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

• SYSTEMS ENGINEERING • Previous Articles Next Articles

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)

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

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.

Figures/Tables 12

Fig 1

Fig 2

Fig 3

Fig 4

Fig 5

Fig 6

Table 1

Fig 7

Fig 8

Fig 9

Table 2

Fig 10

References 30

1	ZHOU M, JIANG L, LIANG S. A UAV patrol system based on bluetooth localization. Proc. of the 2nd Asia-Pacific Conference on Intelligent Robot Systems, 2017: 205-209.
2	TELFORD R, GALLOWAY S. Fault classification and diagnostic system for unmanned aerial vehicle electrical networks based on hidden Markov model. Electrical Systems in Transportation IET, 2015, 5 (3): 103- 111. doi: 10.1049/iet-est.2014.0042
3	MAO Q, ZHANG D. Strategy of control and avoidance of UAVs based on neighborhood tracking and identification. Systems Engineering and Electronics, 2018, 40 (9): 2071- 2078.
4	KAKOOEI M, BALEGHI Y. Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment. International Journal of Remote Sensing, 2017, 38 (8): 2511- 2534.
5	GU J J, SU T, WANG Q H. Multiple moving targets surveillance based on a cooperative network for multi-UAV. IEEE Communications Magazine, 2018, 56 (4): 82- 89. doi: 10.1109/MCOM.2018.1700422
6	PEHLIVANOGLU Y V. A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV. Aerospace Science and Technology, 2012, 16 (1): 47- 55. doi: 10.1016/j.ast.2011.02.006
7	GOERZEN C, KONG Z, METTLER B. A survey of motion planning algorithms from the perspective of autonomous UAV guidance. Journal of Intelligent & Robotic Systems, 2010, 57 (1/4): 65- 100.
8	SUN T Y, HUO C L, TSAI S J. Optimal UAV flight path planning using skeletonization and particle swarm optimizer. Proc. of the IEEE Congress on Evolutionary Computation, 2008: 1183-1188.
9	CHOIU R C H, KAUFMAN A E, LIANG Z R. An interactive fly-path planning using potential fields and cell decomposition for virtual endoscopy. IEEE Trans. on Nuclear Science, 2002, 46(4): 1045-1049.
10	LUO D L, WU S X. Ant colony optimization with potential field heuristic for robot path planning. Systems Engineering and Electronics, 2010, 32 (6): 1277- 1280.
11	MASEHIAN E, SEDIGHIZADEH D. Classic and heuristic approaches in robot motion planning——a chronological review. Proc. of World Academy of Science Engineering & Technology, 2007: 101-106.
12	WANG X F, CHAI J, ZHOU J. Cooperative path planning of multi-UAV based on multi-objective optimization algorithm. Systems Engineering and Electronics, 2017, 39 (4): 782- 787.
13	DUAN H B, PEI L, SHI Y H. Interactive learning environment for bio-inspired optimization algorithms for UAV path planning. IEEE Trans. on Education, 2015, 58(4): 276-281.
14	WANG Q, ZHANG A, QI L. Three-dimensional path planning for UAV based on improved PSO algorithm. Proc. of the China Control and Decision Conference, 2014: 3981-3985.
15	LI M, SONG Q, ZHAO Q J. UAV path re-planning based on improved bidirectional RRT algorithm in dynamic environment. Proc. of the 3rd International Conference on Control, Automation and Robotics, 2017: 658-661.
16	Wang B F, LI S, GUO J. Car-like mobile robot path planning in rough terrain using multi-objective particle swarm optimization algorithm. Neurocomputing, 2017, 282, 42- 51.
17	COELLO C A C, LECHUGA M S. MOPSO: a proposal for multiple objective particle swarm optimization. Proc. of the IEEE Congress on Evolutionary Computation, 2002, 2: 1051-1056.
18	HUANG C, FEI J Y. UAV path planning based on particle swarm optimization with global best path competition. International Journal of Pattern Recognition and Artificial Intelligence, 2018, 32 (06): 132- 140.
19	GENG N, MENG Q. How good are distributed allocation algorithms for solving urban search and rescue problems? A comparative study with centralized algorithms. IEEE Trans. on Automation Science and Engineering, 2018, 16(1): 478-485.
20	GENG N, GONG D W. Robot path planning in an environment with many terrains based on interval multi-objective PSO. Proc. of the IEEE Congress on Evolutionary Computation, 2013: 813-820.
21	GENG N, GONG D W, ZHANG Y. PSO-based robot path planning for multi survivor rescue in limited survival time. Mathematical Problems in Engineering, 2014, 187370.
22	GENG N, SUN X J, GONG D W. Solving robot path planning in an environment with terrains based on interval multi-objective PSO. International Journal of Robotics & Automation, 2016, 31 (2): 100- 110.
23	EBERHART R, KENNEDY J. A new optimizer using particle swarm theory. Proc. of the 6th International Symposium on Micro Machine and Human Science, 1995: 39-43.
24	CLERC M. Particle swarm optimization. New York: Springer International Publishing, 2016.
25	MASEHIAN E, SEDIGHIZADEH D. A multi-objective PSO-based algorithm for robot path planning. Proc. of the IEEE International Conference on Industrial Technology, 2010: 465-470.
26	SHI Z, CHEN Q W, HU W L. Convergence analysis of a class of multi-objective quantum-behaved particle swarm optimization algorithms and its application. Information & Control, 2013, 42 (4): 407- 415.
27	ZHANG E Z, CHEN Q W. Improved r-dominance-based particle swarm optimization for multi-objective optimization. Control Theory & Applications, 2015, 32 (5): 623- 630.
28	DEB K, PRATAP A, AGARWAL S. A fast and elitist multi-objective genetic algorithm: NSGA-Ⅱ. IEEE Trans. on Evolutionary Computation, 2002, 6(2): 182-197.
29	CONESA M J, RIBEIRO A, ANDUJAR D. Multi-path planning based on a NSGA-Ⅱ for a fleet of robots to work on agricultural tasks. Proc. of the IEEE Congress on Evolutionary Computation, 2012: 1-8.
30	HUSSIAN T I, KHAN M K, INDIRA N. Evolutionary path planning for industrial robot using intelligent technique. Artificial Intelligent Systems & Machine Learning, 2011, 3 (9): 579- 587.

