CN111896001A - Three-dimensional ant colony track optimization method - Google Patents
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- G—PHYSICS
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The invention relates to a three-dimensional ant colony track optimization method, which can reduce the route airspace search range, reduce the search feasible nodes in a three-dimensional space, accelerate the convergence speed of an algorithm and improve the search efficiency of an unmanned aerial vehicle in the three-dimensional space by adding an angle factor in a local search space.
Description
Technical Field
The invention relates to the technical field of three-dimensional flight paths, in particular to a three-dimensional ant colony flight path optimization method.
Background
With the rise of the intelligent field, the flight path planning of the unmanned aerial vehicle becomes the key point of research of scholars at home and abroad, and the unmanned aerial vehicle has higher and higher value in the military field or the civil field. Conventional track planning algorithms include a heuristic A-star algorithm, an artificial potential field method, a particle swarm algorithm, a genetic algorithm and the like.
The basic ant colony algorithm has the defects of low convergence speed, long calculation time, easy early trapping into partial optimality and the like, and has certain limitations in three-dimensional flight path planning, and particularly, an optimal flight path is difficult to plan in a three-dimensional space with a large scale and has low convergence. Compared with other search optimization algorithms, the novel ant colony algorithm has good global search capability in the aspect of solving the problem of unmanned aerial vehicle flight path optimization in the three-dimensional environment.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a three-dimensional ant colony track optimization method.
The purpose of the invention can be realized by the following technical scheme:
a three-dimensional ant colony track optimization method is applied to unmanned aerial vehicle track planning and comprises the following steps:
step 1: establishing a three-dimensional environment model;
step 2: inputting an initial pheromone into the three-dimensional environment model, setting a starting point and an end point, and setting algorithm parameters;
and step 3: placing ants at a starting point to obtain the transition probability of the current state;
and 4, step 4: designing a search domain around each node between a starting point and an end point, setting a priority search set for the nodes in the search domain, and acquiring a node self-adaptive threshold value in the search domain;
and 5: comparing the current state transition probability with an adaptive threshold, and selecting the next node where the ant walks according to the comparison result:
step 6: updating the advancing path and the corresponding path length of the ants in real time;
and 7: the step 3 to the step 6 are circulated until the ant reaches the end point;
and 8: the step 3 to the step 7 are circulated until all M ants of the generation traverse;
and step 9: dynamically updating an information elicitation factor alpha, an expected elicitation factor beta and a pheromone;
step 10: the step 3 to the step 9 are circulated until iteration is completed, and a corresponding path generated based on the ant colony is obtained;
step 11: and optimizing the generated corresponding path and outputting an optimal track path.
Further, the algorithm parameters in step 2 include a total number of iterations N, a total number M of ants in each generation, an pheromone intensity coefficient Q, an pheromone volatilization coefficient ρ, an initial information heuristic factor α, and an initial expected heuristic factor β.
Further, in step 5, the current state transition probability is compared with the adaptive threshold, and the selection rule of the next node where the ant walks is selected according to the comparison result, and the corresponding description formula is as follows:
in the formula, q(i+1,j+1,k+1)Is the position of the next node, ηijkIs the heuristic factor carried on the node (i, j, k).
Further, the local update formula of the pheromone in step 9 is as follows:
τijk＝(1-λ)τijk+λτ0
λ∈(0,1)
in the formula, τ0Is the initial pheromone value, λ is the attenuation coefficient of the pheromone, τijkIs the pheromone value carried on node (i, j, k).
Further, the global update formula of the pheromone in step 9 is as follows:
τijk＝(1-λ)τijk+λΔτijk
in the formula,. DELTA.tauijkThe pheromone increment of all nodes (i, j, k) of all ants on the flight path is calculated, C is a constant, and length (alpha)1) Is alpha1Each ant completes the length of the path.
Further, the formula for calculating the heuristic factor is as follows:
where eta is a heuristic factor, D is a distance factor, S0Is the judgment result value, eta, of the feasible point and the infeasible point1Is an angle factor.
Further, the calculation formula of the determination result values of the feasible point and the infeasible point is as follows:
in the formula, h is the height of the expansion node, and map is the height of the three-dimensional map corresponding to the expansion node.
Further, the distance factor is calculated by the formula:
D＝ωD1+(1-ω)D2
ω∈(0,1)
wherein D is a distance factor, S is a starting point, and S is1To a band extension node, S2Is the target point, (x)s，ys，zs) As coordinates of the target point, (x)i，yi，zi) As the current point coordinate, (x)i+1，yi+1，zi+1) The next node coordinate.
Further, the angle factor is calculated by the formula:
in the formula eta1Is an angle factor, theta is a yaw angle, dijIs the distance of the current point to the target path.
Further, the calculation formula of the distance from the current point to the target path is:
compared with the prior art, the invention has the following advantages:
(1) aiming at the defects of low convergence speed, long calculation time and the like of the basic ant colony algorithm, the method improves the ant colony algorithm, improves the local search strategy and the heuristic factor of the algorithm, so that the three-dimensional path planning of the unmanned aerial vehicle can realize the function of rapid convergence.
