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Abstract
Cooperative control of multi-robot systems (MRS) has earned significant research interest over the past two decades due to its potential applications in multi-disciplinary engineering problems. In contrast to a single specialized robot, MRS can be designed to offer flexibility, reconfigurability, robustness to faults and cost-effectiveness in solving complex and challenging tasks. In this paper, we aim to develop a unified cluster formation containment coordination framework for networked robots that can be decomposed into two layers containing the leaders and the followers. According to the proposed methodology, the leader robots are first distributed into a set of distinct and nonoverlapping clusters depending on the positions and priorities of the targets exploiting a game-theoretic rule. Then they are steered to attain the desired formations around the corresponding targets. Subsequently, the follower robots are made to converge into the convex hull spanned by the leaders of the individual clusters. A prototype search and rescue operation is considered to highlight the usefulness of the proposed framework. Furthermore, realtime hardware experiments were conducted on miniature mobile robots to validate the feasibility of the theoretical results.
Original language | English |
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Pages (from-to) | 1936-1955 |
Number of pages | 20 |
Journal | IEEE Transactions on Robotics |
Volume | 37 |
Issue number | 6 |
Early online date | 5 May 2021 |
DOIs | |
Publication status | Published - 1 Dec 2021 |
Keywords
- Autonomous clustering
- Clustering algorithms
- Collision avoidance
- collision avoidance
- containment control
- decentralized decision making
- formation tracking
- multirobot coordination
- Real-time systems
- Robot kinematics
- Robots
- search and rescue
- Target tracking
- task allocation
- Task analysis
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Dive into the research topics of 'A Decentralized Cluster Formation Containment Framework for Multirobot Systems'. Together they form a unique fingerprint.Projects
- 1 Finished
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Robotics and Artificial Intelligence for Nuclear (RAIN)
Lennox, B. (PI), Arvin, F. (CoI), Brown, G. (CoI), Carrasco Gomez, J. (CoI), Da Via, C. (CoI), Furber, S. (CoI), Luján, M. (CoI), Watson, S. (CoI), Watts, S. (CoI) & Weightman, A. (CoI)
2/10/17 → 31/03/22
Project: Research