CN111626500A - Path planning method based on improved artificial bee colony algorithm - Google Patents

Path planning method based on improved artificial bee colony algorithm Download PDF

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CN111626500A
CN111626500A CN202010452861.9A CN202010452861A CN111626500A CN 111626500 A CN111626500 A CN 111626500A CN 202010452861 A CN202010452861 A CN 202010452861A CN 111626500 A CN111626500 A CN 111626500A
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赵蕴龙
马军
陈兵
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention provides a path planning method based on an improved artificial bee colony algorithm, which has the defects of premature convergence and the like in the iterative optimization process of the artificial bee colony algorithm, and for the improvement aspect of the artificial bee colony algorithm, a new initialization strategy is adopted firstly, so that an initial population with higher quality is obtained, and the optimization iteration times are reduced; then different search equations are adopted in three stages of hiring bees, following bees and reconnaissance bees of the traditional artificial bee colony algorithm, so that the local search capability can be enhanced, and premature convergence in the later-stage optimization process can be avoided; and finally, the improved artificial bee colony algorithm is applied to the path planning problem, so that the safety and the reliability of the path can be ensured, and the shortest length of the path can be ensured.

Description

Path planning method based on improved artificial bee colony algorithm
Technical Field
The invention relates to the field of path planning, in particular to a path planning method based on an improved artificial bee colony algorithm.
Background
In real life, various optimization problems are often encountered. With the continuous development of requirements and applications, optimization algorithm theory and research are greatly developed, and besides the traditional methods such as mathematical programming and the like are used for solving the optimization problem, the application of modern optimization methods such as artificial bee colony algorithm and the like is more and more extensive.
An Artificial Bee Colony Algorithm (ABC) is a new heuristic bionic algorithm based on foraging behaviors of Bee colonies, and simulates a Colony behavior that the Bee colonies collaboratively collect honey and exchange honey source information according to different respective division work to find an optimal honey source. The bee colony comprises 3 types of worker bees including hiring bees, following bees and scout bees, wherein the hiring bees are responsible for collecting honey source information, the following bees judge nectar information shared by the hiring bees by watching the swinging dance of a performance of a companion, and decide which hiring bee to follow for honey collection, and the scout bees discover a new food source according to a random update solution method. Certainly, the artificial bee colony algorithm has the advantages of easy implementation, few control parameters and the like, and also has the defects of premature convergence and the like.
The unmanned aerial vehicle path planning comprehensively considers threats such as radar and weather, and meanwhile, the unmanned aerial vehicle has limitations such as flight path length, flight speed and steering angle, although path planning algorithms such as a fast random search tree (RRT), an artificial potential field method (APF) and an A algorithm exist at present, the algorithms are trapped in a trap area, and the planning speed is low. How to design a safe and reliable path can be finally attributed to the numerical optimization problem, and the improved artificial bee colony algorithm can be applied to solving the problems just right.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provide a path planning method based on an improved artificial bee colony algorithm, which aims to prevent the situation of local optimization in the iterative optimization process and improve the performance of the algorithm by enhancing population diversity and improving search randomness from the aspect of algorithm improvement. Compared to piecewise linear interpolation, at the node, it is conductive, with smoothness, which is an advantage. And finally, the method is applied to unmanned aerial vehicle path planning, and a safe and reliable path with the shortest length can be obtained.
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In order that the present invention may be more readily and clearly understood, reference is now made to the following detailed description of the invention taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a path initialization diagram
FIG. 2 is a flow chart of an improved artificial bee colony algorithm
FIG. 3 is a graph of the path results obtained by the present invention.
Detailed Description
To better understand the technical content of the present invention, specific embodiments are described below with reference to the drawings.
The technical scheme for realizing the purpose of the invention is as follows: a path planning method based on an improved artificial bee colony algorithm comprises the following steps:
the method comprises the following steps: modeling a two-dimensional flight environment of the unmanned aerial vehicle;
step two: performing path planning based on an improved artificial bee colony algorithm to avoid trapping in local optimum so as to obtain a global optimum solution;
step three: a spline interpolation curve smoothing path is adopted to reduce the flight risk coefficient during turning;
further, the specific process of the first step is as follows:
(1.1) As shown in FIG. 1, the number N of obstacles and the position information (x) are acquired based on the map informationi，yi) Assume that an obstacle such as a radar can be represented as a circle, and secondly, a plurality of circles in combination represent a polygonal obstacle passing through a threat center position coordinate (x)i，yi) Threat radius size riAnd threat level parameter DiTo characterize the mathematical model. The unmanned aerial vehicle equivalence is a circle, and the position of the unmanned aerial vehicle barycenter is the accurate position of the current unmanned aerial vehicle.
