基于多目标社会学习鸽群优化的多无人机避障控制

阮婉莹; 段海滨

doi:10.1631/FITEE.2000066

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基于多目标社会学习鸽群优化的多无人机避障控制

智能无人系统专辑 | Updated：2022-05-19

- 基于多目标社会学习鸽群优化的多无人机避障控制
- Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization
- 信息与电子工程前沿（英文） 2020年21卷第5期页码：740-748
- Affiliations：
  
  State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China
  Peng Cheng Laboratory, Shenzhen 518000, China
- Author bio：
  
  [ "Wan-ying RUAN, E-mail: wyruan@buaa.edu.cn" ]
  Hai-bin DUAN, E-mail: hbduan@buaa.edu.cn
- Funds：
  
  Project supported by the Science and Technology Innovation 2030-Key Project of "New Generation Artificial Intelligence, " China (No. 018AAA0102303), the National Natural Science Foundation of China (Nos. 91948204, 91648205, U1913602, and U19B2033), and the Aeronautical Foundation of China (No. 20185851022)
- DOI：10.1631/FITEE.2000066
  中图分类号： TP242.6;V279
- 收稿：2020-02-10，
  
  修回：2020-;4-13，
  
  纸质出版：2020-05
- Accepted：
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阮婉莹, 段海滨. 基于多目标社会学习鸽群优化的多无人机避障控制[J]. 信息与电子工程前沿（英文）, 2020,21(5):740-748.

Wan-ying RUAN, Hai-bin DUAN. Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(5): 740-748.
阮婉莹, 段海滨. 基于多目标社会学习鸽群优化的多无人机避障控制[J]. 信息与电子工程前沿（英文）, 2020,21(5):740-748. DOI： 10.1631/FITEE.2000066.

Wan-ying RUAN, Hai-bin DUAN. Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(5): 740-748. DOI： 10.1631/FITEE.2000066.

摘要

提出多目标社会学习鸽群优化（MSLPIO）方法，将其应用于无人机编队避障控制。该算法特点在于，每只鸽子在更新过程中并非向全局最优的鸽子学习，而是学习比自己占优的任何鸽子。在地图指南针算子和地标算子中引入社会学习因子。此外，为避免参数设置的盲目性，采用维数相关的参数设置方法。本文模拟了5架飞机在复杂障碍环境下的飞行过程，实验结果验证了该方法的有效性。与改进的多目标鸽群优化算法和改进的非占优排序遗传算法相比，MSLPIO具有更好的收敛性。

Abstract

We propose multi-objective social learning pigeon-inspired optimization (MSLPIO) and apply it to obstacle avoidance for unmanned aerial vehicle (UAV) formation. In the algorithm

each pigeon learns from the better pigeon but not necessarily the global best one in the update process. A social learning factor is added to the map and compass operator and the landmark operator. In addition

a dimension-dependent parameter setting method is adopted to improve the blindness of parameter setting. We simulate the flight process of five UAVs in a complex obstacle environment. Results verify the effectiveness of the proposed method. MSLPIO has better convergence performance compared with the improved multi-objective pigeon-inspired optimization and the improved non-dominated sorting genetic algorithm.

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references

J Alonso-Mora , , , E Montijano , , , M Schwager , , , 等 . . Distributed multi-robot formation control among obstacles: a geometric and optimization approach with consensus . . Proc IEEE Int Conf on Robotics and Automation , , 2016 . . p.5356 - - 5363 . . DOI: 10.1109/icra.2016.7487747 http://doi.org/10.1109/icra.2016.7487747 . .

MR Benjamin , , , M Defilippo , , , P Robinette , , , 等 . . Obstacle avoidance using multiobjective optimization and a dynamic obstacle manager . . IEEE J Ocean Eng , , 2019 . . 44 ( ( 2 ): ): 331 - - 342 . . DOI: 10.1109/JOE.2019.2896504 http://doi.org/10.1109/JOE.2019.2896504 . .

