CN112508369B - Multi-unmanned aerial vehicle task allocation method based on improved ant colony algorithm - Google Patents
Multi-unmanned aerial vehicle task allocation method based on improved ant colony algorithm Download PDFInfo
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Abstract
The invention discloses a multi-unmanned aerial vehicle task allocation method based on an improved ant colony algorithm, which is characterized in that the ant colony algorithm is improved, and the direction of movement transfer of ants of unmanned aerial vehicles in the movement process is determined by the concentration of pheromones on the flight paths of all unmanned aerial vehicles; in the task allocation searching process, the ants representing the unmanned aerial vehicle intelligently select a path to be taken next; the distance of the path calculated by adopting the ant colony algorithm is compared with the linear distance between the two points, a shorter distance is selected as the optimal path for the unmanned aerial vehicle to find the target, and meanwhile, the gradient descent method is adopted for optimization so as to shorten the flight distance of the unmanned aerial vehicle, so that the method is suitable for task allocation of multiple unmanned aerial vehicles, and the unmanned aerial vehicle can rapidly complete tasks and reduce the flight distance. By adopting the technical scheme of the invention, the time for completing the task can be reduced, and the flight distance of the unmanned aerial vehicle during searching the task can be shortened.
Description
Technical Field
The invention belongs to the technical field of multi-unmanned aerial vehicle task allocation, and relates to a multi-unmanned aerial vehicle task allocation method based on an improved ant colony algorithm.
Background
In recent years, multi-machine cooperative control has become a research hot spot in the field of unmanned aerial vehicles, and task allocation is the guarantee and foundation of multi-unmanned aerial vehicle cooperative control. The task allocation is to reasonably allocate the tasks to be completed to the panelists in the system according to the established targets, so as to achieve the purposes of executing the tasks efficiently and optimizing the unmanned aerial vehicle system. The multi-unmanned aerial vehicle collaborative task allocation is a process of decomposing a combat task into a plurality of subtasks and allocating the subtasks to each unmanned aerial vehicle in a multi-unmanned aerial vehicle system for completion respectively according to the constraint of a set of specific conditions and aiming at realizing the optimal or suboptimal of a certain criterion function. The goal is to assign specific goals and action tasks to each machine offline or in real time, with the goal of overall task efficiency optimization or suboptimal, taking into account various constraints such as task execution order, time, physical conditions of the unmanned aerial vehicle itself, etc. In general, multi-drone mission allocation can be divided into two major parts: task allocation at the upper layer and path planning at the lower layer. The task allocation considers various constraint conditions, aims at effectively achieving the overall task, allocates specific targets and action tasks to each machine, and carries out specific combat path planning according to the allocated tasks. And the function of path planning is to design or generate paths between a series of locations while meeting own or external constraints such as maximum linear velocity, maximum rotational velocity, safety of operation, time and environmental variables. Meanwhile, the multi-unmanned aerial vehicle cooperative task planning system is an important component of the whole multi-unmanned aerial vehicle cooperative control system.
In the task allocation problem of multiple unmanned aerial vehicles, the common methods mainly comprise two kinds of centralized task allocation and distributed task allocation. The centralized task allocation is that communication among unmanned aerial vehicles in formation, signal transmission and control are all carried out by a single control center, a common model is MTSP, VRP, MILP, DNFO, CMTAP, wherein the common model can be divided into an optimization method and a heuristic algorithm, the optimization method comprises an exhaustion method, an integer programming method, a constraint programming method and a graph theory method, and the heuristic algorithm is used for obtaining a local optimal solution or a satisfactory solution in an acceptable time range and comprises a list programming algorithm and an intelligent optimization algorithm; the distributed task allocation method is different from the centralized task allocation method in a signal transmission mode, the former unmanned aerial vehicle can also communicate in a formation, has better flexibility, has higher requirements on unmanned aerial vehicles compared with the centralized task allocation method, and needs the unmanned aerial vehicle to have the capabilities of independent calculation, analysis, decision making and the like, wherein the distributed task allocation method comprises a contract network method and an auction method, the contract network is the distributed task allocation method with the widest application range, and the core of the contract network is that the contract network is the communication mode negotiation processing for solving each problem for preventing conflict, has two roles of a publisher and a bidder, and consists of 4 interaction stages of bidding-bid-winning-confirmation; the auction method is a market mechanism for realizing resource allocation, namely, a buyer decides the price of a specific article by adopting a bid mode on the premise of knowing the auction rule clearly, namely, the article to be auctioned is traded to the highest or lowest price person by adopting a public price competing mode, and the auction method is a negotiation protocol, so that the rule is clear and convenient to operate, and more students are paid attention to in recent years. The auction method uses definite rules to guide the buyer and the seller to interact, has very strong operability, can reasonably allocate resources in a short time, and obtains the optimal solution or the better solution of the problem.