(1.2) assuming that the accurate positions of the starting point S, the end point T and the obstacle are known, and planning an optimal or suboptimal feasible flight path which can avoid the obstacle and has short length between the starting point S and the end point T. Assuming that the unmanned aerial vehicle starts from a starting point S, the original coordinate system xoy realizes coordinate transformation through formulas (1) and (2) to obtain a new coordinate system x ' o ' y ', after the coordinate system is converted, a straight line ST is on the same straight line with the x axis of the new coordinate system, and the y axis passes through the original point S of the new coordinate system and is identical to the straight lineLine ST perpendicular, obstacle and threat (x)i，yi) Can be expressed by formula (3), wherein the included angle between the straight line ST and the horizontal axis is theta, and the position coordinate of a certain point in the original coordinate system xoy is (x)i，yi) The position coordinates of the new coordinate system are (x ', y'), and the position coordinates of the starting point under the original coordinate system are (xs，ys) The coordinate of the end point is (x)T，yT)；
Figure BSA0000209672120000021
Figure BSA0000209672120000022
Figure BSA0000209672120000023
(1.3) after the coordinate transformation is finished, dividing ST into (D +1) small line segments, and making a vertical line L of the straight line ST at the dividing node connecting the divided small line segmentskRandomly taking a point p on all vertical lineskFinally, by vector (SP)1…PDT) represents a flight path;
further, the specific process of the second step is as follows:
(2.1) the process of the second step is as shown in fig. 2, setting parameters of the artificial bee colony algorithm, wherein the parameters of the control algorithm comprise the population size NP, the maximum iteration number maxCycle, the dimension D of the honey source, the non-update number trail of the honey source, the threshold limit, and parameters ξ and β, and generating a D NP matrix E { E ═ E-ijGenerating initial tracks of all honey sources, wherein when the two-dimensional track is generated, each track point is randomly generated in a search step range on the basis of the previous track point (the initial track point and the target track point are determined), the position of each honey source, namely the track point, is generated by the formula (4), and if the generated track has track points exceeding a planning area, the honey source is generated again;
xij＝xi(j-1)+(rand-0.5)·step (4)
wherein i belongs to {1, 2, …, NP }, and represents the number of honey sources; j belongs to {1, 2, …, D }, represents individual dimension, step is search step length, rand (-) is a random function, and the value is in the interval [0, 1 ];
(2.2) calculating the Path cost f by equation (5)i(path quality);
fi＝ω1·Length+ω2·Threat (5)
wherein ω is1+ω2Length denotes the total path Length and thread denotes the Threat cost.
And (2.3) calculating the fitness values of all the initial honey sources according to the formula (6).
Figure BSA0000209672120000024
(2.4) the employed bees correspond to all the honey sources one by one, the employed bee stage is added with the global optimal honey source information obtained in the last iteration, and a new honey source is generated according to the formula (7) and the neighborhood search is completed;
Figure BSA0000209672120000031
(2.5) calculating the fit of the New Honey SourceiAnd evaluating it if the fit of the new honey sourceiReplacing the initial honey source with the new honey source if the initial honey source is superior to the initial honey source, and otherwise, adding 1 to the trail value of the current honey source and the initial honey source;
(2.6) after all the employed bees complete the search, the follower bees calculate the probability of selection for each honey source according to equation (8);
Figure BSA0000209672120000032
(2.7) entering a bee following stage, randomly selecting a dimension jrand of the honey source if rand is less than piThen, all dimensions of the current honey source are traversed, if the current dimension j is jrand or rand < β, the rand is continuously compared with the current dimension j, and if rand is <, a new honey source is generated by adopting an equation (9);
Figure BSA0000209672120000033
(2.8) if rand < xi, generating a new honey source by adopting the formula (10);
Figure BSA0000209672120000034
(2.9) if the production conditions do not belong to the two cases of (2.7) and (2.8), generating a new honey source by adopting the formula (11);
Figure BSA0000209672120000035
(2.10) calculating the fit of all new honey sourcesiAnd evaluating it if the fit of the new honey sourceiReplacing the initial honey source with the new honey source if the initial honey source is superior to the initial honey source, and otherwise, adding 1 to the trail value of the current honey source and the initial honey source;
(2.11) after all the follower bees finish the searching process, the algorithm enters a scout bee stage, firstly, the trail values of all the bee sources are sorted from large to small, and the following three conditions are compared with limit;
(2.12) Honey source trail if the trail value is maximummaxIf trail is providedmaxIf the value is more than limit, generating a new honey source through the formula (12);
Figure BSA0000209672120000036
(2.13) then compare the honey source trail with the second largest trail valuesecondIf trail is providedsecondIf the content is less than or equal to limit, generating a new honey source by adopting the formula (13);
Figure BSA0000209672120000037
(2.14) then comparing the trail values with the third honey source trailthirdIf trail is providedthirdIf the content is less than or equal to limit, generating a new honey source by adopting the formula (14);
Figure BSA0000209672120000038
(2.15) recording the optimal solution so far;
(2.16) if the iteration number reaches maxCycle, terminating the algorithm, outputting the optimal honey source, and otherwise, returning to (2.4) to continue the loop circulation;
further, the specific process of the third step is as follows:
(3.1) carrying out inverse coordinate transformation to obtain an optimal path for connecting all obstacles which are bypassed from the starting point S to the end point T;
and (3.2) adopting a cubic spline interpolation curve smooth path, establishing a simple and continuous analytical model for the physical quantity (unknown quantity) by cubic spline interpolation according to the known observation point, and estimating the characteristic at the non-observation point according to the model. B-spline is conducive to having smoothness at the node, which is an advantage over piecewise linear interpolation. The purpose of applying the cubic B-spline interpolation curve to the smooth path is to reduce the risk coefficient in the turning process of the unmanned aerial vehicle, so that a safe and reliable path is obtained as shown in FIG. 3;
the control point is pi(i-0, 1, …, n) with a p-th order B-spline curve equation of
Figure BSA0000209672120000041
Ni，p(u)PiIs a p-th order B-spline basis function, u ═ u0，u1，…，um]Is a node vector.

