CN110319829B - Unmanned aerial vehicle flight path planning method based on self-adaptive polymorphic fusion ant colony algorithm - Google Patents
Unmanned aerial vehicle flight path planning method based on self-adaptive polymorphic fusion ant colony algorithm Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle flight path planning method based on a self-adaptive polymorphic fusion ant colony algorithm, which belongs to the field of unmanned aerial vehicle flight path planning, and the method introduces a polymorphic ant colony algorithm into the ant colony algorithm, combines the self-adaptive polymorphic ant colony algorithm with the polymorphic ant colony algorithm, forms a global and local parallel search mode, improves the capability of the algorithm for searching a global optimum value, shortens the search time and accelerates the convergence speed; according to the method, on the basis of the traditional polymorphic ant colony algorithm, a self-adaptive parallel rule and a pseudo-random rule are introduced, a state transition rule and a self-adaptive conversion probability are provided, a self-adaptive information updating strategy is introduced, and the problem that the local optimization is easy to fall into in the searching process is solved by adopting the method.
Description
Technical Field
The invention relates to an unmanned aerial vehicle flight path planning method based on a self-adaptive polymorphic fusion ant colony algorithm, and belongs to the field of unmanned aerial vehicle flight path planning.
Background
The ant colony algorithm refers to collective search of natural foods by simulating ants, and an ant colony Algorithm (ACO) is proposed. Ant foraging is a heuristic bionic algorithm based on colony, and is not a single ant which searches food sources independently. Foraging behavior is dependent on communication between ants and ants or between the individual ants and the environment, based on the use of chemicals (called pheromones) produced by ants. The working principle of ants is as follows: first, when ants reach a decision point where they must decide to turn left or right, they will randomly choose the next path and deposit the pheromone on the ground, since they do not know which is the best choice. After a short selection, the difference in the amount of pheromones on the two paths is sufficient to influence the decision of a new ant to enter the system. From this point on, new ants will be more likely to select paths with a greater amount of information. Thus, ants can smell the pheromone taste and are likely to select a path marked by a strong pheromone concentration.
In the real ant society, the ant colony is organized and divided. The polymorphic ant colony algorithm based on the traditional ant colony algorithm, wherein the polymorphism refers to ant colonies with various states and pheromones of an ant colony society, and ants are divided into scout ants, search ants and worker ant ants according to different division of labor. The ant colony of the worker ants is irrelevant to path optimization, so that respective pheromone regulation mechanisms only need to be designed for the ant colony reconnaissance and the ant colony search. The ant colony is responsible for local reconnaissance, the ant colony search is responsible for global search, the division improvement by the ant colony greatly improves the cooperation effect among the ant colonies, and the effectiveness of the algorithm is enhanced. The investigation ant takes the path node of the unmanned aerial vehicle as the center and leaves the investigation element in the investigation process so as to make a selection when the search ant reaches the path node.
Deadlock refers to a situation in which an ant colony may fall into a state of being unable to move when encountering a complex environment in a path search process. This condition is called deadlock. When the mesh length is set to 1, when the drone then moves in the direction of the arrow in fig. 1 at points a and B, if the drone cannot move to other locations around in this location, the algorithm is said to be stuck in a deadlock condition.
The ant colony algorithm has the characteristics of positive feedback, distributed computation and greedy search, so that the ant colony algorithm has stronger robustness and searchability and is applied to multiple fields at present. Besides, the ant colony algorithm has the defects of long search time, slow convergence speed, easy falling into local optimum and the like. In the real nature, the ant colony in the real ant colony society is planned and organized, different kinds of ant colonies have different pheromone regulation and control modes, and the different control modes have very important roles in completing complicated tasks by the colonies.
Disclosure of Invention
The invention aims to solve the technical problem of providing an unmanned aerial vehicle flight path planning method based on a self-adaptive polymorphic fusion ant colony algorithm.