The ant colony algorithm is used as a heuristic global optimization algorithm in the evolution algorithm, and has great advantages in unmanned aerial vehicle task allocation. The basic idea of the ant colony algorithm is as follows: the walking path of the ants is used for representing the feasible solution of the problem to be optimized, and all paths of the whole ant group form a solution space of the problem to be optimized. The ants with shorter paths release more pheromones, the concentration of the pheromones accumulated on the shorter paths gradually increases along with the advancement of time, and the number of ants selecting the paths is increased. Finally, the whole ant is concentrated on the optimal path under the action of positive feedback, and the optimal solution of the problem to be optimized is correspondingly obtained. According to the existing data, the problems that the task completion time is long and the flight distance of unmanned aerial vehicles is long during searching exist in the conventional ant colony algorithm applied to the task distribution of multiple unmanned aerial vehicles in most cases, so that improvement is needed in the aspect.
Disclosure of Invention
In order to overcome the defects in the prior art and solve the problems that the task completion time of a plurality of unmanned aerial vehicles is long and the flight distance of the unmanned aerial vehicle is long during searching, the invention provides a method for improving an ant colony algorithm, which is suitable for the task allocation problem of the unmanned aerial vehicles, improves on the basis of the ant colony algorithm, reduces the task completion time and shortens the flight distance of the unmanned aerial vehicle during searching the task.
The technical scheme of the invention is as follows:
a multi-unmanned aerial vehicle task distribution method based on an improved ant colony algorithm improves the ant colony algorithm, and the distance between a path calculated by the ant colony algorithm and the linear distance between two points are calculated directly by comparing, a shorter distance is selected as an optimal path for an unmanned aerial vehicle to find a target, and a gradient descent method is adopted to shorten the flight distance of the unmanned aerial vehicle. The method can enable the unmanned aerial vehicle to rapidly complete tasks and reduce the flight distance by comparing the calculation time of the algorithm before and after improvement, the flight distance of the unmanned aerial vehicle and the flight time of the unmanned aerial vehicle, and is suitable for task allocation of multiple unmanned aerial vehicles. In the invention, the direction of the movement transfer of ants representing unmanned aerial vehicles in the movement process is determined by the concentration of pheromones on the flight paths of each unmanned aerial vehicle. In the algorithm searching process, the ant representing the unmanned aerial vehicle intelligently selects a path to be taken next. The task allocation method comprises the following steps:
1) Acquiring the size of a task area, the number of unmanned aerial vehicles (frames) participating in task allocation of multiple unmanned aerial vehicles, the number of targets to be searched, the initial positions of the unmanned aerial vehicles and the targets and the speed of the unmanned aerial vehicles;
each unmanned aerial vehicle is expressed as an ant in the improved ant colony algorithm; the number of ant colonies (i.e. a plurality of unmanned aerial vehicles) participating in the improved ant colony algorithm and the number of iterations of the algorithm are set.
