list, searching the point with the minimum F value, setting the point as the current node and adding the closed list which is the list of searched nodes,
F(T)＝G(T)+H(T)
g (T) represents a distance from the start point a to the designated node T along the generated path, that is:
G(T)＝dist(A，T)
h (T) represents the estimated distance to move from the designated node T to the end point B, obtained by the Manhattan distance, i.e.:
H(T)＝|XT-XB|+|YT-YB|
wherein, XT、YTAs X and Y coordinates of node T, XB、YBThe X and Y coordinates of end point B.
S2-3: judging whether the terminal point B is reached, if so, finishing the algorithm; if not, determining a search step length L according to the distance from the current node T to the terminal point,
wherein M isLFor the maximum value of the search step length, [ alpha ], []Denotes rounding up, p is a scale factor, XT、YTAs X and Y coordinates of node T, XB、YBThe X and Y coordinates of end point B.
And checking whether nodes with the distance L in the 8 directions around the current node can be reached, if so, adding the nodes into the openlist, otherwise, ignoring the nodes, and then executing S2-2.
Further, the step S3 specifically includes the following steps:
s3-1 determining a velocity sample space
The speed space determined according to the acceleration and deceleration performance of the unmanned surface vehicle is as follows:
wherein u represents linear velocity, upWhich is indicative of the current line speed,
represents the minimum linear acceleration and the maximum linear acceleration, ω represents the angular velocity, ωpWhich is indicative of the current angular velocity of the vehicle,
the minimum and maximum angular accelerations are indicated and dt represents the time interval.
The speed space determined according to the speed limit of the unmanned surface vehicle is as follows:
V2＝{(u，r)|u∈[umin，umax]，ω∈[ωmin，ωmax]}
wherein u ismin、umaxDenotes the minimum and maximum linear velocities, ωmin、ωmaxRepresenting minimum angular velocity and maximumThe angular velocity.
The final velocity sampling space is:
V＝V1∩V2
s3-2: simulating the trajectory and calculating an evaluation function
Estimating the track according to the motion equation of the unmanned surface vehicle and the sampling speed obtained in S3-1,
in the formula (I), the compound is shown in the specification,
respectively, x-coordinate, y-coordinate, and heading, dt represents a time interval, u, ω represent linear and angular velocities, respectively, and k is 0,1,2 … ….
And evaluating each safe and feasible track by adopting an evaluation function mode, wherein the evaluation function is as follows:
G＝α·heading+β·distance+γ·velocity+δ·deviation
in the formula, G represents a total evaluation function value, heading is used for evaluating the deviation between the current course and the direction of a target point, distance is used for evaluating the minimum distance between each point on the current track and an obstacle, velocity is used for evaluating the size of the current speed, devision is used for evaluating the deviation degree of the current track and a path obtained by global planning, and alpha, beta, gamma and delta are weight coefficients of each index respectively.
Before calculating the total evaluation function of each track, each sub-item in the evaluation function needs to be normalized,
executing the track with the maximum evaluation function value, namely the optimal track;
s3-3: and judging whether the terminal is reached, if so, finishing the algorithm, and if not, executing S3-2.
The invention has the beneficial effects that:
according to the method for planning the hybrid path of the unmanned surface vehicle under the complex environment, the global path is planned through the rapid A-x algorithm to obtain a pre-planned path, and compared with the traditional A-x algorithm, the method for planning the hybrid path of the unmanned surface vehicle under the complex environment greatly improves the operation efficiency and realizes rapid path planning. And then, local path planning is carried out by using DWA on the basis, dynamic obstacles can be avoided in real time, and safe navigation of the unmanned surface vehicle in a complex environment is ensured.
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FIG. 1 is a flow chart of a hybrid path planning method for an unmanned surface vehicle in a complex environment;
FIG. 2 is a result of global path planning in an embodiment of the present invention;
FIG. 3 is a result of a portion of time during a local path planning process in an embodiment of the present invention;
fig. 4 is a final path obtained in an embodiment of the present invention.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention.