Claims (4)

1. A path planning method based on an improved artificial bee colony algorithm is characterized in that the specific process of the method comprises the following steps:
the method comprises the following steps: modeling a two-dimensional flight environment of the unmanned aerial vehicle;
step two: performing path planning based on an improved artificial bee colony algorithm to avoid trapping in local optimum so as to obtain a global optimum solution;
step three: and smoothing the path by adopting a spline interpolation curve to reduce the flight risk coefficient during turning.
2. The method for path planning based on the improved artificial bee colony algorithm according to claim 1, wherein: in the first step, the number N and the position information (x) of the obstacles are obtained according to the map informationi，yi) And modeling the two-dimensional flight environment of the unmanned aerial vehicle. The specific process is as follows:
1) as shown in fig. 1, the number N of obstacles and the position information (x) are acquired from the map informationi，yi) Assume that an obstacle such as a radar can be represented as a circle, and secondly, a plurality of circles in combination represent a polygonal obstacle passing through a threat center position coordinate (x)i，yi) Threat radius size riAnd threat level parameter DiTo characterize the mathematical model. The unmanned aerial vehicle equivalence is a circle, and the position of the unmanned aerial vehicle barycenter is the accurate position of the current unmanned aerial vehicle. (ii) a
2) And planning an optimal or suboptimal feasible flight path which can avoid the obstacle and has short length between the starting point S and the end point T on the assumption that the accurate positions of the starting point S, the end point T and the obstacle are known. Assuming that the unmanned aerial vehicle starts from a starting point S, the original coordinate system xoy realizes coordinate transformation through formulas (1) and (2) to obtain a new coordinate system x ' o ' y ', after the coordinate system is converted, a straight line ST is on the same straight line with the x axis of the new coordinate system, the y axis passes through the original point S of the new coordinate system and is vertical to the straight line ST, and obstacles and threats (x is x)i，yi) Can be expressed by formula (3), wherein the included angle between the straight line ST and the horizontal axis is theta, and the position coordinate of a certain point in the original coordinate system xoy is (x)i，yi) The position coordinates of the new coordinate system are (x ', y'), and the position coordinates of the starting point under the original coordinate system are (xs，ys) The coordinate of the end point is (x)T，yT)；
Figure FSA0000209672110000011
Figure FSA0000209672110000012
Figure FSA0000209672110000013
3) After coordinate transformation is finished, the small line segments are equally divided into (D +1) segments, and then the perpendicular line L of the straight line ST is made at the equal dividing nodes connecting the equal dividing small line segmentskRandomly taking a point p on all vertical lineskFinally pass vector (S P)1…PDT) represents the track.
3. The method for path planning based on the improved artificial bee colony algorithm according to claim 1, wherein: in the second step, path planning is carried out based on an improved artificial bee colony algorithm, and the situation that local optimization is involved to obtain a global optimal solution is avoided; the specific process is as follows:
1) from the parameters of setting the artificial bee colony algorithm, the parameters of the control algorithm comprise the population size NP, the maximum iteration number maxCycle, the dimension D of the honey source, the non-updating number trail of the honey source, the threshold limit, the parameters ξ and β, and the parameters are obtained by generating a D x NP matrix E { E { (E) }ijGenerating initial tracks of all honey sources, wherein when the two-dimensional track is generated, each track point is randomly generated in a search step range on the basis of the previous track point (the initial track point and the target track point are determined), the position of each honey source, namely the track point, is generated by the formula (4), and if the generated track has track points exceeding a planning area, the honey source is generated again;
xij＝xi(j-1)+(rand-0.5)·step (4)
wherein i belongs to {1, 2, …, NP }, and represents the number of honey sources; j ∈ {1, 2, …, D }, representing individual dimensions,
step is the search step length, rand (-) is a random function, and the value is between the interval [0, 1 ];
2) calculating the path cost f by equation (5)i(path quality);
fi＝ω1·Length+ω2·Threat (5)
wherein ω is1+ω2Length denotes the total path Length and thread denotes the Threat cost.
3) Fitness values of all the initial honey sources are calculated according to formula (6).
Figure FSA0000209672110000021