2) Creating an unmanned aerial vehicle pheromone matrix and initializing;
the method comprises the steps of using pheromones to represent whether unmanned aerial vehicle paths fly through a certain node, setting a matrix taking the number of unmanned aerial vehicles as the number of rows and the number of target nodes as the number of columns as an pheromone matrix, wherein each element in the matrix represents an pheromone. Where 0 indicates that the drone path did not fly through the node and 1 indicates that the drone path flown through the node. The two pheromone matrixes are respectively used for storing the optimal task distribution result found in each iteration of the improved ant colony algorithm and the final distribution result after the task is finished, and the paths of the unmanned aerial vehicle for finding all the targets can be obtained according to the pheromone matrixes. Initializing all pheromone elements in the pheromone matrix to be 1.
3) And setting the total amount of unmanned aerial vehicle tasks to be completed.
4) The unmanned aerial vehicle is allocated according to the pheromone matrix, random allocation is firstly carried out, and when the pheromone is smaller than 1, allocation is carried out according to the pheromone matrix; otherwise, carrying out random distribution. When a drone approaches its maximum operating capacity, then the assignment of tasks to the drone is stopped.
5) The method for updating the pheromone matrix is shown in the formulas 1 and 2, so that the optimal path of the unmanned aerial vehicle is found out, and the flight distances corresponding to all unmanned aerial vehicles are recorded.
Path (i, j) represents a connection between the i-th node and the j-th node on the path. At time t+n, the pheromone is obtained on the update path (i, j) according to the formulas 1 and 2:
τ ij (t+n)＝(1-ρ)×τ ij (t)+Δτ ij (t) (1)
In formula 1, t is the current time; n=1, 2, … … n; τ ij Pheromones that are paths (i, j); the constant ρ epsilon (0, 1) represents the pheromone volatilization factor, namely the loss degree of the information quantity on the path, the magnitude of ρ is related to the global searching capacity and the convergence speed of the algorithm, and 1- ρ represents the pheromone residual factor.
Δτ in formula 2 ij Pheromone delta representing path (i, j); m represents the total number of unmanned aerial vehicles,
and (3) representing the pheromone increment of the path (i, j) after the searching is finished, wherein k is the kth unmanned aerial vehicle, and t is the current moment. At the initial time Deltaτ ij (0)＝0，t n Indicating the final moment +.>
And the pheromone which represents the path (i, j) of the kth unmanned aerial vehicle after the current traversal is finished. At time t, ants represented by the kth unmanned aerial vehicle are transferred from node i to node j according to the transfer probability. State transition probability of ant k from node i to node j +.>
Expressed as formula 3:
wherein, allowed k ＝{C-tabu k All nodes which can be selected in the next step of ants represented by a kth unmanned aerial vehicle are represented by the (k) th unmanned aerial vehicle, and C is all node sets; tabu k The method is used for recording all nodes which the ant represented by the kth unmanned aerial vehicle currently walks through, alpha is an information heuristic factor, represents the relative importance degree of the track in an algorithm, reflects the influence degree of the information quantity on the path on the selection path of the ant represented by the unmanned aerial vehicle, and the larger the value is, the stronger the collaboration among the ants represented by the unmanned aerial vehicle is; beta is called the desirability heuristic and represents the relative importance of visibility in the algorithm. η (eta) ij Is a heuristic function that represents in an algorithm the desired degree of transition from node i to node j, which is generally desirable
The ants represented by each drone proceed in search according to equation 3 while the algorithm is running.
Updating the pheromone matrix according to the formulas 1 and 2, obtaining paths through which the unmanned aerial vehicle finds all targets according to the pheromone matrix, recording the corresponding flight distances of all unmanned aerial vehicles, and finding out the optimal paths of the unmanned aerial vehicle;
6) According to the gradient descent method, the current unmanned aerial vehicle position is firstly determined, and then the path obtained in the step 5) is optimized through the formula 4, so that the point with the shortest distance relative to the position is found, and the unmanned aerial vehicle advances along the direction of the point to reach a new position.
D in min (x, y) represents the path length after processing, D Ant Represents the path calculated by the ant colony algorithm, x represents the abscissa of the point, y represents the ordinate of the point,
representing the deviation of x in the path calculated by the ant colony algorithm,/>
The y bias derivative in the path calculated by the ant colony algorithm is shown.