D Biro , , , T Sasaki , , , SJ Portugal . . Bringing a time-depth perspective to collective animal behaviour . . Trends Ecol Evol , , 2016 . . 31 ( ( 7 ): ): 550 - - 562 . . DOI: 10.1016/j.tree.2016.03.018 http://doi.org/10.1016/j.tree.2016.03.018 . .

R Cheng , , , YC Jin . . A social learning particle swarm optimization algorithm for scalable optimization . . Inform Sci , , 2015 . . 291 43 - - 60 . . DOI: 10.1016/j.ins.2014.08.039 http://doi.org/10.1016/j.ins.2014.08.039 . .

B Di , , , R Zhou , , , HB Duan . . Potential field based receding horizon motion planning for centrality-aware multiple UAV cooperative surveillance . . Aerosp Sci Technol , , 2015 . . 46 386 - - 397 . . DOI: 10.1016/j.ast.2015.08.006 http://doi.org/10.1016/j.ast.2015.08.006 . .

HB Duan , , , PX Qiao . . Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning . . Int J Intell Comput Cybern , , 2014 . . 7 ( ( 1 ): ): 24 - - 37 . . DOI: 10.1108/IJICC-02-2014-0005 http://doi.org/10.1108/IJICC-02-2014-0005 . .

M Faisal , , , R Hedjar , , , Sulaiman M Al , , , 等 . . Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment . . Int J Adv Robot Syst , , 2013 . . 10 ( ( 1 ): ): 37 DOI: 10.5772/54427 http://doi.org/10.5772/54427 . .

G Ferri , , , A Munafò , , , KD LePage . . An autonomous underwater vehicle data-driven control strategy for target tracking . . IEEE J Ocean Eng , , 2018 . . 43 ( ( 2 ): ): 323 - - 343 . . DOI: 10.1109/JOE.2018.2797558 http://doi.org/10.1109/JOE.2018.2797558 . .

D Floreano , , , RJ Wood . . Science, technology and the future of small autonomous drones . . Nature , , 2015 . . 521 ( ( 7553 ): ): 460 - - 466 . . DOI: 10.1038/nature14542 http://doi.org/10.1038/nature14542 . .

A Forestiero . . Bio-inspired algorithm for outliers detection . . Multimed Tools Appl , , 2017 . . 76 ( ( 24 ): ): 25659 - - 25677 . . DOI: 10.1007/s11042-017-4443-1 http://doi.org/10.1007/s11042-017-4443-1 . .

X Guo , , , JQ Lu , , , A Alsaedi , , , 等 . . Bipartite consensus for multi-agent systems with antagonistic interactions and communication delays . . Phys A , , 2018 . . 495 488 - - 497 . . DOI: 10.1016/j.physa.2017.12.078 http://doi.org/10.1016/j.physa.2017.12.078 . .

HS Ho , , , CS Do . . Multi-agent based design of autonomous UAVs for both flocking and formation flight . . J Adv Korea Navig Technol , , 2017 . . 21 ( ( 6 ): ): 521 - - 528 . . DOI: 10.1109/icra.2016.7487747 http://doi.org/10.1109/icra.2016.7487747 . .

C Kownacki , , , L Ambroziak . . Local and asymmetrical potential field approach to leader tracking problem in rigid formations of fixed-wing UAVs . . Aerosp Sci Technol , , 2017 . . 68 465 - - 474 . . DOI: 10.1016/j.ast.2017.05.040 http://doi.org/10.1016/j.ast.2017.05.040 . .

QN Luo , , , HB Duan . . Distributed UAV flocking control based on homing pigeon hierarchical strategies . . Aerosp Sci Technol , , 2017 . . 70 257 - - 264 . . DOI: 10.1016/j.ast.2017.08.010 http://doi.org/10.1016/j.ast.2017.08.010 . .