4) The employment bees correspond to all honey sources one by one, the global optimal honey source information obtained in the last iteration is added in the stage of the employment bees, and new honey sources are generated according to the formula (7) and neighborhood searching is completed;
Figure FSA0000209672110000022
5) calculating the fit of the new honey sourceiAnd evaluating it if the fit of the new honey sourceiReplacing the initial honey source with the new honey source if the initial honey source is superior to the initial honey source, and otherwise, adding 1 to the trail value of the current honey source and the initial honey source;
6) after all the employed bees complete the search, the follower bees calculate the selection probability of each honey source according to the formula (8);
Figure FSA0000209672110000023
7) then, entering a bee following stage, randomly selecting a dimension jrand of the bee source if rand is less than piThen, all dimensions of the current honey source are traversed, if the current dimension j is jrand or rand < β, the rand is continuously compared with the current dimension j, and if rand is <, a new honey source is generated by adopting an equation (9);
Figure FSA0000209672110000024
8) if rand < xi, then using formula (10) to generate new honey source;
Figure FSA0000209672110000025
9) if the generation condition does not belong to the two cases of (7) and (8), generating a new honey source by adopting the formula (11);
Figure FSA0000209672110000026
10) calculating the fit of all new honey sourcesiAnd evaluating it if the fit of the new honey sourceiReplacing the initial honey source with the new honey source if the initial honey source is superior to the initial honey source, and otherwise, adding 1 to the trail value of the current honey source and the initial honey source;
11) after all the follower bees finish the searching process, the algorithm enters a scout bee stage, the trail values of all the bee sources are firstly sorted from large to small, and the following three conditions are compared with limit;
12) honey source trail if the trail value is maximalmaxIf trail is providedmaxIf the value is more than limit, generating a new honey source through the formula (12);
Figure FSA0000209672110000027
13) then if the trail values are then compared the second largest honey source trailsecondIf trail is providedsecondIf the content is less than or equal to limit, generating a new honey source by adopting the formula (13);
Figure FSA0000209672110000028
14) then comparing the trail values with the third honey source trailthirdIf trail is providedthirdIf the content is less than or equal to limit, generating a new honey source by adopting the formula (14);
Figure FSA0000209672110000031
15) recording the optimal solution so far;
16) if the iteration number reaches maxCycle, the algorithm is terminated, the optimal honey source is output, and if not, the loop is continued to (4).
4. The method for path planning based on the improved artificial bee colony algorithm according to claim 1, wherein: a spline interpolation curve smooth path is adopted in the third step so as to reduce the flight risk coefficient during turning; the specific process is as follows:
1) performing inverse coordinate transformation to obtain an optimal path for connecting all barriers which are bypassed from the starting point S to the end point T;
2) a cubic spline interpolation curve smoothing path is adopted, cubic spline interpolation is carried out by using known observation points to establish a simple and continuous analytical model for physical quantity (unknown quantity), and the characteristic of the non-observation point is presumed according to the model. B-spline is conducive to having smoothness at the node, which is an advantage over piecewise linear interpolation. The purpose of applying the cubic B-spline interpolation curve to the smooth path is to reduce the risk coefficient in the turning process of the unmanned aerial vehicle, so that a safe and reliable path is obtained;
the control point is pi(i-0, 1, …, n) with a p-th order B-spline curve equation of
Figure FSA0000209672110000032
Ni，p(u)PiIs a p-th order B-spline basis function, u ═ u0，u1，…，um]Is a node vector.
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CN112484732A (en) * 2020-11-30 2021-03-12 北京工商大学 IB-ABC algorithm-based unmanned aerial vehicle flight path planning method
CN112965527A (en) * 2021-02-16 2021-06-15 北京信息科技大学 Unmanned aerial vehicle formation topology generation optimization method based on improved artificial bee colony algorithm
CN113365282A (en) * 2021-06-22 2021-09-07 成都信息工程大学 WSN obstacle area covering deployment method adopting artificial bee colony algorithm of problem features
CN113478489A (en) * 2021-07-29 2021-10-08 桂林电子科技大学 Mechanical arm trajectory planning method
CN114553302A (en) * 2022-02-25 2022-05-27 中国电子科技集团公司第三十八研究所 Real-time cooperative communication method for unmanned aerial vehicle swarm
DE202023103072U1 (en) 2023-06-05 2023-06-20 Fuad Alhosban An optimized path rating system to select a food source based on the mean artificial bee colony
CN117146833A (en) * 2023-10-31 2023-12-01 北京卓翼智能科技有限公司 Unmanned aerial vehicle path planning method and device based on improved bat algorithm