7) The length of the straight line distance between the two points is calculated. As shown in equation 5, the calculated length is compared with the optimized path length in step 6), and a smaller value is taken as the distance between the unmanned aerial vehicle and the target position, i.e. the unmanned aerial vehicle finds the optimal path of all targets.
Where D represents the optimal path length, D Ant Represents the path length obtained by ant algorithm, D th Representing the straight line distance between the two points.
8) Judging whether the task is completed, if so, terminating the operation, otherwise, returning to the step 4), updating the pheromone matrix according to the optimal path of each step, finding out and storing the optimal task allocation result in each iteration.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a multi-unmanned aerial vehicle task allocation method for improving an ant colony algorithm, which is used for comparing the path distance calculated by the ant colony algorithm with the straight line distance between two points calculated directly, selecting a smaller value as an optimal path and being suitable for the problem of multi-unmanned aerial vehicle task allocation. The invention has the following technical advantages:
the calculation speed is high, and the time for completing task allocation of multiple unmanned aerial vehicles can be reduced to a large extent;
secondly, the flight distance of the unmanned aerial vehicle during searching tasks is shortened, and compared with an ant colony algorithm, the unmanned aerial vehicle flight distance searching method can reduce the flight distance of the unmanned aerial vehicle during completing tasks
Drawings
Fig. 1 is a flow chart of a multi-unmanned aerial vehicle task allocation method for improving an ant colony algorithm.
FIG. 2 is a final roadmap in an example of an embodiment of the invention;
in the figure, delta, ++line types respectively represent paths taken by three unmanned aerial vehicles to find task points.
Detailed Description
The invention is further described by way of examples in the following with reference to the accompanying drawings, but in no way limit the scope of the invention.
The invention provides a method for improving an ant colony algorithm, which is used for task allocation of multiple unmanned aerial vehicles, each unmanned aerial vehicle is expressed as an ant in the improved ant colony algorithm, the path distance calculated by the ant colony algorithm is compared with the straight line distance between two points, a shorter distance is selected as an optimal path for searching targets of the unmanned aerial vehicles, and the method is suitable for the task allocation problem of the multiple unmanned aerial vehicles.
Fig. 1 is a flow chart of a multi-unmanned aerial vehicle task allocation method for improving an ant colony algorithm. In specific implementation, the method specifically comprises the following implementation steps:
1) The method comprises the steps of obtaining the size of a task completion area, the number of unmanned aerial vehicles participating in task allocation of multiple unmanned aerial vehicles, the number of targets to be searched, the initial positions of the unmanned aerial vehicles and the targets, the speed of the unmanned aerial vehicles, the number of unmanned aerial vehicle groups participating in an algorithm and the iteration times of the algorithm.
2) The method comprises the steps of using pheromones to represent whether unmanned aerial vehicles fly through paths, setting a matrix taking the number of unmanned aerial vehicles as the number of rows and the number of target points as the number of columns as an pheromone matrix, wherein the pheromone matrix is 3*5 in the invention. Each element in the matrix represents a pheromone, where 0 represents that the drone path did not fly past the point and 1 represents that the drone path flown past the point. The two pheromone matrixes are respectively used for storing the optimal task distribution result found in each iteration of the improved ant colony algorithm and the final distribution result after the task is finished, and the paths of the unmanned aerial vehicle for finding all the targets can be obtained according to the pheromone matrixes. All pheromones are initialized to 1.
In the unmanned aerial vehicle task allocation method for improving the ant colony algorithm, which is provided by the invention, ants (k=1, 2, …, m) expressed by unmanned aerial vehicle k move in the motion process, and the motion transfer direction is determined by the pheromone concentration on each path. In the algorithm searching process, ants represented by the unmanned aerial vehicle can intelligently select a path to be taken next.
