Reinforcement learning method for target hunting control of multi‐robot systems with obstacles

Aiming at the target encirclement problem of multi‐robot systems, a target hunting control method based on reinforcement learning is proposed. First, the Markov game modeling for the multi‐robot system is carried out. According to the task of hunting, potential energy models are designed to meet the...

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Veröffentlicht in:International journal of intelligent systems Jg. 37; H. 12; S. 11275 - 11298
Hauptverfasser: Fan, Zhilin, Yang, Hongyong, Liu, Fei, Liu, Li, Han, Yilin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York John Wiley & Sons, Inc 01.12.2022
Schlagworte:
Algorithms
Control methods
Control systems
Hunting
Intelligent systems
Machine learning
multi‐robot systems
Obstacle avoidance
Potential energy
potential energy function
reinforcement learning
Robot control
Robots
Strategy
target hunting
Teaching methods
Tracking
ISSN:0884-8173, 1098-111X
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Zusammenfassung:Aiming at the target encirclement problem of multi‐robot systems, a target hunting control method based on reinforcement learning is proposed. First, the Markov game modeling for the multi‐robot system is carried out. According to the task of hunting, potential energy models are designed to meet the requirements of arriving at the desired state and avoiding obstacles. The multi‐robot reinforcement learning algorithm guided by the potential energy models is presented to perform the hunting, where reinforcement learning principles are combined with the model control. Secondly, based on the potential energy models, the target‐tracking hunting strategy and the target‐circumnavigation hunting strategy are established. In the former, the consensus tracking of multi‐robot systems is achieved by designing the velocity potential energy function. And in the latter, virtual circumnavigation points are added to construct the potential energy function, which realizes the desired circumnavigation. Finally, the effectiveness of target hunting control based on the multi‐robot reinforcement learning method is verified by simulation.
Bibliographie:ObjectType-Article-1
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ISSN:0884-8173
1098-111X
DOI:10.1002/int.23042