In order to solve the technical problems, the invention adopts the following technical scheme:
an unmanned aerial vehicle flight path planning method based on a self-adaptive polymorphic fusion ant colony algorithm comprises the following steps:
step 1, modeling an initial environment by adopting a grid method, and setting constants Q, C and N;
wherein Q represents the total amount of pheromones released by the scout ant in one cycle, C represents the information amount on each path at the initial time, and N represents the specified iteration times;
wherein j belongs to i and represents that a feasible point j of the scout ant is in an unmanned plane path node i;
dij is the total path taken by the selected scout ant;
d ij min the minimum distance from the unmanned aerial vehicle path node i to other N-1 path nodes is taken as a path center;
and 3, setting initial information quantity on each path at the initial time according to the following formula (2):
wherein d is ij max The maximum distance from the path node i as the center to other N-1 path nodes is taken as the center;
c is a constant and is the information amount on each path at the initial moment;
wherein q is a random number belonging to [0,1 ];
j belongs to allowedk and represents that j is a feasible point;
q 0 ＝1-e -1-k (k =1,2,3.... Times, N), N being the number of iterations;
τ ij (t) represents the amount of information on the current time path;
η ij (t) represents a heuristic function representing the expected degree of ant transfer from path point i to path point j,
alpha represents a pheromone importance factor, and the larger the value of alpha is, the larger the pheromone concentration plays a role in metastasis;
beta represents the importance degree factor of the heuristic function, which is the relative importance degree of the priori knowledge in the process of guiding the ant search, and the larger beta is, the larger the effect of the heuristic function in the transfer process is, and the larger the probability that the ant selects the node with the short path is;
P ij k (t) represents the adaptive transition probability, which is expressed as follows:
wherein eta i Representing the potential node number of the ants at the path node i to move next;
upsilon represents the relative importance degree of the number of potential path nodes moving in the next step to the selection of ant paths, and the larger the number of potential path nodes in the next step is, the fewer pheromones are, namely the number of pheromones is in inverse proportion to the number of potential path nodes;
when the search ant k sinks into deadlock in the path search, adopting a direction determination method to solve the problem of deadlock; step 6, calculating the path length L of each search ant k (k =1,2,.., m), and recording a current optimal solution; 7, when the specified iteration number N is reached or the solution is not improved in the last ten iterations, jumping to the step 9; otherwise, the pheromone concentration of each path is modified according to equation (5), equation (5) being as follows:
wherein, Δ τ ij k (t) represents pheromone concentration;
q represents the total amount of pheromone released by the ants in one cycle;
d k l the shortest distance from the path node i as the center to other n-1 path nodes is represented;
L best represents the optimal path length;
and 9, outputting the optimal solution.
Further, when the search ant k sinks into the deadlock in the path search, a direction determination method is adopted to solve the deadlock problem, and the specific method is as follows:
when the search ant k is in a deadlock state, the taboo table is updated, pheromones at deadlock edges are punished, so that the search ant k can adjust the advancing direction at the original position, find a barrier-free direction and then continue to advance.
The invention has the following beneficial effects:
(1) The invention introduces the polymorphic ant colony algorithm into the ant colony algorithm, combines the self-adaption and the polymorphic ant colony algorithm to form a global and local parallel search mode, improves the capability of the algorithm for searching the global optimum value, shortens the search time and accelerates the convergence speed.
According to the method, on the basis of the traditional polymorphic ant colony algorithm, a self-adaptive parallel rule and a pseudo-random rule are introduced, a state transition rule and a self-adaptive conversion probability are provided, a self-adaptive information updating strategy is introduced, and the problem that the local optimization is easy to fall into in the searching process is solved by adopting the method.
(2) Aiming at the problems that search ants are easy to fall into local optimization when studying the determined range of elements, and pheromone intensity and expected intensity are ignored in the iteration process, the method adopts a self-adaptive parallel rule and a pseudorandom parallel rule on the basis of a polymorphic ant colony algorithm, and effectively avoids the problem that search ants are easy to fall into local optimization in the search process. And searching ants to perform state transfer according to the pseudorandom rule in the searching stage, and determining the optimal combination parameters in the state transfer function by adopting a self-adaptive parallel strategy.
(3) In the polymorphic ant colony algorithm, search ants leave pheromones in the best possible solution path, while ignoring other paths, which may result in a final solution, perhaps not the best. To avoid this situation, the present invention introduces an adaptive information update strategy. The global searching capability is enhanced.
(4) The early death method aiming at the deadlock problem is not beneficial to global optimization and reduces the diversity of path solutions. When the ant is in a deadlock state, the taboo table is updated, and the pheromone at the deadlock edge is punished, so that the ant can adjust the advancing direction at the original position, find the barrier-free direction and then continue to advance.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Figure 1 shows a schematic diagram of a drone stuck in a deadlock.
Fig. 2 is a schematic diagram of deadlock resolution for a drone.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail and fully with reference to the accompanying drawings 1-2 and the specific embodiments.
The invention mainly combines self-adaptation and polymorphic ant colony algorithms, the combined algorithm is applied to the unmanned aerial vehicle track planning, the key point is to introduce the polymorphic ant colony algorithm, the ant division cooperation among the polymorphic ant colony is utilized, and a global and local parallel search mode is formed by detecting and searching the ants, so that the capability of the algorithm for searching the global optimum value is improved. The method mainly solves the problems that the traditional ant colony algorithm is small in pheromone concentration difference, unobvious in positive feedback effect, blind in path searching, relatively slow in convergence speed and prone to falling into local optimum in the initial stage of flight path planning.