Let m be the total number of ants that unmanned aerial vehicle represents, unmanned aerial vehicle carries out path planning with the coordinate point as benchmark, uses d ij (i, j=0, 1, … …, n-1) represents the distance between the coordinate point i and the coordinate point j, τ ij And (t) represents the pheromone concentration at the time t, on the line connecting the coordinate point i and the coordinate point j. At the initial time, ants represented by m unmanned aerial vehicles are randomly placed, and the initial pheromone concentration on each path is set to be 1. At time t, ant k transitions from node i to node j with state transition probability
Expressed as formula 3:
wherein, allowed k ＝{C-tabu k All nodes which can be selected in the next step of ants represented by a kth unmanned aerial vehicle are represented by the (k) th unmanned aerial vehicle, and C is all node sets; tabu k The method is used for recording all nodes which the ant represented by the kth unmanned aerial vehicle currently walks through, alpha is an information heuristic factor, represents the relative importance degree of the track in an algorithm, reflects the influence degree of the information quantity on the path on the selection path of the ant represented by the unmanned aerial vehicle, and the larger the value is, the stronger the collaboration among the ants represented by the unmanned aerial vehicle is; beta is called the desirability heuristic and represents the relative importance of visibility in the algorithm. η (eta) ij Is a heuristic function that represents in an algorithm the desired degree of transition from node i to node j, which is generally desirable
The ants represented by each drone proceed in search according to equation 3 while the algorithm is running.
In the moving process of ants represented by unmanned aerial vehicles, heuristic information is submerged in order to avoid excessive information remained on the road, and after the traversing of ants represented by each unmanned aerial vehicle is completed, the residual information is updated. Thus, at time t+n, the pheromone on the path (i, j) is updated by adjustment according to equations 1 and 2:
τ ij (t+n)＝(1-ρ)×τ ij (t)+Δτ ij (t) (1)
In formula 1, t is the current time; n=1, 2, … … n; τ ij Pheromones that are paths (i, j); the pheromone is data in the pheromone matrix, the pheromone is 0 to indicate no passing path, and the pheromone is 1 to indicate passing path; the constant rho epsilon (0, 1) represents the pheromone volatilization factor, the loss degree of the information quantity on the path is represented, the magnitude of rho is related to the global searching capacity and convergence speed of the algorithm, and 1-rho represents the pheromone residual factor;
in formula 2, Δτ ij Pheromone delta representing path (i, j); m represents the total number of unmanned aerial vehicles,
and (3) representing the pheromone increment of the path (i, j) after the search is finished, wherein k is the kth unmanned aerial vehicle. At the initial time Deltaτ ij (0)＝0，t n Indicating the final moment +.>
And the pheromone which represents the path (i, j) of the kth unmanned aerial vehicle after the current traversal is finished. At time t, the ant represented by the kth unmanned plane is transferred from node i to node j according to the transfer probability calculated in equation 4.
3) The total amount of tasks that can be completed is set.
4) The unmanned aerial vehicle is allocated according to the pheromone matrix, random allocation is firstly carried out, and when the pheromone is smaller than 1, allocation is carried out according to the pheromone matrix; otherwise, carrying out random distribution. When a drone approaches his maximum working capacity, then the assignment of tasks to him is stopped.
5) Updating the pheromone matrix, finding out the optimal ant path and recording the corresponding flight distances of all unmanned aerial vehicles.
6) At time t+n, the pheromone on the path (i, j) is updated according to equations 1 and 2.
7) According to the gradient descent method, the current unmanned aerial vehicle position is firstly determined, then the path obtained in the ant colony algorithm is optimized through the formula 4, so that the point with the shortest distance relative to the position is found, and the unmanned aerial vehicle advances along the direction of the point to reach a new position.
D in min (x, y) represents the path length after processing, D Ant Represents the path calculated by the ant colony algorithm, x represents the abscissa of the point, y represents the ordinate of the point,
representing the deviation of x in the path calculated by the ant colony algorithm,/>
The y bias derivative in the path calculated by the ant colony algorithm is shown.
8) The length of the straight line distance between the two points is calculated. As shown in formula 5, the length is calculated by comparing with the optimized path length in step 7), and the smaller value is taken as the distance between the unmanned aerial vehicle and the target position, i.e. the unmanned aerial vehicle finds the optimal path of all targets.