The invention provides a planning method for a hybrid path of an unmanned surface vehicle in a complex environment, which comprises the following steps:
s1: according to the known environmental information and the size of the unmanned surface vehicle, a two-dimensional grid map is established, and the map is subjected to expansion processing;
s1-1: establishing a two-dimensional grid map
Simplify the working environment of unmanned surface vehicleIs a two-dimensional plane of m × n meters, m and n are respectively the length and width of the plane, and the grid size s is set according togDivide the plane into
For a square grid of the same size, the grid attribute is represented by "0" or "1", with "0" representing an unreachable area and "1" representing an reachable area.
S1-2: inflation processing of map
In order to ensure the safe navigation of the unmanned surface vehicle, the distance between the unmanned surface vehicle and any obstacle is larger than the safe radius, therefore, the obstacle in the grid map is subjected to expansion processing by traversing all grids, if the attributes of the grid and 8 adjacent grids are all '1', the value is '1', otherwise, the value is '0'. According to the safe radius R of the unmanned surface vehicle3And grid size sgThe number of expansions N is determined,
repeating the expansion treatment for N times;
s2: global path planning is carried out by using a rapid A-x algorithm, the search step length is dynamically adjusted according to the distance between the current position and the terminal point, and a pre-planned path is generated
S2-1: adding the starting point to the open list; the open list is a list of nodes to be searched;
s2-2: traversing the open list, searching the point with the minimum F value, setting the point as the current node and adding the closed list which is the list of searched nodes,
F(T)＝G(T)+H(T)
g (T) represents a distance from the start point a to the designated node T along the generated path, that is:
G(T)＝dist(A，T)
h (T) represents the estimated distance to move from the designated node T to the end point B, obtained by the Manhattan distance, i.e.:
H(T)＝|XT-XB|+|YT-YB|
wherein, XT、YTAs X and Y coordinates of node T, XB、YBThe X and Y coordinates of end point B.
S2-3: judging whether the terminal point B is reached, if so, finishing the algorithm; if not, determining a search step length L according to the distance from the current node T to the terminal point,
wherein M isLFor the maximum value of the search step length, [ alpha ], []Denotes rounding up, p is a scale factor, XT、YTAs X and Y coordinates of node T, XB、YBThe X and Y coordinates of end point B.
The traditional A-algorithm value searches 8 adjacent nodes of the current node, the searching times are increased along with the increase of the map scale, and when the map is large, the operation time of the algorithm is greatly increased, so that the searching step length is introduced to accelerate the searching speed.
Checking whether nodes with the distance of L in 8 directions around the current node can be reached, if so, adding the nodes into openlist, otherwise, ignoring the nodes, and then executing S2-2;
s3: determining a feasible speed sampling space according to the motion parameter constraint of the unmanned surface vehicle, performing a plurality of groups of sampling, simulating the track, calculating an evaluation function of each track, and selecting an optimal track to execute;
s3-1 determining a velocity sample space
The speed sampling space is mainly determined by the performance of the unmanned surface vehicle.
The speed space determined according to the acceleration and deceleration performance of the unmanned surface vehicle is as follows:
wherein u represents linear velocity, upWhich is indicative of the current line speed,
represents the minimum linear acceleration and the maximum linear acceleration, ω represents the angular velocity, ωpWhich is indicative of the current angular velocity of the vehicle,
the minimum and maximum angular accelerations are indicated and dt represents the time interval.
The speed space determined according to the speed limit of the unmanned surface vehicle is as follows:
V2＝{(u，r)|u∈[umin，umax]，ω∈[ωmin，ωmax]}
wherein u ismin、umaxDenotes the minimum and maximum linear velocities, ωmin、ωmaxRepresenting the minimum angular velocity and the maximum angular velocity.
The final velocity sampling space is:
V＝V1∩V2
s3-2: simulating the trajectory and calculating an evaluation function
Estimating the track according to the motion equation of the unmanned surface vehicle and the sampling speed obtained in S3-1,
in the formula (I), the compound is shown in the specification,
respectively, x-coordinate, y-coordinate, and heading, dt represents a time interval, u, ω represent linear and angular velocities, respectively, and k is 0,1,2 … ….
