CN114326792A - Unmanned aerial vehicle collaborative track control method based on mixed group intelligent algorithm - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle collaborative track control method based on a hybrid group intelligent algorithm, and belongs to the technical field of positioning navigation and control. Firstly, designing a collaborative track evaluation function of the unmanned aerial vehicle according to a required constraint item; secondly, setting related parameters of the intelligent algorithm of the mixed group, initializing a particle group and a tabu table, and setting the maximum iteration times of the algorithm; then, judging the spatial position of the particle solution according to the track evaluation function, comparing to generate an individual optimal solution, judging whether to update a global optimal solution, repeating iteration until the maximum iteration times are reached to obtain an individual extreme value and a global extreme value of the particle, and updating the particle position and speed formula; finally, each unmanned aerial vehicle updates the state according to the particle position and speed formula and moves to the next track point to generate a new particle population; until each drone reaches the final target point. The method can be effectively applied to navigation problems such as flight path planning of platforms such as unmanned aerial vehicles and the like, and has important significance for development and construction of related industries.
Description
Technical Field
The invention belongs to the technical field of positioning navigation and control, and particularly relates to an unmanned aerial vehicle collaborative track control method based on a mixed group intelligent algorithm.
Background
With the wider and wider application of unmanned aerial vehicles in recent years, the realization of the cooperative formation flight execution task of unmanned aerial vehicles has become a hot spot of current research. In a plurality of technologies related to unmanned aerial vehicle cooperation, cooperative track planning control is one of the most critical technologies, and the unmanned aerial vehicle has the characteristics of wide interdisciplinary coverage, high algorithm innovation difficulty and the like.
The collaborative flight path planning control is essentially an optimization problem, relevant scholars at home and abroad carry out a series of researches on the collaborative flight path planning control, and the researches show that a heuristic group intelligent algorithm is an effective way for solving the collaborative flight path planning of the unmanned aerial vehicle, most typically, an ant colony algorithm, a particle swarm algorithm and the like are utilized, and compared with a traditional convex optimization algorithm, the collaborative flight path planning control has the advantages of being loose in expression requirements on target functions and constraint conditions, high in calculation timeliness, strong in robustness and the like.
The particle swarm algorithm has good advantages when being applied to various optimization problems, but the particle swarm algorithm has the problems of poor local searching capability, easy premature trapping in local optimization and the like when the global optimal value is taken as a searching target, so that the particle swarm algorithm needs to be improved. Besides the optimization algorithm parameters, the method is combined with other heuristic intelligent algorithms such as tabu search and the like, so that the inherent defects of a single algorithm can be overcome, and a good application effect can be achieved on the unmanned aerial vehicle collaborative track planning problem.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle collaborative track control method based on a hybrid group intelligent algorithm.
In order to achieve the purpose, the invention adopts the technical scheme that:
an unmanned aerial vehicle collaborative flight path control method based on a mixed group intelligent algorithm comprises the following steps:
(1) designing a collaborative track evaluation function of the unmanned aerial vehicle according to the constraint item;
(2) setting related parameters of a mixed group intelligent algorithm, initializing a particle group and a tabu table, and setting the maximum iteration times of the algorithm;
(3) starting iteration, judging the spatial position of the particle solution according to the track evaluation function, comparing to generate an individual optimal solution, judging whether to update a global optimal solution, repeating the iteration until the maximum iteration times are reached to obtain an individual extreme value and a global extreme value of the particle, and updating the particle position and speed formula;
(4) each unmanned aerial vehicle updates the state according to the particle position and speed formula and moves to the next track point to generate a new particle population;
(5) and (5) repeating the steps (3) and (4) until each unmanned aerial vehicle reaches a final target point.
Further, the constraint term in step (1) includes: the unmanned aerial vehicle comprises six elements of range, flight height, distance between the unmanned aerial vehicle and an obstacle area, turning radius, climbing angle and distance between the unmanned aerial vehicle and the obstacle area.
