[1]WU Yingying,DING Zhaohong,LIU Huaping,et al.Self-organizing target search algorithm of multi-agent system for envi-ronment detection[J].CAAI Transactions on Intelligent Systems,2020,15(2):289-295.[doi:10.11992/tis.201908023]
Copy

Self-organizing target search algorithm of multi-agent system for envi-ronment detection

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 2 Page number: 289-295 Column: 学术论文—智能系统 Public date: 2020-03-05
Title:
Self-organizing target search algorithm of multi-agent system for envi-ronment detection
Author(s):
WU Yingying1;  DING Zhaohong1;  LIU Huaping2; 3;  ZHAO Huailin1;  SUN Fuchun2; 3
1. School of Electrical and Electronics Engineering, Shanghai Institute of Technology, Shanghai 201418, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
3. State Key Laboratory of Intelligent Techn
Keywords:
self-organizing algorithm; target search; differential evolution algorithm; multi-agent bionic algorithm; unmanned aerial vehicle; unstructured environment; flocking; dynamic target
CLC:
TP242.6
DOI:
10.11992/tis.201908023
Abstract:
In this study, the framework of self-organizing target search algorithm was developed to coordinate unmanned aerial vehicle (UAV) swarm to find static and dynamic targets in a complex unstructured environment. First, the UAVs distributed target search model was developed from the biologically-inspired mechanisms called flocking and stigmergy, which incorporated the magnetic detector’s equivalent average width feature. Secondly, an improved differential evolution algorithm, which introduced adaptive operators, was proposed for the balancing of exploration and exploitation in the multi-UAV collaborative search system and realizing optimization of UAV collaborative search. This self-organizing target search algorithm aims at optimizing the number of tracking targets in the shortest possible time. The target search strategy tested on the simulation framework validates the algorithm’s adaptability for uncertain spatial targets in unstructured complex scenarios.
References:
[1] BENKOSKI S J, MONTICINO M G, WEISINGER J R. A survey of the search theory literature[J]. Naval research lo-gistics, 1991, 38(4): 469-494.
[2] 吴军, 徐昕, 连传强, 等. 协作多机器人系统研究进展综述[J]. 智能系统学报, 2011, 6(1): 13-27
WU Jun, XU Xin, LIAN Chuanqiang, et al. A survey of recent advances in cooperative multi-robot systems[J]. CAAI transactions on intelligent systems, 2011, 6(1): 13-27
[3] 袁波. 面向卫星资源规划的海面运动目标分析方法研究[D]. 长沙: 国防科学技术大学, 2010: 4-5.
YUAN Bo. Research on analysis of maritime moving target for satellite resource scheduling[D]. Changsha: National University of Defense Technology, 2010: 4-5.
[4] FORSTER C, PIZZOLI M, SCARAMUZZA D. Appear-ance-based active, monocular, dense reconstruction for micro aerial vehicle[C]//Proceedings of 2014 Robotics: Science and Systems Conference. Berkeley, USA, 2014.
[5] XIA Fei, ZAMIR A R, HE Zhiyang, et al. Gibson Env: real-world perception for embodied agents[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 9068-9079.
[6] RAMIREZ-PAREDES J P, DOUCETTE E A, CURTIS J W, et al. Distributed information-based guidance of multiple mobile sensors for urban target search[J]. Autonomous ro-bots, 2018, 42(2): 375-389.
[7] BEST G, CLIFF O M, PATTEN T, et al. Dec-MCTS: de-centralized planning for multi-robot active perception[J]. The international journal of robotics research, 2019, 38(2/3): 316-337.
[8] FAIGL J. GSOA: growing self-organizing Ar-ray-unsupervised learning for the close-enough traveling salesman problem and other routing problems[J]. Neuro-computing, 2018, 312: 120-134.
[9] BEST G, FAIGL J, FITCH R. Online planning for mul-ti-robot active perception with self-organising maps[J]. Autonomous robots, 2018, 42(4): 715-738.
[10] PRICE E, LAWLESS G, LUDWIG R, et al. Deep neural network-based cooperative visual tracking through multiple micro aerial vehicles[J]. IEEE robotics and automation letters, 2018, 3(4): 3193-3200.
[11] TALLAMRAJU R, RAJAPPA S, BLACK M J, et al. De-centralized MPC based obstacle avoidance for multi-robot target tracking scenarios[C]//Proceedings of 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics. Philadelphia, USA, 2018.
[12] AHMAD A, LAWLESS G, LIMA P. An online scalable approach to unified Multirobot cooperative localization and object tracking[J]. IEEE transactions on robotics, 2017, 33(5): 1184-1199.
[13] CHEN Yufan, LIU Miao, EVERETT M, et al. Decentralized non-communicating Multiagent collision avoidance with deep reinforcement learning[C]//Proceedings of 2017 IEEE International Conference on Robotics and Automation. Singapore, 2017.
[14] LONG Pinxin, LIU Wenxi, PAN Jia. Deep-learned colli-sion avoidance policy for distributed multiagent navigation[J]. IEEE robotics and automation letters, 2017, 2(2): 656-663.
[15] 熊雄, 杨日杰, 沈阳. 基于等效平均探测宽度的航空磁探潜搜索概率评估模型[J]. 系统工程与电子技术, 2014, 36(3): 487-493
XIONG Xiong, YANG Rijie, SHEN Yang. Search proba-bility evaluation model of airborne magnetic anomaly detection based on equivalent average sweep width[J]. Systems engineering and electronics, 2014, 36(3): 487-493
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems