CN109784585A - A kind of mixing of unmanned plane unmanned boat lays dispatching method - Google Patents
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Abstract
A kind of mixing of unmanned plane unmanned boat lays dispatching method, comprising steps of S1, the optimal collision prevention path of unmanned boat using A* path planning algorithm, between the optimal collision prevention path of the unmanned boat for calculating separately out harbour to each target point and each target point；S2, use straight line path as harbour the unmanned plane path to each target point and the unmanned plane path between each target point；S3, the different speed according to possessed by unmanned plane unmanned boat, converts time loss for path distance, to convert isomery multiple traveling salesmen problem MTSP for unmanned plane unmanned boat mixed scheduling；S4 converts multiple independent traveling salesman problem TSP for isomery multiple traveling salesmen problem MTSP using K-means clustering algorithm；S5 is optimized using genetic algorithm, to obtain optimal unmanned plane unmanned boat mixed scheduling scheme.The present invention realizes the path planning and task schedule of unmanned plane unmanned boat heterogeneous system, has and stablizes, efficient advantage.
Description
Technical field
The invention belongs to unmanned plane unmanned boat cooperative scheduling scheme field, specifically a kind of unmanned plane unmanned boat is mixed
Conjunction lays dispatching method.
Background technique
Unmanned boat is a kind of with contexture by self, autonomous navigation ability, and can independently complete environment sensing, target acquisition etc.
The small-size water surface platform of task can undertake information acquisition, Snoopy Protocol, clearance, antisubmarine, precision strike, arrest, hydro_geography are surveyed
It examines, anti-terrorism, the functions such as relayed communications.Unmanned boat can be used a variety of different modules, carry different biographies according to the difference of mission
Sensor executes equipment, and execution task also shows that diversity.
Unmanned plane mainly includes airframe, flight control system, data link system, launch recycling system, power-supply system etc..Nothing
Man-machine design is dexterous, and space utilization rate is high, reusable, in military, civilian and research field extensive application.Mesh
Before, unmanned plane communication, electronic reconnaissance, monitoring, guidance, detection, in terms of used, have and huge answer
With value.
Research about unmanned plane or the respective cooperative scheduling of unmanned boat has very much, but different for unmanned plane-unmanned boat
Structure platform, both how to play respective advantage (unmanned plane has the advantage of faster movement speed and farther away combat radius,
Unmanned boat has the advantage of vdiverse in function, executable multiple-task), when unmanned plane, unmanned boat are all set out, and meanwhile it is simultaneous
The difference in water surface path Yu aerial path is cared for, how to distribute target to them, Lai Tigao task efficiency shortens the execution time,
Find it is a kind of efficiently feasible unmanned plane unmanned boat mixed scheduling method be a problem to be solved.
Summary of the invention
In order to solve the above technical problems, the present invention provides a kind of mixing of unmanned plane unmanned boat to lay dispatching party
Method.
In order to solve the above-mentioned technical problem, the present invention takes following technical scheme:
A kind of mixing of unmanned plane unmanned boat lays dispatching method, comprising the following steps:
S1, using A* path planning algorithm, the optimal collision prevention path of the unmanned boat for calculating separately out harbour to each target point,
And the optimal collision prevention path of unmanned boat between each target point；
S2, use straight line path as harbour the unmanned plane path to each target point and the nothing between each target point
Man-machine path；
S3, the different speed according to possessed by unmanned plane unmanned boat, converts time loss for path distance, thus will
Unmanned plane unmanned boat mixed scheduling is converted into isomery multiple traveling salesmen problem MTSP；
S4 converts multiple independent travelling salesmans for isomery multiple traveling salesmen problem MTSP using K-means clustering algorithm and asks
Inscribe TSP；
S5 is optimized using genetic algorithm, to obtain optimal unmanned plane unmanned boat mixed scheduling scheme.
