Hybrid Formation Control for Multi-Robot Hunters Based on Multi-Agent Deep Deterministic Policy Gradient

  • Oussama Hamed Complex Cyber Physical Systems Laboratory,ENSAM, Casablanca, University of Hassan II, Morocco
  • Mohamed Hamlich Complex Cyber Physical Systems Laboratory,ENSAM, Casablanca, University of Hassan II, Morocco
Keywords: Reinforcement learning, Multi-Robot System, Cooperative hunting, Path Planning, Mobile robot, Collaborative robots


The cooperation between mobile robots is one of the most important topics of interest to researchers, especially in the many areas in which it can be applied. Hunting a moving target with random behavior is an application that requires robust cooperation between several robots in the multi-robot system. This paper proposed a hybrid formation control for hunting a dynamic target which is based on wolves’ hunting behavior in order to search and capture the prey quickly and avoid its escape and Multi Agent Deep Deterministic Policy Gradient (MADDPG) to plan an optimal accessible path to the desired position. The validity and the effectiveness of the proposed formation control are demonstrated with simulation results.


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How to Cite
Hamed, O. and Hamlich, M. 2021. Hybrid Formation Control for Multi-Robot Hunters Based on Multi-Agent Deep Deterministic Policy Gradient. MENDEL. 27, 2 (Dec. 2021), 23-29. DOI:https://doi.org/10.13164/mendel.2021.2.023.
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