Automatic Leader-Follower Persistent Formation Generation With Minimum Agent-Movement in Various Switching Topologies

被引:70
作者
Yu, Dengxiu [1 ,2 ]
Chen, C. L. Philip [3 ,4 ,5 ]
机构
[1] Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau 999078, Peoples R China
[2] Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Peoples R China
[3] Univ Macau, Fac Sci & Technol, Macau 999078, Peoples R China
[4] Dalian Maritime Univ, Coll Nav, Dalian 116026, Peoples R China
[5] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
基金
中国国家自然科学基金;
关键词
Topology; Switches; Optimization; Shape; Multi-agent systems; Sensors; Control law; downward-tree; leader-follower; multiagent systems (MASs); relation-invariable persistent formation (RIPF); switching topologies; MULTIAGENT SYSTEMS; CONSENSUS; NETWORK;
D O I
10.1109/TCYB.2018.2865803
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents the generation strategy, motion planning, and switching topologies of a distance-based leader-follower relation-invariable persistent formation (RIPF) of multiagent systems (MASs). An efficient algorithm is designed to find out if a persistent formation can be generated from a rigid graph. Derived from the properties of a rigid graph, the algorithm to generate RIPF from any initial location is presented. In order to generate different RIPFs in the switching topology, state and transition matrices are introduced. To achieve the minimum agent-movement among RIPFs, a downward-tree combinatorial optimization algorithm is presented. In the end, with the selected minimum agent-movement RIPF, a control law is designed to drive initial RIPF to desired RIPF with given distances among agents. Simulation results show the proposed generation method, control law, and downward-tree are effective to realize the desired formation.
引用
收藏
页码:1569 / 1581
页数:13
相关论文
共 45 条
[1]   Saturated Nussbaum Function Based Approach for Robotic Systems With Unknown Actuator Dynamics [J].
Chen, Ci ;
Liu, Zhi ;
Zhang, Yun ;
Chen, C. L. Philip ;
Xie, Shengli .
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 46 (10) :2311-2322
[2]   AN INTEGRATION OF NEURAL-NETWORK AND RULE-BASED SYSTEMS FOR DESIGN AND PLANNING OF MECHANICAL ASSEMBLIES [J].
CHEN, CLP ;
PAO, YH .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1993, 23 (05) :1359-1371
[3]   A connection between formation infeasibility and velocity alignment in kinematic multi-agent systems [J].
Dimarogonas, Dimos V. ;
Kyriakopoulos, Kostas J. .
AUTOMATICA, 2008, 44 (10) :2648-2654
[4]   Network-based leader-following consensus for distributed multi-agent systems [J].
Ding, Lei ;
Han, Qing-Long ;
Guo, Ge .
AUTOMATICA, 2013, 49 (07) :2281-2286
[5]   Distributed Time-Varying Formation Tracking Analysis and Design for Second-Order Multi-Agent Systems [J].
Dong, Xiwang ;
Xiang, Jie ;
Han, Liang ;
Li, Qingdong ;
Ren, Zhang .
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2017, 86 (02) :277-289
[6]   An optimization-based shared control framework with applications in multi-robot systems [J].
Fang, Hao ;
Shang, Chengsi ;
Chen, Jie .
SCIENCE CHINA-INFORMATION SCIENCES, 2018, 61 (01)
[7]   Iterative learning control approach for consensus of multi-agent systems with regular linear dynamics [J].
Fu, Qin ;
Gu, Panpan ;
Li, Xiangdong ;
Wu, Jianrong .
SCIENCE CHINA-INFORMATION SCIENCES, 2017, 60 (07)
[8]  
Gaur P. K., 2017, CORR
[9]  
Hendrickx J., 2006, P INT S MATH THEORY, P859
[10]  
Hendrickx J. M., 2006, CORR