After obtaining a plurality of simulated tracks, removing tracks which do not meet safety constraints, wherein the safety constraints are that the minimum value of the distance between each point on the tracks and the barrier is greater than a safety radius:
min(distance)＞Rs
then, each safe and feasible track is evaluated in an evaluation function mode, wherein the evaluation function is as follows:
G＝α·heading+β·distance+γ·velocity+δ·deviation
in the formula, G represents a total evaluation function value, heading is used for evaluating the deviation between the current course and the direction of a target point, distance is used for evaluating the minimum distance between each point on the current track and an obstacle, velocity is used for evaluating the size of the current speed, devision is used for evaluating the deviation degree of the current track and a path obtained by global planning, and alpha, beta, gamma and delta are weight coefficients of each index respectively. The physical meaning of the evaluation function formed by the four components is as follows: in local planning, the unmanned surface vehicle is enabled to avoid obstacles and move towards a target at a higher speed, and meanwhile, the deviation between a local planning result and a globally planned path is reduced as much as possible.
Before calculating the total evaluation function of each track, each sub-item in the evaluation function needs to be normalized,
executing the track with the maximum evaluation function value, namely the optimal track;
s3-3: and judging whether the terminal is reached, if so, finishing the algorithm, and if not, executing S3-2.
S4: judging whether the unmanned surface vehicle reaches the target position, if so, ending the algorithm; if not, the map is updated, and then S3 is executed.
According to the steps of the method, the environment of 2000 × 2000 m is considered in the embodiment, the simulation experiment is carried out by applying the water surface unmanned ship mixed path planning method, the global path planning result is shown in fig. 2, after two dynamic obstacles are introduced, the local path planning is carried out, the positions of the unmanned ships at part of the moment are shown in fig. 3, the path obtained by the global planning can be seen to collide with the obstacles, the local planning can successfully avoid collision, and the final complete path is shown in fig. 4.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features.
Claims (4)
1. A planning method for a hybrid path of an unmanned surface vehicle under a complex environment is characterized by comprising the following steps: the method comprises the following steps:
s1: establishing a two-dimensional grid map according to the known environment information, and performing expansion processing on the map;
s2: carrying out global path planning by using a rapid A-x algorithm, dynamically adjusting the search step length according to the distance between the current position and the terminal point, and generating a pre-planned path;
s3: determining a feasible speed sampling space according to the motion parameter constraint of the unmanned surface vehicle, performing a plurality of groups of sampling, simulating the track, calculating an evaluation function of each track, and selecting an optimal track to execute;
s4: judging whether the unmanned surface vehicle reaches the target position, if so, ending the algorithm; if not, the map is updated, and then S3 is executed.
2. The method for planning the hybrid path of the unmanned surface vehicle under the complex environment according to claim 1, wherein: the step S1 specifically includes the following steps:
s1-1: establishing a two-dimensional grid map
Simplifying the working environment of the unmanned surface vehicle into a two-dimensional plane of m multiplied by n meters, wherein m and n are the length and the width of the plane respectively, and the grid size s is set according togDivide the plane intoAll are the same sizeA square grid of (1), with "0" or "1" representing the grid attribute, "0" representing an unreachable region, and "1" representing an reachable region;
s1-2: inflation processing of map
The expansion processing method comprises the steps of traversing all grids, if the attributes of the grid and 8 adjacent grids are all '1', the value is '1', otherwise, the value is '0', and according to the safe radius R of the unmanned surface vehiclesAnd grid size sgThe number of expansions N is determined,
the swelling treatment was repeated N times.
3. The method for planning the hybrid path of the unmanned surface vehicle under the complex environment according to claim 1, wherein: the step S2 specifically includes the following steps:
s2-1: add the starting point to the open list: the open list is a list of nodes to be searched;
s2-2: traversing openlist, searching the point with the minimum F value, setting the point as the current node and adding close list, wherein close list is the list of searched nodes,
F(T)＝G(T)+H(T)
g (T) represents a distance from the start point a to the designated node T along the generated path, that is:
G(T)＝dist(A，T)
h (T) represents the estimated distance to move from the designated node T to the end point B, obtained by the Manhattan distance, i.e.:
H(T)＝|XT-XB|+|YT-YB|
wherein, XT、YTAs X and Y coordinates of node T, XB、YBThe X coordinate and the Y coordinate of the end point B;
s2-3: judging whether the terminal point B is reached, if so, finishing the algorithm; if not, determining a search step length L according to the distance from the current node T to the terminal point,
wherein M isLFor the maximum value of the search step length, [ alpha ], []Denotes rounding up, p is a scale factor, XT、YTAs X and Y coordinates of node T, XB、YBThe X coordinate and the Y coordinate of the end point B;
and checking whether nodes with the distance L in the 8 directions around the current node can be reached, if so, adding the nodes into the openlist, otherwise, ignoring the nodes, and then executing S2-2.