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CN110889625A (en) * 2019-11-25 2020-03-17 航天时代飞鸿技术有限公司 Task planning method for swarm unmanned aerial vehicle system

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CN110569957A (en) * 2019-09-03 2019-12-13 华侨大学 Optimization method based on artificial bee colony algorithm
CN110889625A (en) * 2019-11-25 2020-03-17 航天时代飞鸿技术有限公司 Task planning method for swarm unmanned aerial vehicle system

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112484732A (en) * 2020-11-30 2021-03-12 北京工商大学 IB-ABC algorithm-based unmanned aerial vehicle flight path planning method
CN112965527A (en) * 2021-02-16 2021-06-15 北京信息科技大学 Unmanned aerial vehicle formation topology generation optimization method based on improved artificial bee colony algorithm
CN112965527B (en) * 2021-02-16 2023-06-16 北京信息科技大学 Unmanned aerial vehicle formation topology generation optimization method based on improved artificial bee colony algorithm
CN113365282A (en) * 2021-06-22 2021-09-07 成都信息工程大学 WSN obstacle area covering deployment method adopting artificial bee colony algorithm of problem features
CN113478489A (en) * 2021-07-29 2021-10-08 桂林电子科技大学 Mechanical arm trajectory planning method
CN113478489B (en) * 2021-07-29 2022-05-10 桂林电子科技大学 Mechanical arm track planning method
CN114553302A (en) * 2022-02-25 2022-05-27 中国电子科技集团公司第三十八研究所 Real-time cooperative communication method for unmanned aerial vehicle swarm
CN114553302B (en) * 2022-02-25 2023-05-16 中国电子科技集团公司第三十八研究所 Unmanned plane bee colony real-time collaborative communication method
DE202023103072U1 (en) 2023-06-05 2023-06-20 Fuad Alhosban An optimized path rating system to select a food source based on the mean artificial bee colony
CN117146833A (en) * 2023-10-31 2023-12-01 北京卓翼智能科技有限公司 Unmanned aerial vehicle path planning method and device based on improved bat algorithm
CN117146833B (en) * 2023-10-31 2024-01-05 北京卓翼智能科技有限公司 Unmanned aerial vehicle path planning method and device based on improved bat algorithm

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Application publication date: 20200904