[1]吴莹莹,丁肇红,刘华平,等.面向环境探测的多智能体自组织目标搜索算法[J].智能系统学报,2020,15(2):289-295.[doi:10.11992/tis.201908023]
 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]
点击复制

面向环境探测的多智能体自组织目标搜索算法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年2期
页码:
289-295
栏目:
学术论文—智能系统
出版日期:
2020-07-05

文章信息/Info

Title:
Self-organizing target search algorithm of multi-agent system for envi-ronment detection
作者:
吴莹莹1 丁肇红1 刘华平23 赵怀林1 孙富春23
1. 上海应用技术大学 电气与电子工程学院, 上海 201418;
2. 清华大学 计算机科学与技术系, 北京 100084;
3. 清华大学 智能技术与系统国家重点实验室, 北京 100084
Author(s):
WU Yingying1 DING Zhaohong1 LIU Huaping23 ZHAO Huailin1 SUN Fuchun23
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
关键词:
自组织算法目标搜索差分进化仿生集群无人机非结构化环境鸟群效应动态目标
Keywords:
self-organizing algorithmtarget searchdifferential evolution algorithmmulti-agent bionic algorithmunmanned aerial vehicleunstructured environmentflockingdynamic target
分类号:
TP242.6
DOI:
10.11992/tis.201908023
摘要:
针对在复杂非结构化环境下如何协调多个无人机发现静态或动态目标的问题,建立了自组织目标搜索算法框架。结合磁探仪等效平均探测宽度模型,受昆虫协调方式和鸟群效应的生物机制启发,提出了基于仿生集群算法的无人机集群分布式目标搜索模型;采用改进的自适应差分进化算法帮助无人机集群模型在环境中平衡勘探和探索,实现无人机群体的协同搜索优化。该自组织目标搜索算法旨在以最短时间实现跟踪目标数量的最大化。基于仿真平台的实验测试了该策略的性能,验证了算法对具有未知目标的非结构化复杂环境的适用性。
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.

参考文献/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

备注/Memo

备注/Memo:
收稿日期:2019-08-20。
基金项目:国家自然科学基金项目(U1613212);上海市自然科学基金项目(19ZR1455200);校级基金项目(XTCX2018-10)
作者简介:吴莹莹,硕士研究生,主要研究方向为机器人控制、多智能体控制;丁肇红,副教授,主要研究方向为智能控制与决策、系统建模。主持完成多项上海市教委科研项目。主编《自动控制原理》教材。发表学术论文数十篇;刘华平,副教授,博士生导师,主要研究方向为机器人感知、学习与控制、多模态信息融合。发表学术论文10余篇
通讯作者:刘华平.E-mail:hpliu@tsinghua.edu.cn
更新日期/Last Update: 1900-01-01