Further, the step S1 is specifically included:
S101 obtains the map in related waters, and according to accuracy requirement and map size, to map carries out rasterizing, and marks
Remember the waters that can pass through out, impassabitity waters, target point and harbour point, and named port point is node 1, target point is from node 2
Start to calculate；
Harbour point or target point are put it into the list of entitled " OPEN " as starting point, and calculate value by S102
Function f (n)；
The smallest node n of cost function in " OPEN " list is moved in the list of entitled " CLOSED " by S103, if
N is the target point place node in addition to starting point, then obtains solution path；If n is not the target point institute in addition to starting point
In node, then next step S104 is gone to；
S104 finds the reachable all nodes of n node, removes the node in impassabitity waters and is already present on " OPEN "
Node in list or " CLOSED " list, then calculate their cost function and put it into " OPEN " list；
S105 jumps to S103, and recycles, until finding solution path or determining without solution；
S106 jumps to S102, by the calculated result of cost function f (n), obtain harbour to each target point unmanned boat
The optimal collision prevention path of unmanned boat between optimal collision prevention path and each target point.
The step S2 is specifically included:
S201 establishes plane right-angle coordinate, obtains the coordinate value (x at harbour1,y1) and each target point coordinate value
(x2,y2)、(x3,y3)、(x4,y4)……(xn,yn)；
S202 calculates harbour (x using Euclidean distance formula1,y1) arrive first aim point (x2,y2) unmanned plane road
Diameter distance:
S203, referring to S202, calculate separately out harbour to each target point unmanned plane path distance and each target point
Between unmanned plane path distance.
The step S3 is specifically included:
S301 turns the harbour of the obtained unmanned boat of step S1 to the path between each target point and each target point
Turn to corresponding unmanned boat path distance L 'ij；
S302 obtains the average speed V of unmanned plane according to the actual parameter of unmanned plane unmanned boat usedUAVAnd unmanned boat
Average speed VUSV；
S303, by the unmanned plane path distance at harbour obtained in step S2 to each target point and each target point it
Between unmanned plane path distance, according to formula Tij=Lij/VUAV, obtain corresponding unmanned plane time loss Tij；
S304, by path distance obtained in step S301, according to formula T 'ij=L 'ij/VUSV, obtain it is corresponding nobody
Ship time loss T 'ij；
S305, it is most short for target with time loss, unmanned plane unmanned boat is converted by unmanned plane unmanned boat mixed scheduling
Isomery multiple traveling salesmen problem.
The step S4 is specifically included:
S401, if sharing N number of target point, the sum of unmanned plane and unmanned boat is K, then there is restrictive condition N > K, any to select
Select K initial cluster center Z1(1), Z2(1) ..., ZK(1)；
Remaining sample is assigned to some in K cluster centre according to shortest distance principle by S402, it may be assumed that if
min{||X-Zi(k) | |, i=1,2 ..., K }=| | X-Zj(k) | |=Dj(k)
Then X ∈ Sj(k)；
X is any one in remaining sample, DjIt (k) is X to cluster centre Zj(k) Euclidean distance, SjIt (k) is cluster
To the set of the sample of Z (j)；
S403 calculates the new vector value Z of each cluster centrej(k+1), j=1,2 ..., K.
I.e. using mean vector as new cluster centre；
S404, if Zj(k+1)≠Zj(k), j=1,2 ..., K then return to S402, target point are reclassified, is laid equal stress on
Multiple iterative calculation；If Zj(k+1)=Zj(k), j=1,2 ..., K, algorithmic statement, calculating finish, finally by the more travelling salesmans of isomery
Problem MTSP is converted into K independent traveling salesman problem TSP.
The step S5 is specifically included:
S501 first assumes that the carrier of the independent traveling salesman problem TSP is for the 1st independent traveling salesman problem TSP
UAV generates initial population using the local search ability of greedy algorithm for genetic algorithm；
S502 is solved using genetic algorithm, and iterative process calculates fitness function individual in population first each time
Value, then successively executes selection, intersection, mutation operator；
S503 returns to S501, the carrier of independent traveling salesman problem TSP is changed to USV, thus an independent trip
The problem TSP of doing business respectively obtains two groups of solutions for UAV and USV；
S504 successively solves the 2nd, 3 ..., the solution of K independent traveling salesman problem TSP, equally, each independent travelling
Quotient's problem TSP respectively obtains two groups of solutions for UAV and USV；
S505 reuses genetic algorithm, finds the optimum allocation mode of independent traveling salesman problem TSP Yu UAV, USV.