4. The method for planning the hybrid path of the unmanned surface vehicle under the complex environment according to claim 1, wherein: the step S3 specifically includes the following steps:
s3-1: determining a velocity sampling space
The speed space determined according to the acceleration and deceleration performance of the unmanned surface vehicle is as follows:
wherein u represents linear velocity, upWhich is indicative of the current line speed,
represents the minimum linear acceleration and the maximum linear acceleration, ω represents the angular velocity, ωpWhich is indicative of the current angular velocity of the vehicle,representing the minimum and maximum angular accelerations, dt representing the time interval;
the speed space determined according to the speed limit of the unmanned surface vehicle is as follows:
V2＝{(u，r)|u∈[umin，umax]，ω∈[ωmin，ωmax]}
wherein u ismin、umaxTo representMinimum and maximum linear velocity, ωmin、ωmaxRepresenting a minimum angular velocity and a maximum angular velocity;
the final velocity sampling space is:
V＝V1∩V2
s3-2: simulating the trajectory and calculating an evaluation function
Estimating the track according to the motion equation of the unmanned surface vehicle and the sampling speed obtained in S3-1,
wherein x, y, and,
Denote x-coordinate, y-coordinate and heading, respectively, dt denotes the time interval, u, ω denote linear and angular velocity, respectively, and k is 0,1, 2.
And evaluating each safe and feasible track by adopting an evaluation function mode, wherein the evaluation function is as follows:
G＝α·heading+β·distance+γ·velocity+δ·deviation
in the formula, G represents a total evaluation function value, heading is used for evaluating the deviation between the current course and the direction of a target point, distance is used for evaluating the minimum distance between each point on the current track and an obstacle, velocity is used for evaluating the size of the current speed, devision is used for evaluating the deviation degree of the current track and a path obtained by global planning, and alpha, beta, gamma and delta are weight coefficients of each index respectively;
before calculating the total evaluation function of each track, each sub-item in the evaluation function needs to be normalized,
executing the track with the maximum evaluation function value, namely the optimal track;
s3-3: and judging whether the terminal is reached, if so, finishing the algorithm, and if not, executing S3-2.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103744428A (en) * | 2014-01-17 | 2014-04-23 | 哈尔滨工程大学 | Unmanned surface vehicle path planning method based on neighborhood intelligent water drop algorithm |
| CN107037809A (en) * | 2016-11-02 | 2017-08-11 | 哈尔滨工程大学 | A kind of unmanned boat collision prevention method based on improvement ant group algorithm |
| CN108549378A (en) * | 2018-05-02 | 2018-09-18 | 长沙学院 | A kind of mixed path method and system for planning based on grating map |
| CN109916419A (en) * | 2019-03-12 | 2019-06-21 | 哈尔滨工程大学 | A kind of hybrid genetic algorithm unmanned boat real-time route planing method of object-oriented |
| CN110398250A (en) * | 2019-08-13 | 2019-11-01 | 哈尔滨工程大学 | A kind of unmanned boat global path planning method |
-
2019
- 2019-11-04 CN CN201911064032.7A patent/CN110703762B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103744428A (en) * | 2014-01-17 | 2014-04-23 | 哈尔滨工程大学 | Unmanned surface vehicle path planning method based on neighborhood intelligent water drop algorithm |
| CN107037809A (en) * | 2016-11-02 | 2017-08-11 | 哈尔滨工程大学 | A kind of unmanned boat collision prevention method based on improvement ant group algorithm |
| CN108549378A (en) * | 2018-05-02 | 2018-09-18 | 长沙学院 | A kind of mixed path method and system for planning based on grating map |
| CN109916419A (en) * | 2019-03-12 | 2019-06-21 | 哈尔滨工程大学 | A kind of hybrid genetic algorithm unmanned boat real-time route planing method of object-oriented |
| CN110398250A (en) * | 2019-08-13 | 2019-11-01 | 哈尔滨工程大学 | A kind of unmanned boat global path planning method |
Non-Patent Citations (1)
| Title |
|---|
| 尚明栋等: "《基于改进蚁群算法的水面无人艇智能避碰方法研究》", 《船舶工程》 * |
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