Where D represents the optimal path length, D Ant Represents the path length obtained by ant algorithm, D th Representing the straight line distance between the two points.
9) Judging whether the task is completed, if so, terminating the operation, otherwise, returning to the step 2), updating the pheromone matrix according to the optimal path of each step, finding out and storing the optimal task allocation result in each iteration.
When the invention is embodied, the task is completed, namely, all targets in the task area are found by unmanned aerial vehicles. The multi-unmanned aerial vehicle task allocation for improving the ant colony algorithm can be realized through the steps.
The present example randomly deploys five task points in a task area of 100 x 100, and performing task allocation on the three unmanned aerial vehicles, and searching 5 task points. The speed of the three unmanned aerial vehicles is given, and the flying speeds of the three unmanned aerial vehicles are the same. The number of clusters participating in the algorithm is set to 30, and the number of iterations is also set to 30. Giving the position of the task point and the initial position of the unmanned aerial vehicle, then continuously calculating the optimal path of each step of searching the task point by the method, and finally searching all the task points by performing task allocation. Fig. 2 shows a final roadmap in one embodiment of the method according to the invention. Three different shapes of lines in the figure represent paths taken by three unmanned aerial vehicles to find a task point.
It should be noted that the purpose of the disclosed embodiments is to aid further understanding of the present invention, but those skilled in the art will appreciate that: various alternatives and modifications are possible without departing from the spirit and scope of the invention and the appended claims. Therefore, the invention should not be limited to the disclosed embodiments, but rather the scope of the invention is defined by the appended claims.
Claims (4)
1. The method for distributing the tasks of the multiple unmanned aerial vehicles based on the improved ant colony algorithm is characterized in that the ant colony algorithm is improved, the direction of the movement transfer of the ants of the unmanned aerial vehicles in the movement process is determined by the concentration of pheromones on the flight paths of the unmanned aerial vehicles; in the task allocation searching process, the ants representing the unmanned aerial vehicle intelligently select a path to be taken next; the distance of the path calculated by adopting the ant colony algorithm is compared with the linear distance between the two points, a shorter distance is selected as the optimal path for searching the target by the unmanned aerial vehicle, and meanwhile, the gradient descent method is adopted for optimization so as to shorten the flight distance of the unmanned aerial vehicle, so that the method is suitable for task allocation of multiple unmanned aerial vehicles, and the unmanned aerial vehicle can rapidly complete the task and reduce the flight distance; the method comprises the following steps:
1) Acquiring the size of a task area, the number of unmanned aerial vehicles participating in task allocation of multiple unmanned aerial vehicles, the number of targets to be searched, the initial positions of the unmanned aerial vehicles and the targets and the speed of the unmanned aerial vehicles; the ants in the improved ant colony algorithm represent each unmanned aerial vehicle; setting the number of ant colony participated in improving an ant colony algorithm, namely the number of unmanned aerial vehicles and the iteration times of the algorithm;
2) Creating an unmanned aerial vehicle pheromone matrix and initializing;
the method comprises the steps that whether an unmanned plane path flies through a certain node is represented by a pheromone, a value of 0 represents that the unmanned plane path does not fly through the node, and a value of 1 represents that the unmanned plane path flies through the node;
setting a matrix taking the number of unmanned aerial vehicles as the number of rows and the number of target nodes as the number of columns as a pheromone matrix, wherein each element in the matrix is a pheromone;
setting two pheromone matrixes, wherein the two pheromone matrixes are respectively used for storing an optimal task allocation result found in each iteration of the improved ant colony algorithm and a final allocation result after the task is ended;
initializing all pheromone elements in the pheromone matrix to be 1;
3) Setting the total amount of unmanned aerial vehicle tasks to be completed;
4) Distributing unmanned aerial vehicles according to the pheromone matrix;