Further, the specific mode of the step (3) is as follows:
(301) after iteration is started, calculating an evaluation function value of each particle in the population topological structure, and comparing to obtain a current individual and a global optimal solution; continuously calculating after the next iteration to obtain an optimal solution, comparing the optimal solution with the previous iteration, and judging whether to update the individual optimal solution and the global optimal solution;
(302) setting a parameter k, carrying out additional judgment once every k iterations by the algorithm, and continuing to carry out iteration if the particle global optimal solution is updated at least once in the k iterations;
(303) if the global optimal solution of the particle is not updated in the k iterations, judging that the global optimal solution of the current particle is trapped into local optimal; in order to enable the algorithm to jump out of a local optimal loop, adding the current global optimal solution into a tabu table, so that the current global optimal solution cannot participate in next iterative search, namely cannot appear in a solution space of next iteration, and then continuing to iterate;
(304) the taboo solution, when the privilege criteria are met, is removed from the taboo table and participates in the next iteration; the privileged criteria occur in two cases:
1) the scale of the tabu table reaches the upper limit; the tabu table adopts a first-in first-out principle, and if the rule of the tabu table reaches the upper limit after the current optimal solution is tabu in a certain iteration, the solution added in the current tabu table at the earliest time is moved out of the tabu table;
2) the taboo length is 0; the tabbed solutions have different tabbed lengths λ when added to the tabbed table, the values of which are determined by the following equation:
in the formula, F is an evaluation function value corresponding to a global optimal solution which is not updated in k iterations, and G is an evaluation function value corresponding to a last global optimal solution before k iterations;
then, every time iteration is carried out, the taboo length of a solution in the taboo table is reduced by 1, and a solution with the taboo length of 0 is moved out of the taboo table and participates in the next iteration search;
(305) the individual historical optimal solution and the global historical optimal solution which are generated when the maximum iteration number is finally reached are individual extreme values
And global extremum
And substituting the speed and position updating formula of the particle swarm algorithm to obtain the position and speed of the next track point of the unmanned aerial vehicle.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method is oriented to the problem of track control in actual flight of the unmanned aerial vehicle, and overcomes the defects of difficult support of the inherent airborne computing power, low instantaneity and the like of the traditional convex optimization algorithm through the heuristic mixed group intelligent algorithm.
(2) The method effectively overcomes the defect that the particle swarm optimization is easy to fall into local optimization in practical application by combining the tabu search algorithm, so that the algorithm has wider solving range and higher efficiency.
(3) According to the method, actual element constraint conditions in the multi-unmanned aerial vehicle collaborative flight process are fully considered, and the credibility of the comprehensive evaluation function judgment algorithm is established.
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FIG. 1 is a flow chart of a method of an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the following figures and examples.
An unmanned aerial vehicle collaborative flight path control method based on a mixed group intelligent algorithm comprises the following steps:
(1) taking six elements of range, flight height, distance between the unmanned aerial vehicle and an obstacle area, turning radius, climbing angle and inter-aircraft distance as constraint items to form an unmanned aerial vehicle collaborative track evaluation function;
(2) setting relevant parameters of a mixed group intelligent algorithm: learning factor c1And c2Inertia weight factor omega, particle population size NsMaximum flight velocity v of particlesmaxSetting the initial position and speed of the unmanned aerial vehicle, initializing a particle population and a tabu table, and setting the maximum iteration times of the algorithm;
(3) and starting iteration, judging the spatial position of the particle solution according to the track evaluation function, comparing to generate an individual optimal solution, judging whether to update the global optimal solution, repeating the iteration until the maximum iteration times is reached to obtain an individual extreme value and a global extreme value of the particle, and updating the particle position and speed formula. The method comprises the following specific steps:
(301) after iteration is started, calculating an evaluation function value of each particle in the population topological structure, and comparing to obtain a current individual and a global optimal solution; continuously calculating after the next iteration to obtain an optimal solution, comparing the optimal solution with the previous iteration, and judging whether to update the individual optimal solution and the global optimal solution;
(302) setting a parameter k, carrying out additional judgment once every k iterations by the algorithm, and continuing to carry out iteration if the particle global optimal solution is updated at least once in the k iterations;
(303) and if the global optimal solution of the particle is not updated in the k iterations, judging that the global optimal solution of the current particle is trapped in the local optimal solution. In order to enable the algorithm to jump out of a local optimal loop, adding the current global optimal solution into a tabu table, and continuing iteration (the solution in the tabu table cannot participate in the next iterative search, namely cannot appear in the solution space of the next iteration);
(304) the tabbed solution, upon meeting the privilege criteria, exits the tabbed table and participates in the next iteration. The privileged criteria occur in two cases: 1) the scale of the tabu table reaches the upper limit. The tabu table adopts a first-in first-out principle, and if the rule of the tabu table reaches the upper limit after the current optimal solution is tabu in a certain iteration, the solution added in the current tabu table at the earliest time is moved out of the tabu table; 2) the length of the taboo is 0. The tabbed solutions have different tabbed lengths λ when added to the tabbed table, the values of which are determined by the following formula
In the formula, F is an evaluation function value corresponding to a global optimal solution that is not updated in k iterations, and G is an evaluation function value corresponding to a last global optimal solution before k iterations. Then, every time iteration is carried out, the taboo length of a solution in the taboo table is reduced by 1, and a solution with the taboo length of 0 is moved out of the taboo table and participates in the next iteration search;
(305) the individual historical optimal solution and the global historical optimal solution which are generated when the maximum iteration number is finally reached are individual extreme values
And global extremum
And substituting the speed and position updating formula of the particle swarm algorithm to obtain the position and speed of the next track point of the unmanned aerial vehicle.
(4) Each unmanned aerial vehicle updates the state according to the particle position and speed formula and moves to the next track point to generate a new particle population;
(5) and (5) repeating the steps (3) and (4) until each unmanned aerial vehicle reaches a final target point.
