Encirclement Guaranteed Cooperative Pursuit with Robust Model Predictive Control

Wang, Chen1,2; Chen, Hua1; Pan, Jia2; Zhang, Wei1,3

10.1109/IROS51168.2021.9636127

2021

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2153-0858

978-1-6654-1715-0

2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)

1473-1479

SEP 27-OCT 01, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

This paper studies a novel encirclement guaranteed cooperative pursuit problem involving N pursuers and a single evader in an unbounded two-dimensional game domain. Throughout the game, the pursuers are required to maintain encirclement of the evader, i.e., the evader should always stay inside the convex hull generated by all the pursuers, in addition to achieving the classical capture condition. To tackle this challenging cooperative pursuit problem, a robust model predictive control (RMPC) based formulation framework is first introduced, which simultaneously accounts for the encirclement and capture requirements under the assumption that the evader's action is unavailable to all pursuers. Despite the reformulation, the resulting RMPC problem involves a bilinear constraint due to the encirclement requirement. To further handle such a bilinear constraint, a novel encirclement guaranteed partitioning scheme is devised that simplifies the original bilinear RMPC problem to a number of linear tube MPC (TMPC) problems solvable in a decentralized manner. Simulation experiments demonstrate the effectiveness of the proposed solution framework. Furthermore, comparisons with existing approaches show that the explicit consideration of the encirclement condition significantly improves the chance of successful capture of the evader in various scenarios.

Games Electron tubes Intelligent robots Predictive control

第一 ; 通讯

英语

CPCI-S ; EI

National Natural Science Foundation of China[62073159,62003155]

Automation & Control Systems ; Computer Science ; Engineering ; Robotics

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Robotics

WOS:000755125501032

20220711623859

Model predictive control ; Predictive control systems

Automatic Control Principles and Applications:731 ; Control Systems:731.1

Web of Science

1.Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen, Peoples R China
2.Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
3.Peng Cheng Lab, Shenzhen, Peoples R China

Wang, Chen,Chen, Hua,Pan, Jia,et al. Encirclement Guaranteed Cooperative Pursuit with Robust Model Predictive Control[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:1473-1479.