Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (12): 3029-3038.doi: 10.13229/j.cnki.jdxbgxb20210403

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Improved algorithm of UAV search based on electric field model and simulation analysis

Hang ZHU(),Han-bo YU,Jia-hui LIANG,Hong-ze LI   

  1. College of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
  • Received:2021-04-25 Online:2022-12-01 Published:2022-12-08

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Abstract:

An improved ant colony algorithm based on physical electric field model was proposed in order to solve the problem of a single UAV visual navigation for ground moving target search. The complex search area was defined， the grid was divided， and the grid node was defined as an activable pheromone point. The ant colony algorithm was optimized based on the potential field rules of the physical electric field model， and the probability model was introduced. An improved particle swarm optimization algorithm for UAV search based on electric field model was established to control the pose and speed of UAV. The simulation results show that the average success rate of the improved ant colony algorithm for searching moving targets is 67.3%， and the average operation time of the example is 8.33 s. The simulation results show that the improved ant colony algorithm has higher success rate and less time-consuming than the particle swarm optimization algorithm.

Key words: automatic control technology, unmanned aerial vehicle （UAV） search, physical electric field model, ant colony algorithm, grid method

CLC Number: 

  • V249

Fig.1

Pheromone point diagram"

Fig.2

Area where the drone will go next"

Fig.3

Algorithm flow chart"

Fig.4

Robot will walk in a straight line on the way to escape"

Fig.5

Robot won't turn back on its way to escape"

Fig.6

Robot will turn back on its way to escape"

Fig.7

Comparison of algorithm success times"

Table 1

Comparison of average time of algorithm"

机器人行走方式粒子群算法时间/s优化算法时间/s

直线

不折返

折返

15.25

21.5

22.5

5.5

9.0

10.5

Fig.8

Comparison of search area with time in three cases"