In a word, the hybrid swarm intelligent algorithm adopted by the invention combines the particle swarm algorithm and the tabu search algorithm, and has the advantages of high search speed, wide range and difficult falling into local optimum.
The method can be applied to a multi-unmanned aerial vehicle collaborative task execution scene, and based on embedded software and an airborne processing platform, the track point collaborative iteration solving capability of the algorithm is realized, and the navigation problems of the unmanned platform, such as actual track planning, are solved.
Claims (3)
1. An unmanned aerial vehicle collaborative flight path control method based on a mixed group intelligent algorithm is characterized by comprising the following steps:
(1) designing a collaborative track evaluation function of the unmanned aerial vehicle according to the constraint item;
(2) setting related parameters of a mixed group intelligent algorithm, initializing a particle group and a tabu table, and setting the maximum iteration times of the algorithm;
(3) starting iteration, judging the spatial position of the particle solution according to the track evaluation function, comparing to generate an individual optimal solution, judging whether to update a global optimal solution, repeating the iteration until the maximum iteration times are reached to obtain an individual extreme value and a global extreme value of the particle, and updating the particle position and speed formula;
(4) each unmanned aerial vehicle updates the state according to the particle position and speed formula and moves to the next track point to generate a new particle population;
(5) and (5) repeating the steps (3) and (4) until each unmanned aerial vehicle reaches a final target point.
2. The unmanned aerial vehicle cooperative track control method based on the hybrid swarm intelligence algorithm according to claim 1, wherein the constraint term in step (1) comprises: the unmanned aerial vehicle comprises six elements of range, flight height, distance between the unmanned aerial vehicle and an obstacle area, turning radius, climbing angle and distance between the unmanned aerial vehicle and the obstacle area.
3. The unmanned aerial vehicle collaborative flight path control method based on the hybrid swarm intelligent algorithm according to claim 1, wherein the specific mode of the step (3) is as follows:
(301) after iteration is started, calculating an evaluation function value of each particle in the population topological structure, and comparing to obtain a current individual and a global optimal solution; continuously calculating after the next iteration to obtain an optimal solution, comparing the optimal solution with the previous iteration, and judging whether to update the individual optimal solution and the global optimal solution;
(302) setting a parameter k, carrying out additional judgment once every k iterations by the algorithm, and continuing to carry out iteration if the particle global optimal solution is updated at least once in the k iterations;
(303) if the global optimal solution of the particle is not updated in the k iterations, judging that the global optimal solution of the current particle is trapped into local optimal; in order to enable the algorithm to jump out of a local optimal loop, adding the current global optimal solution into a tabu table, so that the current global optimal solution cannot participate in next iterative search, namely cannot appear in a solution space of next iteration, and then continuing to iterate;
(304) the taboo solution, when the privilege criteria are met, is removed from the taboo table and participates in the next iteration; the privileged criteria occur in two cases:
1) the scale of the tabu table reaches the upper limit; the tabu table adopts a first-in first-out principle, and if the rule of the tabu table reaches the upper limit after the current optimal solution is tabu in a certain iteration, the solution added in the current tabu table at the earliest time is moved out of the tabu table;
2) the taboo length is 0; the tabbed solutions have different tabbed lengths λ when added to the tabbed table, the values of which are determined by the following equation:
in the formula, F is an evaluation function value corresponding to a global optimal solution which is not updated in k iterations, and G is an evaluation function value corresponding to a last global optimal solution before k iterations;
then, every time iteration is carried out, the taboo length of a solution in the taboo table is reduced by 1, and a solution with the taboo length of 0 is moved out of the taboo table and participates in the next iteration search;
(305) the individual historical optimal solution and the global historical optimal solution which are generated when the maximum iteration number is finally reached are individual extreme values
And global extremum
And substituting the speed and position updating formula of the particle swarm algorithm to obtain the position and speed of the next track point of the unmanned aerial vehicle.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103279793A (en) * | 2013-04-25 | 2013-09-04 | 北京航空航天大学 | Task allocation method for formation of unmanned aerial vehicles in certain environment |
| CN112327923A (en) * | 2020-11-19 | 2021-02-05 | 中国地质大学（武汉） | Multi-unmanned aerial vehicle collaborative path planning method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103279793A (en) * | 2013-04-25 | 2013-09-04 | 北京航空航天大学 | Task allocation method for formation of unmanned aerial vehicles in certain environment |
| CN112327923A (en) * | 2020-11-19 | 2021-02-05 | 中国地质大学（武汉） | Multi-unmanned aerial vehicle collaborative path planning method |
Non-Patent Citations (3)
| Title |
|---|
| 李勇刚等: "结合禁忌搜索的改进粒子群优化算法", 计算机工程, vol. 38, no. 18, pages 155 - 157 * |
| 杨川力等: "基于GAPSO-TS的多基地无人机航路-时间协同规划", 火力与指挥控制, vol. 46, no. 9, pages 21 * |
| 耿健等: "基于TB-PC算法的MISO系统辨识的研究", 计算机技术及其应用, vol. 39, no. 4, pages 19 * |
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