The path planning and task schedule of unmanned plane unmanned boat heterogeneous system can be achieved in the present invention, has and stablizes, efficiently
Advantage.
Detailed description of the invention
Attached drawing 1 is the method for the present invention flow diagram.
Specific embodiment
To further understand the features of the present invention, technological means and specific purposes achieved, function, below with reference to
Present invention is further described in detail with specific embodiment for attached drawing.
As shown in Fig. 1, present invention discloses a kind of mixing of unmanned plane unmanned boat to lay dispatching method, including following step
It is rapid:
S1, using A* path planning algorithm, the optimal collision prevention path of the unmanned boat for calculating separately out harbour to each target point,
And the optimal collision prevention path of unmanned boat between each target point.
S2, use straight line path as harbour the unmanned plane path to each target point and the nothing between each target point
Man-machine path.
S3, the different speed according to possessed by unmanned plane unmanned boat, converts time loss for path distance, thus will
Unmanned plane unmanned boat mixed scheduling is converted into isomery multiple traveling salesmen problem MTSP.
S4 converts multiple independent travelling salesmans for isomery multiple traveling salesmen problem MTSP using K-means clustering algorithm and asks
Inscribe TSP.
S5 is optimized using genetic algorithm, to obtain optimal unmanned plane unmanned boat mixed scheduling scheme.
For step S1, specifically include:
S101 obtains the map in related waters, and according to actual accuracy requirement and map size, to map carries out grid
Change, and mark the waters that can pass through, impassabitity waters, target point and harbour point, and named port point is node 1, target point
It is calculated since node 2, such as node 3, node 4, node 5 are all target point.Such as it can be according to the actual size of unmanned boat
And its operational capabilities, selecting the length of square grid is 10 meters, can establish a three-dimensional array [x, y, t], store x respectively
Waters information t at axis values, y-axis numerical value and point (x, y), wherein t=-1,0,1,2, respectively represent impassabitity waters, mesh
Punctuate, unmanned boat present position and the waters that can pass through.
Harbour point or target point are put it into the list of entitled " OPEN " as starting point, and calculate value by S102
Function f (n), wherein f (n)=g (n)+h (n), h (n) are the position (nth point) where current unmanned boat and the road between starting point
Diameter distance, i.e., the distance of the starting point obtained by A* path planning algorithm to the path n point, h (n) can once be obtained by preceding
H (n-1) adds the step-length currently moved and obtains；G (n) is linear distance of the nth point to terminal.
The smallest node n of cost function in " OPEN " list is moved in the list of entitled " CLOSED " by S103, if
N is the target point place node in addition to starting point, then obtains solution path；If n is not the target point institute in addition to starting point
In node, then next step S104 is gone to.
S104 finds the reachable all nodes of n node, removes the node in impassabitity waters and is already present on " OPEN "
Node in list or " CLOSED " list, then calculate their cost function and put it into " OPEN " list.
S105 jumps to S103, and recycles, until finding solution path or determining without solution.I.e. when all roads
After diameter is attempted, not without the path of other target points, then it is determined as between origin-to-destination without there are directapaths.
S106 jumps to S102, by the calculated result of cost function f (n), obtain harbour to each target point unmanned boat
The optimal collision prevention path of unmanned boat between optimal collision prevention path and each target point.Assuming that harbour point adds the total N of all target points
It is a, it is marked respectively, such as A point and B point, since the shortest path of A point to B point is also shortest path of the B point to A point, because
This needs cycle calculations N altogether！/ 2 times.
For step S2, specifically include:
S201 establishes plane right-angle coordinate, remaining target point is node 2, node 3 ... node n, obtains harbour
Coordinate value (x1,y1) and each target point coordinate value (x2,y2)、(x3,y3)、(x4,y4)……(xn,yn), it can be with reality
Geographical coordinate as above-mentioned coordinate value, facilitate the conversion in step S3.