As shown in fig. 1 and fig. 2, an unmanned aerial vehicle flight path planning method based on an adaptive polymorphic fusion ant colony algorithm includes the following steps:
step 1, modeling an initial environment by adopting a grid method, and setting constants Q, C and N;
wherein Q represents the total amount of pheromones released by the scout ant in one cycle, C represents the information amount on each path at the initial time, and N represents the specified iteration times;
wherein j belongs to i and represents that a feasible point j of the scout ant is in an unmanned plane path node i;
dij is the total path taken by the selected scout ant;
d ij min the minimum distance from the unmanned aerial vehicle path node i to other N-1 path nodes is taken as a path center;
and 3, setting initial information quantity on each path at the initial time according to the following formula (2):
wherein d is ij max The maximum distance from the path node i to other N-1 path nodes is taken as the center;
c is a constant and is the information amount on each path at the initial moment;
step 5, calculating the next step transfer position of each search ant k according to a formula (3), and setting the next step transfer position as j; setting the previous position as i, and putting j into a corresponding taboo table of the search ant k until each search ant k completes a cycle to obtain a solution;
wherein q is a random number belonging to [0,1 ];
j belongs to allowedk and represents that j is a feasible point;
q 0 ＝1-e -1-k (k =1,2,3.... Times, N), N being the number of iterations;
τ ij (t) represents the amount of information on the current time path;
η ij (t) represents a heuristic function representing the expected degree of ant transfer from path point i to path point j,
alpha represents the pheromone importance factor, and the larger the value of alpha is, the larger the pheromone concentration plays a role in metastasis;
beta represents an importance degree factor of the heuristic function, which is the relative importance degree of the priori knowledge in the process of guiding the ant search, and the larger beta represents that the greater the effect of the heuristic function in the transfer process is, the greater the probability that the ant selects a node with a short path is;
P ij k (t) represents the adaptive transition probability, which is expressed as follows:
wherein eta is i Representing the potential node number of the ants at the path node i moving next step;
upsilon represents the relative importance degree of the number of potential path nodes moving in the next step to the selection of ant paths, and the larger the number of potential path nodes in the next step is, the fewer pheromones are, namely the number of pheromones is in inverse proportion to the number of potential path nodes;
when the search ant k sinks into the deadlock in the path search, a direction determination method is adopted to solve the problem of the deadlock; step 6, calculating the path length L of each search ant k (k＝1, 2.., m), and recording the current optimal solution; 7, when the specified iteration number N is reached or the solution is not improved in the last ten iterations, jumping to the step 9; otherwise, the pheromone concentration of each path is modified according to equation (5), equation (5) being as follows:
wherein, Δ τ ij k (t) represents pheromone concentration;
q represents the total amount of pheromone released by the ants in one cycle;
d k l the shortest distance from the path node i as the center to other n-1 path nodes is represented;
L best represents the optimal path length;
and 9, outputting the optimal solution.
Further, when the search ant k catches in the deadlock in the path search, a direction determination method is adopted to solve the deadlock problem, and the specific method is as follows:
when the search ant k is in a deadlock state, the taboo table is updated, pheromones at deadlock edges are punished, so that the search ant k can adjust the advancing direction at the original position, find a barrier-free direction and then continue to advance.
To resolve the deadlock, scientists have taken the approach of early death. The main idea of their method is to die ants trapped in deadlocks, stopping the updating of the pheromones of the paths that have been travelled. When a large number of ants are in a deadlock state, the method is not only not beneficial to global optimization, but also reduces the diversity of path solutions.
Interpretation of terms: 1. polymorphic ant colony algorithm: in the real ant society, the ant colony is organized and has division of labor. The polymorphic ant colony algorithm based on the traditional ant colony algorithm, wherein the polymorphism refers to ant colonies with various states and pheromones of an ant colony society, and ants are divided into scout ants, search ants and worker ant ants according to different division of labor. The ant colony of the worker ants is irrelevant to path optimization, so that respective pheromone regulation mechanisms only need to be designed for the ant colony reconnaissance and the ant colony search. The ant colony is responsible for local reconnaissance, the ant colony search is responsible for global search, the division improvement by the ant colony greatly improves the cooperation effect among the ant colonies, and the effectiveness of the algorithm is enhanced. The ant reconnaissance takes the path node of the unmanned aerial vehicle as a center, and leaves a survey element in the survey process so as to make a selection when the search ant reaches the path node.