random distribution is carried out at the beginning; when the pheromone is smaller than 1, distributing according to the pheromone matrix; otherwise, carrying out random distribution; when one unmanned aerial vehicle approaches to the maximum working capacity, stopping task allocation to the unmanned aerial vehicle;
5) Updating the pheromone matrix;
path (i, j) represents a connection between an i-th node and a j-th node on the path; at time t+n, the pheromone on the path (i, j) is updated by equations 1 and 2:
τ ij (t+n)＝(1-ρ)×τ ij (t)+Δτ ij (t) (1)
Wherein t is the current time; n=1, 2, … … n; τ ij Pheromones that are paths (i, j); the constant rho epsilon (0, 1) represents the pheromone volatilization factor, namely the loss degree of information quantity on a path, and the magnitude of rho is related to the global searching capacity and convergence speed of the algorithm; 1- ρ is the pheromone residual factor; Δτ ij Pheromone delta representing path (i, j); m represents the total number of unmanned aerial vehicles,
a pheromone increment of a path (i, j) after searching is finished is represented, wherein k is the kth unmanned plane; at the initial time Deltaτ ij (0)＝0；t n Indicating the final moment +.>
A pheromone representing a path (i, j) of the kth unmanned aerial vehicle after the current traversal is finished; at the time t, ants represented by the kth unmanned aerial vehicle are transferred from the node i to the node j according to the transfer probability;
updating the pheromone matrix according to the formulas 1 and 2, obtaining paths through which the unmanned aerial vehicle finds all targets according to the pheromone matrix, recording the corresponding flight distances of all unmanned aerial vehicles, and finding out the optimal paths of the unmanned aerial vehicle;
6) Optimizing the obtained path by adopting a gradient descent method;
according to the gradient descent method, firstly determining the current position of the unmanned aerial vehicle, and then optimizing the path obtained in the step 5) through the formula 3, so as to find the point with the shortest distance relative to the position, and advancing along the direction of the point to reach a new position;
wherein D is min (x, y) represents the path length after processing, D Ant Represents the path calculated by the ant colony algorithm, x represents the abscissa of the point, y represents the ordinate of the point,
representing the deviation of x in the path calculated by the ant colony algorithm,/>
Representing the deviation derivative of y in the path calculated by the ant colony algorithm;
7) Calculating the length of the path obtained by the ant colony algorithm in the step 5) and the length of the linear distance between the two points, and comparing the lengths to obtain the optimal path for finding all targets by the unmanned aerial vehicle;
wherein D represents the optimal path length, D Ant Represents the path length obtained by ant algorithm, D th Representing the straight line distance between two points;
comparing the calculated two lengths, and taking a smaller value as the distance between the unmanned aerial vehicle and the target position, namely, the unmanned aerial vehicle finds the optimal path of all targets;
judging whether the task is completed, if so, terminating the operation, otherwise, returning to the step 4), updating the pheromone matrix according to the optimal path of each step, finding out and storing the optimal task allocation result in each iteration.
2. The method for assigning tasks to multiple unmanned aerial vehicles based on the improved ant colony algorithm of claim 1, wherein the number of unmanned aerial vehicle groups is set to 30.
3. The method for multi-unmanned aerial vehicle task allocation based on the improved ant colony algorithm according to claim 1, wherein the iteration number is set to 30.
4. The improvement-based system of claim 1A multi-unmanned aerial vehicle task allocation method of an ant colony algorithm is characterized in that in the step 5), the state transition probability of ants k from a node i to a node j is provided
Expressed as formula 3:
wherein, allowed k ＝{C-tabu k All nodes which can be selected in the next step of ants represented by a kth unmanned aerial vehicle are represented by the (k) th unmanned aerial vehicle, and C is all node sets; tabu k The method is used for recording all nodes which the ant represented by the kth unmanned aerial vehicle currently walks through, alpha is an information heuristic factor, represents the relative importance degree of the track in an algorithm, and reflects the influence degree of the information quantity on the path on the selection path of the ant represented by the unmanned aerial vehicle; beta is called the desirability heuristic; η (eta) ij Is a heuristic function that represents in an algorithm the desired degree of transition from node i to node j.
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