S202 calculates harbour (x using Euclidean distance formula1,y1) arrive first aim point (x2,y2) unmanned plane road
Diameter distance:
S203, referring to S202, calculate separately out harbour to each target point unmanned plane path distance and each target point
Between unmanned plane path distance.With step S106, N number of node is shared, but due to symmetric relation, needs cycle calculations altogether
N！/ 2 times.
The step S3 is specifically included:
S301 turns the harbour of the obtained unmanned boat of step S1 to the path between each target point and each target point
Turn to corresponding unmanned boat path distance L 'ij.Specific conversion thinking is as follows: in such as step S1, assuming initially that each grid
Length is 10 meters, and due to being to obtain approximate path in grating map, two grids, which directly connect, is divided into two kinds, (1)
Straight line connection, length is 10 meters at this time, and the connection of (2) oblique line, length is at this timeRice, thus corresponding unmanned boat path away from
From are as follows:A is straight line connection number all in path, and b is oblique line all in path
Connection number.
S302 obtains the average speed V of unmanned plane according to the actual parameter of unmanned plane unmanned boat usedUAVAnd unmanned boat
Average speed VUSV。
S303, by the unmanned plane path distance at harbour obtained in step S2 to each target point and each target point it
Between unmanned plane path distance, according to formula Tij=Lij/VUAV, obtain corresponding unmanned plane time loss Tij。
S304, by path distance obtained in step S301, according to formula T 'ij=L 'ij/VUSV, obtain it is corresponding nobody
Ship time loss T 'ij。
S305, it is most short for target with time loss, unmanned plane unmanned boat is converted by unmanned plane unmanned boat mixed scheduling
Isomery multiple traveling salesmen problem.By step S1 it is found that co-existing in N-1 target point, it is assumed that unmanned plane UAViThe target point of process is
A, a+1, a+2 ..., b, then unmanned plane UAViPath time consumption are as follows: Similarly, it is assumed that unmanned boat
USViThe target point of process is c, c+1, c+2 ..., d, then unmanned boat USViPath time consumption are as follows:Due to whole system task completion time depend on all unmanned planes without
Time-consuming longest individual, i.e. T in people's shiptotal=max { min [min (TUAVi),min(TUSVi)], then the MTSP problem can be with
Statement are as follows: while optimal the target point method of salary distribution and path planning are found, so that TtotalIt is minimum.
In addition, being specifically included for step S4:
S401, if sharing N number of target point, the sum of unmanned plane and unmanned boat is K, then there is restrictive condition N > K, any to select
Select K initial cluster center Z1(1), Z2(1) ..., ZK(1)。
Remaining sample is assigned to some in K cluster centre according to shortest distance principle by S402, it may be assumed that if
min{||X-Zi(k) | |, i=1,2 ..., K }=| | X-Zj(k) | |=Dj(k)
Then X ∈ Sj(k)；
S403 calculates the new vector value Z of each cluster centrej(k+1), j=1,2 ..., K.
I.e. using mean vector as new cluster centre.
S404, if Zj(k+1)≠Zj(k), j=1,2 ..., K then return to S402, target point are reclassified, is laid equal stress on
Multiple iterative calculation；If Zj(k+1)=Zj(k), j=1,2 ..., K, algorithmic statement, calculating finish, finally by the more travelling salesmans of isomery
Problem MTSP is converted into K independent traveling salesman problem TSP.I-th of TSP can be stated are as follows: optimal path is found, so that
TTSPi=TUAVj(it is assumed that i-th of TSP is by unmanned plane UAVjExecute) it is minimum.T in S305 at this timetotalChange are as follows: Ttotal
=max (TTSPi), i=1,2 ..., K.
For step S5, specifically include:
S501 first assumes that the carrier of the independent traveling salesman problem TSP is for the 1st independent traveling salesman problem TSP
UAV generates initial population using the local search ability of greedy algorithm for genetic algorithm.Greedy algorithm is to solve TSP problem
Algorithm has principle simple, and the fast advantage of solving speed can effectively improve the quality of the initialization population of genetic algorithm.