1 Goerzen C, Kong Z, Mettler B. A survey of motion planning algorithms from the perspective of autonomous UAV guidance[J]. Journal of Intelligent and Robotic Systems, 2010, 57(11): 65-100.
2 Huang Li-wei, Hong Qu, Peng Ji, et al. A novel coordinated path planning method using k-degree smoothing for multi-UAVs[J]. Applied Soft Computing, 2016, 48(11): 182-192.
3 Duan Hai-bin, Zhang Xiang-yin, Wu Jiang, et al. Max-min adaptive ant colony optimization approach to multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments[J]. Journal of Bionic Engineering, 2009, 6(2): 161-173.
4 Dong Shi-you, Zhu Xiao-ping, Long Guo-qing. Cooperative planning method for swarm UAVs based on hierarchical strategy[C]∥3rd International Conference on System Science, Engineering Design and Manufacturing Informatization, Pacific Grove, USA, 2012: 304-307.
5 Liu Yang, Zhan Xue-jun, Guan Xiang-min, et al. Potential odor intensity grid based UAV path planning algorithm with particle swarm optimization approach[J]. Mathematical Problems in Engineering, 2016, 21(2): 1-16.
6 Moon S W, Oh E, David H S. An integral framework of task assignment and path planning for multiple unmanned aerial vehicles in dynamic environments[J]. Journal of Intelligent & Robotic Systems, 2013, 70(1-4): 303-313.
7 Mehrez B, Ahmed B. A novel UAV path planning algorithm to search for floating objects on the ocean surface based on object's trajectory prediction by regression[J]. Robotics and Autonomous Systems, 2021, 135(1): 1-18.
8 Cheng Ze, Wang Er-gan, Tang Yi-xin, et al. Real-time path planning strategy for UAV based on improved particle swarm optimization[J]. Journal of Computers, 2014, 9(1): 209-214.
9 Shin J J, Bang H, Morlier J. UAV path planning under dynamic threats using an improved PSO algorithm[J]. International Journal of Aerospace Engineering, 2020, 13(6): 1-17.
10 Liu Yang, Zhan Xue-jun, Guan Xiang-min, et al. Potential odor intensity grid based UAV path planning algorithm with particle swarm optimization approach[J]. Mathematical Problems in Engineering, 2016, 21(9): 1-16.
11 Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents[J]. IEEE Transactions on Cybernetics, 1996, 26(1): 29-41.
12 Perez-Carabaza S, Besada-Portas E, Lopez-Orozco J A, et al. Ant colony optimization for multi-UAV minimum time search in uncertain domains[J]. Applied Soft Computing, 2018, 62(9): 789-806.
13 Zhen Zi-yang, Yan Chen, Wen Liang-dong, et al. An intelligent cooperative mission planning scheme of UAV swarm in uncertain dynamic environment[J]. Aerospace Science and Technology, 2020, 100(1): 1-16.
14 Zhang Lin, Zhu Yi-an, Shi Xian-chen. A hierarchical decision-making method with a fuzzy ant colony algorithm for mission planning of multiple UAVs[J]. Information, 2020, 11(4): No.226.
15 Michael Faraday. Experimental Researches in Electricity[M]. London: Dover Publications, 2004.
16 朱磊,樊继壮,赵杰, 等. 基于栅格法的矿难搜索机器人全局路径规划与局部避障[J]. 中南大学学报:自然科学版, 2011, 42(11): 3421-3428.
Zhu Lei, Fan Ji-zhuang, Zhao Jie, et al. Global path planning and local obstacle avoidance of searching robot in mine disasters based on grid method[J]. Journal of Central South University (Science and Technology), 2011, 42(11): 3421-3428.
17 Marco D, Gianni D C, Luca M G. Ant algorithms for discrete optimization[J]. Artificial Life, 1999, 5(2): 137-172.
18 车翔玖,张孙旻. 基于异步更新策略的蚁群边缘提取算法[J]. 吉林大学学报:工学版, 2017, 47(5): 1577-1582.
Che Xiang-jiu, Zhang Sun-min. Edge extraction method based on ant colony asynchronous update strategy[J]. Journal of Jilin University (Engineering and Technology Edition), 2017, 47(5): 1577-1582.
19 Yu B, Yang Z Z, Yao B Z. An improved ant colony optimization for vehicle routing problem[J]. Euro-pean Journal of Operational Research, 2009, 196(1): 171-176.
20 Sande H V, Henrotte F, Hameyer K. The Newton-Raphson method for solving non-linear and anisotropic time-harmonic problems[J]. Compel International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2004, 23(4): 950-958.
21 Chen Yong-bo, Luo Guan-chen, Mei Yue-song, et al. UAV path planning using artificial potential field method updated by optimal control theory[J]. International Journal of Systems Science, 2016, 47(5-8): 1407-1420.
22 Mahamed O, Salah A S. Improved continuous ant colony optimization algorithms for real-world engineering optimization problems[J]. Engineering Applications of Artificial Intelligence, 2019, 85(5): 818-829.
23 Trivedi R R, Pawaskar D N, Shimpi R P. Enhancement of travel range of electrostatically driven fixed-fixed microbeam in static and dynamic mode using mutated particle swarm optimization[J]. Structural and Multidisciplinary Optimization, 2021, 63(6): 2989-3005.
24 Chao Luo, Jiang Zhi-peng, Zheng Yuan-jie. A novel reconstructed training-set SVM with roulette cooperative coevolution for financial time series classification[J]. Expert Systems with Applications, 2019, 123(1):283-298.
25 Mo Pin-qiang, Ma Dan-yang, Zhu Qi-yin. Interpretation of heating and cooling data from thermal cone penetration test using a 1D numerical model and a PSO algorithm[J]. Computers and Geotechnics, 2021, 130(2): 236-248.
26 Cao Guang-hui, Wang Xing-yuan. Image encryption based on the combination of roulette wheel selection with linear congruence pixel transformation[J]. Multimedia Tools and Applications, 2019, 78(8): 10625-10647.
27 Qian Wu-wen, Chai Jun-rui, Xu Zeng-guang, et al. Differential evolution algorithm with multiple mutation strategies based on roulette wheel selection[J]. Applied Intelligence, 2018, 48(10): 3612-3629.
28 Kennedy J, Eberhart R C. Particle swarm optimization[C]∥IEEE International Conference on Neural Networks, Orlando, USA, 1997: 1942-1948.
29 Kennedy J, Eberhart R C. A discrete binary version of the particle swarm algorithm[C]∥IEEE International Conference on Computational Cybernetics and Simulation, Piscataway, USA, 1997: 4104-4109.
30 Shorakaei H, Vahdani M, Imani B, et al. Optimal cooperative path planning of unmanned aerial vehicle by a parallel genetic algorithm[J]. Robotic, 2016, 34 (4): 823-836.
31 Richter C A, Bry A, Roy N. Polynomial trajectory planning for aggressive quadrotor flight in dense in door environments[C]∥Robotics Research, Berlin, Germany, 2016: 649-666.
32 Wang Bai-lin, Li Tie-ke, Wang Hai-feng, et al. Partheno-genetic algorithm for the permutation flow-shop scheduling problem with maximum waiting times[J]. Applied Mechanics and Materials, 2014, 65(5): 631-632.
33 Liu L Y, Robin P G. A geometry-independent fine-mesh-based monte carlo importance generator[J]. Nuclear Science and Engineering, 1997, 125(2): 188-195.
34 Chu Yan-li, Yao Fan, He Yu-yao, et al. An improved multi-node newton iteration search method based on PSO[J]. OPTIK, 2021, 232(5): 169-177.
35 Cristian D C, Carlos V S, Jesus A L, et al. A cascade hybrid PSO feed-forward neural network model of a biomass gasification plant for covering the energy demand in an AC microgrid[J]. Energy Conversion and Management, 2021, 232(2): 322-338.
36 Chakraborty T, Banik S K, Bhadra A K, et al. Dynamically learned PSO based neighborhood influenced fuzzy c-means for pre-treatment and post-treatment organ segmentation from CT images[J]. Computer Methods and Programs in Biomedicine, 2021, 202(2): 196-204.
37 Song Bao-ye, Wang Zi-dong, Zou Lei. An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve[J]. Applied Soft Computing, 2021, 100(1): 162-174.
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