2. Deadlock: when the ant colony encounters a complex environment in the path searching process, the ant colony may be trapped in a state of being unable to move. This condition is called deadlock. When the mesh length is set to 1, when the drone then moves in the direction of the arrow in fig. one at points a and B, if the drone cannot move to other locations around in this location, the algorithm is said to be stuck in a deadlock.
Polymorphic ant colony algorithm:
the investigation ants take the path nodes of the unmanned aerial vehicle as the center, leave the investigation elements in the investigation process, and make a selection when the search ants reach the path nodes. Scouting and searching ants in a polymorphic ant colony perform the following tasks:
and (3) detecting the ants: the scout ants (number: m) are respectively placed at the path nodes (number: n) of the unmanned aerial vehicles, each scout ant takes the path node (number: n-1) of one unmanned aerial vehicle as the center, and the investigation result is combined with the existing information to form s ij And marking on the route. From path node i to path node j S ij (i,j＝1,2,.....,n-1,i≠j)
The following formula is used for calculation:
wherein dij is the total path of the selected ant; d ij min When the unmanned aerial vehicle path node i is taken as the center, the unmanned aerial vehicle path node i reaches other n-1 path nodesThe minimum distance of the points.
Based on this result, the information amount of each path at the initial time is first set as follows:
wherein d is ij max Is the maximum distance to other n-1 path nodes centered on path node i. C is a constant and is the amount of information on each path at the initial time.
Searching ants: adaptive transition probability
Wherein, tau ij (t) is the amount of information on the current path η ij (t) is a heuristic function representing the expected degree of ant transfer from path point i to path point j; alpha and beta are two important parameters, eta i Representing the number of potential nodes moved by ants at the node i in the next step, wherein alpha is an pheromone importance degree factor, and the larger the value of alpha is, the larger the function of the pheromone concentration in transfer is represented; beta is an importance degree factor of the heuristic function, is the relative importance degree of the priori knowledge in the process of guiding ant searching, and the larger beta represents the larger function of the heuristic function in the transfer process, namely, ants can select nodes with relatively short distance by using relatively high probability, upsilon represents the relative importance degree of the potential node quantity moving in the next step to ant path selection, the more potential nodes in the next step, the less pheromones, namely, the pheromones are in inverse proportion to the potential node quantity. The values of alpha and beta are crucial and directly influence the performance of the algorithm.
After all ants complete one cycle, the pheromone on each path is updated to the following formula:
whereinRho (0 < rho < 1) is pheromone volatility coefficient, 1-rho pheromone persistence, Δ τ ij Represents the sum of the information amount released by all ants on the path (i, j) in the current cycle,
Δτ ij k indicating the amount of information that the kth ant left on the path (i, j) in the current cycle.
Wherein Q is a constant, L k Is the path length that the kth ant has traveled.
Polymorphic self-adaptive ant colony algorithm:
in the polymorphic ant colony algorithm, search ants can still fall into local optimum within the range determined by research elements, and pheromone intensity and expected intensity are ignored in iteration. On the basis of the polymorphic ant colony algorithm, a self-adaptive parallel rule and a pseudorandom parallel rule are introduced, so that the problem that the search process is easy to fall into local optimum is effectively avoided. And searching ants to perform state transfer according to the pseudorandom rule in the searching stage, and determining the optimal combination parameters in the state transfer function by adopting a self-adaptive parallel strategy. The state transition rules employed are as follows:
wherein q is a member belonging to [0,1]Random number of (c), q0=1-e -1-k (K =1,2,3.. Times, N), N being the number of iterations.
According to the state transition rule, each time an ant chooses which track node to move to, a random number is generated in [0,1], and then the transition direction is determined according to the state transition rule. The adaptive transition probability may be computed as:
τ ij (t) the amount of information on the path (i, j) at time t; eta i j (t) is a heuristic function expressed as η ij (t)＝1/d ij (ii) a Alpha is an information importance degree factor and represents the relative importance of the path; β is a desired heuristic factor representing the relative importance of visibility; tabu k Is a tabu set.
In the polymorphic ant colony algorithm, search ants leave pheromones in the best possible solution path, while ignoring other paths, which may result in a final solution, perhaps not the best. To avoid this, an adaptive information update strategy is introduced herein, and the amount of adaptive information is determined by the following formula:
adaptively updating the amount of information in the search process avoids ignoring a path because there are fewer pheromones on the path, and also prevents pheromones from decreasing as the number of iterations increases.