Its basic ideas is as follows: (1) randomly choosing a city first as starting point；(2) city having arrived at is removed, selection is most
Close city is as next city；(3) until all cities of traversal, from is turned again to, available one at this time
Path.
S502 is solved using genetic algorithm, and iterative process calculates fitness function individual in population first each time
Value, then successively executes selection, intersection, mutation operator.Roulette wheel selection alternatively operator is used in the present embodiment, it can also
To use elitist selection mechanism or the two to mix selection；In view of the discreteness of TSP problem, intersect in this example, mutation operator
Select binary coding mode.
S503 returns to S501, the carrier of independent traveling salesman problem TSP is changed to USV, thus an independent trip
The problem TSP of doing business respectively obtains two groups of solutions for UAV and USV.
S504 successively solves the 2nd, 3 ..., the solution of K independent traveling salesman problem TSP, equally, each independent travelling
Quotient's problem TSP respectively obtains two groups of solutions for UAV and USV.
S505 reuses genetic algorithm, finds the optimum allocation mode of independent traveling salesman problem TSP Yu UAV, USV,
K TSP problem and A UAV and B USV are corresponded, wherein A+B=K, so that TtotalIt is minimum.
For the optimization algorithm in S505, ant algorithm, particle swarm algorithm and simulated annealing etc. can also be used real
Existing, algorithm above is the common knowledge of those skilled in the art.
The above is only present pre-ferred embodiments, is not intended to limit the present invention in any form, although
The present invention is disclosed as above with preferred embodiment, and however, it is not intended to limit the invention, any person skilled in the art,
It does not depart within the scope of technical solution of the present invention, when the technology contents using the disclosure above make a little change or are modified to equivalent change
The equivalent embodiment of change, but without departing from the technical solutions of the present invention, technology refers to above embodiments according to the present invention
Made any simple modification, equivalent change and modification, belong in the range of technical solution of the present invention.
Claims (6)
1. a kind of mixing of unmanned plane unmanned boat lays dispatching method, comprising the following steps:
S1, using A* path planning algorithm, the optimal collision prevention path of the unmanned boat for calculating separately out harbour to each target point, and it is each
The optimal collision prevention path of unmanned boat between a target point；
S2, use straight line path as harbour the unmanned plane path to each target point and the unmanned plane between each target point
Path；
S3, the different speed according to possessed by unmanned plane unmanned boat, converts time loss for path distance, thus by nobody
Machine unmanned boat mixed scheduling is converted into isomery multiple traveling salesmen problem MTSP；
S4 converts multiple independent traveling salesman problems for isomery multiple traveling salesmen problem MTSP using K-means clustering algorithm
TSP；
S5 is optimized using genetic algorithm, to obtain optimal unmanned plane unmanned boat mixed scheduling scheme.
2. the mixing of unmanned plane unmanned boat according to claim 1 lays dispatching method, which is characterized in that the step S1
It specifically includes:
S101 obtains the map in related waters, and according to accuracy requirement and map size, to map carries out rasterizing, and marks
Can pass through waters, impassabitity waters, target point and harbour point, and named port point is node 1, and target point is then opened from node 2
Begin to calculate；
Harbour point or target point are put it into the list of entitled " OPEN " as starting point, and calculate cost function by S102
f(n)；
The smallest node n of cost function in " OPEN " list is moved in the list of entitled " CLOSED ", if n is by S103
Node where target point in addition to starting point, then obtain solution path；If n is not the target point place section in addition to starting point
Point then goes to next step S104；
S104 finds the reachable all nodes of n node, removes the node in impassabitity waters and is already present on " OPEN " list
Or the node in " CLOSED " list, then calculate their cost function and put it into " OPEN " list；
S105 jumps to S103, and recycles, until finding solution path or determining without solution；
S106 jumps to S102, and by the calculated result of cost function f (n), it is optimal to the unmanned boat of each target point to obtain harbour
The optimal collision prevention path of unmanned boat between collision prevention path and each target point.