(2) According to the method, the angle factor is added into the local search space, so that the air route airspace search range is reduced, the search feasible nodes in the three-dimensional space are reduced, the convergence speed of the algorithm is increased, and the search efficiency of the unmanned aerial vehicle in the three-dimensional space is improved.
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FIG. 1 is a schematic diagram of the relationship between nodes in the embodiment of the method of the present invention;
FIG. 2 is a three-dimensional space node selection diagram according to an embodiment of the present invention;
FIG. 3 is a flowchart of an algorithm of an embodiment of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
Fig. 3 shows an algorithm flowchart of an embodiment of the method of the present invention, which specifically includes:
the method comprises the following steps: establishing a three-dimensional environment model;
step two: inputting initial pheromones, selecting a starting point and an end point, and setting algorithm parameters; the algorithm parameters comprise iteration total times N, total number M of ants in each generation, pheromone intensity coefficient Q, pheromone volatilization coefficient rho, initial information heuristic factor alpha and initial expected heuristic factor beta;
step three: placing ants at a starting point, and calculating the current state transition probability;
step four: designing a search domain around each node, setting a priority search set for the nodes in the search domain, and calculating a node self-adaptive threshold value in the search domain;
step five: comparing the current state transition probability with a self-adaptive threshold, and selecting the next node where the ant walks according to the comparison result;
step six: updating the path and the path length;
step seven: repeating the third step to the sixth step until the ant reaches the end point;
step eight: repeating the third step to the seventh step until all the M ants of the generation traverse;
step nine: dynamically updating an information elicitation factor alpha, an expected elicitation factor beta and a pheromone;
step ten: repeating the third step to the ninth step until the iteration is finished;
step eleven: optimizing the generated path;
step twelve: and outputting the optimal path.
The whole process specifically relates to the following principle contents:
design of one, heuristic function
(1) Feasible point and infeasible point
For the safety consideration of unmanned aerial vehicle flight, make unmanned aerial vehicle can be safe avoid barrier and threat thereof, the event carries out evaluation and treatment to unmanned aerial vehicle's flight node. When the flight height of the unmanned aerial vehicle is greater than the height of the map, the unmanned aerial vehicle is a feasible point. Otherwise, it is an infeasible point. The following equation was used.
In the formula, h is the height of the expansion node, and map is the height of the three-dimensional map corresponding to the expansion node.
(2) Distance factor and yaw angle factor
The shortest path planning is related to the selected point and the yaw angle of the unmanned aerial vehicle, and is shown in fig. 1, wherein the starting point, the extended node and the target point, the yaw angle, and the euclidean distance from the node to be extended to the connecting line between the starting point and the target point.
The relationship between them is as follows:
the distance factor of the unmanned aerial vehicle is set as:
D＝ωD1+(1-ω)D2
ω∈(0,1)
wherein D is a distance factor, S is a starting point, and S is1To a band extension node, S2Is the target point, (x)s，ys，zs) As coordinates of the target point, (x)i，yi，zi) As the current point coordinate, (x)i+1，yi+1，zi+1) The next node coordinate.
Unmanned aerial vehicle receives the influence of yaw angle in flight process, so introduce the angle factor:
in the formula eta1Is an angle factor, theta is a yaw angle, dijFor the distance from the current point to the target path, the flying route of the unmanned aerial vehicle is constrained to be as close to the target distance as possible, turning is reduced, and the information heuristic factor alpha and the expected heuristic factor beta represent the importance degree of the angle and the distance from the point to the target.
The calculation formula of the distance from the current point to the target path is specifically as follows:
the distance factor and the angle factor of the unmanned aerial vehicle determine the flight direction of the unmanned aerial vehicle, the feasible point determines the safe area of the flight space, and the unmanned aerial vehicle can find the globally optimal node in the safe area by the aid of the distance factor and the angle factor. Therefore, the selected heuristic factors are as follows:
where eta is a heuristic factor, D is a distance factor, S0Is the judgment result value, eta, of the feasible point and the infeasible point1Is an angle factor.
Second, pheromone updating
(1) The local update formula of the pheromone is as follows:
τijk＝(1-λ)τijk+λτ0
λ∈(0,1)
in the formula, τ0Is the initial pheromone value, λ is the attenuation coefficient of the pheromone, τijkIs the pheromone value carried on node (i, j, k).
(2) The global pheromone update formula is:
τijk＝(1-λ)τijk+λΔτijk
in the formula,. DELTA.tauijkThe pheromone increment of all nodes (i, j, k) of all ants on the flight path is calculated, C is a constant, and length (alpha)1) Is alpha1Each ant completes the length of the path.
Third, route selection
When planning a path, the search area is pi (tan theta)2And specifies the direction of motion of the drone as being within the area defined by the yaw angle. The maximum transverse movement distance of the unmanned aerial vehicle is Lxmax, and the maximum longitudinal movement distance of the unmanned aerial vehicle is Lymax. Therefore, the next flight space of the unmanned aerial vehicle is selected from 13 nodes in the search domain, according to the path search method, when the unmanned aerial vehicle selects the next node i +1 from the current node i, the position information of the node where the unmanned aerial vehicle is currently located is used for constructing a tabu table, the node information which can pass through in the current path search space is calculated, calculation is carried out through a heuristic function, and finally the values of heuristic functions of other nodes which can pass through are obtained. The selection rule is as follows:
in the formula, q(i+1,j+1,k+1)Is the position of the next node, ηijkIs the heuristic factor carried on the node (i, j, k).