PK Mohanty , , , DR Parhi . . Path generation and obstacle avoidance of an autonomous mobile robot using intelligent hybrid controller . . Proc 3 rd Int Conf on Swarm, Evolutionary, and Memetic Computing , , 2012 . . p.240 - - 247 . . DOI: 10.1007/978-3-642-35380-2_29 http://doi.org/10.1007/978-3-642-35380-2_29 . .

EJ Molinos , , , Llamazares , , , M Ocana . . Dynamic window based approaches for avoiding obstacles in moving . . Robot Auton Syst , , 2019 . . 118 112 - - 130 . . DOI: 10.1016/j.robot.2019.05.003 http://doi.org/10.1016/j.robot.2019.05.003 . .

AK Paul , , , PC Shill . . New automatic fuzzy relational clustering algorithms using multi-objective NSGA-Ⅱ . . Inform Sci , , 2018 . . 448-449 112 - - 133 . . DOI: 10.1016/j.ins.2018.03.025 http://doi.org/10.1016/j.ins.2018.03.025 . .

HX Qiu , , , HB Duan . . Multiple UAV distributed close formation control based on in-flight leadership hierarchies of pigeon flocks . . Aerosp Sci Technol , , 2017a . . 70 471 - - 486 . . DOI: 10.1016/j.ast.2017.08.030 http://doi.org/10.1016/j.ast.2017.08.030 . .

HX Qiu , , , HB Duan . . Pigeon interaction mode switch-based UAV distributed flocking control under obstacle environments . . ISA Trans , , 2017b . . 71 93 - - 102 . . DOI: 10.1016/j.isatra.2017.06.016 http://doi.org/10.1016/j.isatra.2017.06.016 . .

HX Qiu , , , HB Duan . . A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles . . Inform Sci , , 2020 . . 509 515 - - 529 . . DOI: 10.1016/j.ins.2018.06.061 http://doi.org/10.1016/j.ins.2018.06.061 . .

F Sun , , , K Turkoglu . . Distributed real-time non-linear receding horizon control methodology for multi-agent consensus problems . . Aerosp Sci Technol , , 2017 . . 63 82 - - 90 . . DOI: 10.1016/j.ast.2016.12.018 http://doi.org/10.1016/j.ast.2016.12.018 . .

J Wang , , , IS Ahn , , , YF Lu , , , 等 . . A distributed estimation algorithm for collective behaviors in multiagent systems with applications to unicycle agents . . Int J Contr Autom Syst , , 2017 . . 15 ( ( 6 ): ): 2829 - - 2839 . . DOI: 10.1007/s12555-016-0015-9 http://doi.org/10.1007/s12555-016-0015-9 . .

ST Wu . . Flight Control System , , 2 nd Ed : : Beijing, China Beihang University Press , , 2013 . . .

MD Yan , , , X Zhu , , , XX Zhang , , , 等 . . Consensus-based three-dimensional multi-UAV formation control strategy with high precision . . Front Inform Technol Electron Eng , , 2017 . . 18 ( ( 7 ): ): 968 - - 977 . . DOI: 10.1631/FITEE.1600004 http://doi.org/10.1631/FITEE.1600004 . .

T Zhang , , , Q Li , , , CS Zhang , , , 等 . . Current trends in the development of intelligent unmanned autonomous systems . . Front Inform Technol Electron Eng , , 2017 . . 18 ( ( 1 ): ): 68 - - 85 . . DOI: 10.1631/FITEE.1601650 http://doi.org/10.1631/FITEE.1601650 . .

WW Zhao , , , HR Chu , , , MY Zhang , , , 等 . . Flocking control of fixed-wing UAVs with cooperative obstacle avoidance capability . . IEEE Access , , 2019 . . 7 17798 - - 17808 . . DOI: 10.1109/ACCESS.2019.2895643 http://doi.org/10.1109/ACCESS.2019.2895643 . .

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