3. the mixing of unmanned plane unmanned boat according to claim 2 lays dispatching method, which is characterized in that the step S2
It specifically includes:
S201 establishes plane right-angle coordinate, obtains the coordinate value (x at harbour1,y1) and each target point coordinate value (x2,
y2)、(x3,y3)、(x4,y4)……(xn,yn)；
S202 calculates harbour (x using Euclidean distance formula1,y1) arrive first aim point (x2,y2) unmanned plane path away from
From:
S203 calculates separately out harbour between the unmanned plane path distance and each target point of each target point referring to S202
Unmanned plane path distance.
4. the mixing of unmanned plane unmanned boat according to claim 3 lays dispatching method, which is characterized in that the step S3
It specifically includes:
S301 is converted by the harbour of the obtained unmanned boat of step S1 to the path between each target point and each target point
Corresponding unmanned boat path distance L 'ij；
S302 obtains the average speed V of unmanned plane according to the actual parameter of unmanned plane unmanned boat usedUAVWith putting down for unmanned boat
Equal speed VUSV；
S303, by the unmanned plane path distance L at harbour obtained in step S2 to each target pointijAnd between each target point
Unmanned plane path distance, according to formula Tij=Lij/VUAV, obtain corresponding unmanned plane time loss Tij；
S304, by path distance obtained in step S301, according to formula T 'ij=L 'ij/VUSV, obtain the corresponding unmanned boat time
Consume T 'ij；
S305, it is most short for target with time loss, convert unmanned plane unmanned boat mixed scheduling to the isomery of unmanned plane unmanned boat
Multiple traveling salesmen problem.
5. the mixing of unmanned plane unmanned boat according to claim 4 lays dispatching method, which is characterized in that the step S4
It specifically includes:
S401, if sharing N number of target point, the sum of unmanned plane and unmanned boat is K, then there is restrictive condition N > K, arbitrarily selection K
A initial cluster center Z1(1), Z2(1) ..., ZK(1)；
Remaining sample is assigned to some in K cluster centre according to shortest distance principle by S402, it may be assumed that if
min{||X-Zi(k) | |, i=1,2 ..., K }=| | X-Zj(k) | |=Dj(k)
Then X ∈ Sj(k)；
X is any one in remaining sample, DjIt (k) is X to cluster centre Zj(k) Euclidean distance, Sj(k) Z is arrived for cluster
(j) set of sample；
S403 calculates the new vector value Z of each cluster centrej(k+1), j=1,2 ..., K.
I.e. using mean vector as new cluster centre；
S404, if Zj(k+1)≠Zj(k), j=1,2 ..., K then return to S402, target point are reclassified, and repeat to change
In generation, calculates；If Zj(k+1)=Zj(k), j=1,2 ..., K, algorithmic statement, calculating finish, finally by isomery multiple traveling salesmen problem
MTSP is converted into K independent traveling salesman problem TSP.
6. the mixing of unmanned plane unmanned boat according to claim 5 lays dispatching method, which is characterized in that the step S5
It specifically includes:
S501 first assumes that the carrier of the independent traveling salesman problem TSP is UAV for the 1st independent traveling salesman problem TSP,
Using the local search ability of greedy algorithm, initial population is generated for genetic algorithm；
S502 is solved using genetic algorithm, and iterative process calculates fitness function value individual in population first each time, so
Selection, intersection, mutation operator are successively executed afterwards；
S503 returns to S501, the carrier of independent traveling salesman problem TSP is changed to USV, thus an independent travelling salesman
Problem TSP respectively obtains two groups of solutions for UAV and USV；
S504 successively solves the 2nd, 3 ..., and the solution of K independent traveling salesman problem TSP, equally, each independent travelling salesman asks
Topic TSP respectively obtains two groups of solutions for UAV and USV；
S505 reuses genetic algorithm, finds the optimum allocation mode of independent traveling salesman problem TSP Yu UAV, USV.
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| CN110443454A (en) * | 2019-07-04 | 2019-11-12 | 珠海云洲智能科技有限公司 | The arrival scheduling method and device that unmanned boat is formed into columns |
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