Deadlock problem:
the ant colony may deadlock when encountering a complex environment in the path searching process. When the mesh length is set to 1, when the drone then moves in the direction of the arrow in fig. one at points a and B, if the drone cannot move to other locations around in this location, the algorithm is said to be stuck in a deadlock.
The direction determining method is a tool for solving the deadlock problem, when ants are in a deadlock state, the taboo table is updated, pheromones at deadlock edges are punished, so that the ants can adjust the advancing direction at the original position, find the barrier-free direction and then continue to advance.
This direction is indicated by the arrow in fig. 2. The application of the method in solving the deadlock problem aims to improve the global search capability of the algorithm and effectively reduce the possibility that other ants fall into deadlock at the same position, and the pheromone punishment formula of the method is as follows:
τ rs ＝(1-λ)τ rs (8)
where 1- λ is the corresponding penalty factor.
To resolve the deadlock, scientists have taken the approach of early death. The main idea of their method is to die ants trapped in deadlocks, stopping the updating of the pheromones of the paths that have been travelled. When a large number of ants are in a deadlock state, the method is not only unfavorable for global optimization, but also reduces the diversity of path solutions.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (1)
1. An unmanned aerial vehicle flight path planning method based on a self-adaptive polymorphic fusion ant colony algorithm is characterized by comprising the following steps:
step 1, modeling an initial environment by adopting a grid method, and setting constants Q, C and N;
wherein Q represents the total amount of pheromone released by the scout ant at one cycle, and N represents the initial time per path
The information quantity, N represents the specified number of iterations;
step 2, respectively placing m scout ants on n path nodes; each scout ant investigates other n-1 path nodes with the path node as the center; the survey factor S is calculated according to the following formula (1) i (i, j =1, 2.... Times.n-1, i ≠ j), and places the result in s ij The method comprises the following steps:
wherein j belongs to i and represents that a feasible point j of the scout ant is in an unmanned plane path node i;
d ij a total path taken by the selected scout ant;
d ij min the minimum distance from the unmanned aerial vehicle path node i to other n-1 path nodes is taken as a path center;
and 3, setting initial information quantity on each path at the initial time according to the following formula (2):
wherein d is ij min The maximum distance from the path node i to other n-1 path nodes is taken as the center;
c is a constant and is the information amount on each path at the initial moment;
step 4, randomly selecting the initial position of each search ant and putting the initial position into a corresponding taboo set;
step 5, calculating the next transfer position of each search ant k according to the following calculation, and setting the next transfer position as j; setting the previous position as i, and putting j into a corresponding tabu table of the search ant k until each search ant k completes one cycle to obtain a solution;
wherein q is a random number belonging to [0,1 ];
j∈allowde k represents j as a feasible point;
τ ij (t) represents the amount of information on the current time path;
η ij (t) represents a heuristic function representing the expected degree of ant transfer from path point i to path point j;
alpha represents a pheromone importance factor, and the larger the value of alpha is, the larger the pheromone concentration plays a role in metastasis;
beta represents the importance degree factor of the heuristic function, which is the relative importance degree of the priori knowledge in the process of guiding the ant search, and the larger beta is, the larger the effect of the heuristic function in the transfer process is, and the larger the probability that the ant selects the node with the short path is;
P ij k (t) represents the adaptive transition probability, which is expressed as follows:
wherein eta is i Representing the potential node number of the ants at the path node i to move next;
upsilon represents the relative importance degree of the number of potential path nodes moving in the next step to the selection of ant paths, and the larger the number of potential path nodes in the next step is, the fewer pheromones are, namely the number of pheromones is in inverse proportion to the number of potential path nodes;
taub k is a tabu set;
when the search ant k sinks into the deadlock in the path search, a direction determination method is adopted to solve the problem of the deadlock;
the direction determination method comprises the following steps: when the search ant k is in a deadlock state, updating the tabu table, punishing the pheromone at the deadlock edge, so that the search ant k can adjust the advancing direction at the original position, find out the barrier-free direction and then continue to advance;
step 6, calculating the path length L of each search ant k (k =1,2,.., m), and recording a current optimal solution;
step 7, when the specified iteration times are reached or the required solution is not improved in the last ten iterations, skipping to step 9; otherwise, the pheromone concentration of each path is modified according to equation (5), equation (5) being as follows:
wherein, Δ τ ij k (t) represents pheromone concentration;
q represents the total amount of pheromone released by ants once in a cycle;
step 8, setting i =0 and j =0, clearing the tabu table, updating N for N +1, and returning to the step 4;
and 9, outputting the optimal solution.
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