Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization

doi:10.21629/JSEE.2020.01.14

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (1): 130-141.doi: 10.21629/JSEE.2020.01.14

收稿日期:2019-02-22 出版日期:2020-02-20 发布日期:2020-02-25

Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization

Zhen XU¹(), Enze ZHANG^2,*(), Qingwei CHEN¹()

¹ School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
² College of Information Engineering, Yangzhou University, Yangzhou 225009, China

Received:2019-02-22 Online:2020-02-20 Published:2020-02-25
Contact: Enze ZHANG E-mail:xz940706@163.com;yzzez8986@yzu.edu.cn;cqw6061@163.com
About author:XU Zhen was born in 1994. He is a Ph.D. candidate in School of Automation, Nanjing University of Science and Technology. His research interests include the control and path planning of multi-agent system. E-mail: xz940706@163.com|ZHANG Enze was born in 1989. She received her Ph.D. degree in Nanjing University of Science and Technology in 2017. Now, she is a lecturer in Yangzhou University. Her research interests include multi-objective optimization and particle swarm algorithm. E-mail: yzzez8986@yzu.edu.cn|CHEN Qingwei was born in 1963. He received his Ph.D. degree in control theory and control engineering from Nanjing University of Science and Technology in 2005. He is a professor in the School of Automation, Nanjing University of Science and Technology. His research interests include intelligent control and servo system. E-mail: cqw6061@163.com
Supported by:
the National Natural Science Foundation of China(61673214);the National Natural Science Foundation of China(61673217);the National Natural Science Foundation of China(61673219);the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(18KJB120011);the Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX19 0299);This work was supported by the National Natural Science Foundation of China (61673214; 61673217; 61673219), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (18KJB120011), and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX19 0299)

摘要/Abstract

Abstract:

This paper presents a path planning approach for rotary unmanned aerial vehicles (R-UAVs) in a known static rough terrain environment. This approach aims to find collision-free and feasible paths with minimum altitude, length and angle variable rate. First, a three-dimensional (3D) modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs. Considering the length, height and tuning angle of a path, the path planning of R-UAVs is described as a tri-objective optimization problem. Then, an improved multi-objective particle swarm optimization algorithm is developed. To render the algorithm more effective in dealing with this problem, a vibration function is introduced into the collided solutions to improve the algorithm efficiency. Meanwhile, the selection of the global best position is taken into account by the reference point method. Finally, the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine. Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.

Key words: unmanned aerial vehicle (UAV), path planning, multi-objective optimization, particle swarm optimization

. [J]. Journal of Systems Engineering and Electronics, 2020, 31(1): 130-141.

Zhen XU, Enze ZHANG, Qingwei CHEN. Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization[J]. Journal of Systems Engineering and Electronics, 2020, 31(1): 130-141.

图/表 12

参考文献 30

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Algorithm cost	The proposed algorithm			RD	NSGA-Ⅱ
Algorithm cost	Length	Height	Angle	RD	Length	Height	Angle
Partial data	1 075.3	6 964	3.15	0.411	1 115.5	7 086	3.28
	1 064.2	7 280	3.39	0.531	1 068.4	8 117	3.67
	1 161.6	5 438	3.46	0.471	1 180.3	6 097	3.29
	1 124.2	6 462	3.08	0.365	1 036.4	8 260	3.63
Mean time/s	3.382 7				4.076 2

Algorithm	The proposed algorithm				MOPSO			NSGA-Ⅱ
Algorithm	Length	Height	Angle	RD	Length	Height	Angle	Length	Height	Angle
Partial datum	1 197.6	64 436	4.71	0.485 7	1 413.1	61 381	4.17	1 564.5	57 353	5.342 8
	1 139.5	67 372	4.65	0.426 0	1 407.8	68 068	5.30	1 900.2	59 270	4.639 3
	1 186.2	69 504	4.53	0.468 0	1 170.9	76 417	5.16	1 221.7	65 440	3.976 9
	1 137.9	72 002	4.92	0.507 5	1 577.1	52 095	4.78	944.3	81 349	5.548 4
Mean time/s	17.092 3				19.350 2			21.335 5

Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization

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