According to the method, the local search strategy of the ant colony algorithm is improved, the heuristic function design is improved, the convergence speed of the algorithm is increased, and an enough rational design scheme is provided for the air route design of the unmanned aerial vehicle.
When the angle factor selection node is added, the unmanned aerial vehicle is restrained from selecting feasible nodes around a target path from an initial point to a terminal point, and the searched path reaches the target with the shortest path.
The yaw angle θ is added to the conventional ant colony algorithm, and the node search in the three-dimensional space is as shown in fig. 2. O point as the current point, ak+1As a node distribution map for searching the next layer, a space in which a search domain is a pyramid is formed from the previous node to the next node due to the influence of the yaw angle θ. Therefore, after the angle factor is added, the nodes in the space with the search step length of 2 can be reduced to 13 nodes from the original 25 nodes, and the area searched by the unmanned aerial vehicle is reduced to pi (tan theta)2Where is the step size. Therefore, the ant colony algorithm after adding the angle greatly reduces the number of searching nodes. And the convergence speed of the algorithm is improved.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A three-dimensional ant colony track optimization method is applied to unmanned aerial vehicle track planning and is characterized by comprising the following steps:
step 1: establishing a three-dimensional environment model;
step 2: inputting an initial pheromone into the three-dimensional environment model, setting a starting point and an end point, and setting algorithm parameters;
and step 3: placing ants at a starting point to obtain the transition probability of the current state;
and 4, step 4: designing a search domain around each node between a starting point and an end point, setting a priority search set for the nodes in the search domain, and acquiring a node self-adaptive threshold value in the search domain;
and 5: comparing the current state transition probability with an adaptive threshold, and selecting the next node where the ant walks according to the comparison result:
step 6: updating the advancing path and the corresponding path length of the ants in real time;
and 7: the step 3 to the step 6 are circulated until the ant reaches the end point;
and 8: the step 3 to the step 7 are circulated until all M ants of the generation traverse;
and step 9: dynamically updating an information elicitation factor alpha, an expected elicitation factor beta and a pheromone;
step 10: the step 3 to the step 9 are circulated until iteration is completed, and a corresponding path generated based on the ant colony is obtained;
step 11: and optimizing the generated corresponding path and outputting an optimal track path.
2. The three-dimensional ant colony flight path optimization method according to claim 1, wherein the algorithm parameters in the step 2 include total iteration number N, total number M of ants per generation, pheromone intensity coefficient Q, pheromone volatilization coefficient p, initial information heuristic factor α, and initial expected heuristic factor β.
3. The method as claimed in claim 1, wherein the step 5 is a rule for comparing the current state transition probability with an adaptive threshold and selecting the next node for ant walking according to the comparison result, and the corresponding description formula is:
in the formula, q(i+1,j+1,k+1)Is the next sectionPosition of point, ηijkIs the heuristic factor carried on the node (i, j, k).
4. The three-dimensional ant colony track optimization method according to claim 1, wherein the local update formula of the pheromone in the step 9 is as follows:
τijk＝(1-λ)τijk+λτ0
λ∈(0,1)
in the formula, τ0Is the initial pheromone value, λ is the attenuation coefficient of the pheromone, τijkIs the pheromone value carried on node (i, j, k).
5. The three-dimensional ant colony track optimization method according to claim 1, wherein the global update formula of the pheromone in the step 9 is as follows:
τijk＝(1-λ)τijk+λΔτijk
in the formula,. DELTA.tauijkThe pheromone increment of all nodes (i, j, k) of all ants on the flight path is calculated, C is a constant, and length (alpha)1) Is alpha1Each ant completes the length of the path.
6. The three-dimensional ant colony track optimization method according to claim 3, wherein the formula for calculating the heuristic factor is as follows:
where eta is a heuristic factor, D is a distance factor, S0Is the judgment result value, eta, of the feasible point and the infeasible point1Is an angle factor.
7. The three-dimensional ant colony track optimization method according to claim 6, wherein the calculation formula of the judgment result values of the feasible point and the infeasible point is as follows:
in the formula, h is the height of the expansion node, and map is the height of the three-dimensional map corresponding to the expansion node.
8. The method according to claim 6, wherein the distance factor is calculated by the following formula:
D＝ωD1+(1-ω)D2
ω∈(0,1)
wherein D is a distance factor, S is a starting point, and S is1To a band extension node, S2Is the target point, (x)s，ys，zs) As coordinates of the target point, (x)i，yi，zi) As the current point coordinate, (x)i+1，yi+1，zi+1) The next node coordinate.
9. The method according to claim 6, wherein the angle factor is calculated by the following formula:
in the formula eta1Is an angle factor, theta is a yaw angle, dijIs the distance of the current point to the target path.
10. The method according to claim 9, wherein the distance between the current point and the target path is calculated by the following formula:
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