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

[1]	Jun CHEN, Xudong GAO, Jia RONG, Xiaoguang GAO. A situation awareness assessment method based on fuzzy cognitive maps [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1108-1122.
[2]	Bing WANG, Pengfei ZHANG, Yufeng HE, Xiaozhi WANG, Xianxia ZHANG. Scenario-oriented hybrid particle swarm optimization algorithm for robust economic dispatch of power system with wind power [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1143-1150.
[3]	Honghong ZHANG, Xusheng GAN, Shuangfeng LI, Zhiyuan CHEN. UAV safe route planning based on PSO-BAS algorithm [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1151-1160.
[4]	Yangjun GAO, Guangyun LI, Zhiwei LYU, Lundong ZHANG, Zhongpan LI. Improved adaptively robust estimation algorithm for GNSS spoofer considering continuous observation error [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1237-1248.
[5]	Lanyong ZHANG, Ruixuan ZHANG. Research on UAV cloud control system based on ant colony algorithm [J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 805-811.
[6]	Yuan ZENG, Wenbin LU, Bo YU, Shifei TAO, Haosu ZHOU, Yu CHEN. Improved IMM algorithm based on support vector regression for UAV tracking [J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 867-876.
[7]	Jianwei SUN, Chao WANG, Qingzhan SHI, Wenbo REN, Zekun YAO, Naichang YUAN. Intelligent optimization methods of phase-modulation waveform [J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 916-923.
[8]	Gang LIU, Zhibiao AN, Songyang LAO, Wu LI. Firepower distribution method of anti-ship missile based on coupled path planning [J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 1010-1024.
[9]	Dongju CAO, Wendong YANG, Hui CHEN, Yang WU, Xuanxuan TANG. Energy efficiency maximization for buffer-aided multi-UAV relaying communications [J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 312-321.
[10]	Yangyang JIANG, Yan GAO, Wenqi SONG, Yue LI, Quan QUAN. Bibliometric analysis of UAV swarms [J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 406-425.
[11]	Zihang DING, Junwei XIE, Zhengjie LI. Adaptive transmit beamspace optimization design based on RD-log-FDA radar [J]. Journal of Systems Engineering and Electronics, 2022, 33(1): 91-96.
[12]	Alireza MOHSENI, Vincent DUCHAINE, Tony WONG. Experimental study of path planning problem using EMCOA for a holonomic mobile robot [J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1450-1462.
[13]	Jinqiang HU, Husheng WU, Renjun ZHAN, Rafik MENASSEL, Xuanwu ZHOU. Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior [J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1463-1476.
[14]	Jiaxin HU, Leping YANG, Huan HUANG, Yanwei ZHU. Optimal reconfiguration of constellation using adaptive innovation driven multiobjective evolutionary algorithm [J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1527-1538.
[15]	Ziquan YU, Youmin ZHANG, Bin JIANG. PID-type fault-tolerant prescribed performance control of fixed-wing UAV [J]. Journal of Systems Engineering and Electronics, 2021, 32(5): 1053-1061.

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

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 12

References 30

Related Articles 15

Recommended Articles

Metrics

Comments