Hierarchical Multi-Robot Pursuit with Deep Reinforcement Learning and Navigation Planning

Robotic systems have been used to solve multiple important problems, including pursuit. However, traditional methods in game theory and controlling make it difficult to resolve pursuit problems with high dimensions, continuous spaces, and complex non-convex obstacles. Recently, many researchers have proved that deep reinforcement learning (DRL) has strong feature extraction and decision-making ability when solving decision problems in high-dimensional, continuous space. Thus, applying DRL methods to multi-robot pursuit problems is reasonable. Firstly, in this paper, a multi-robot pursuit simulation environment with complex obstacles in the Unity simulation engine is built. Moreover, a hierarchical method combined with global decision by DRL, local navigation through A* and Navigation Mesh, and collision avoidance by Reciprocal Velocity Obstacles (RVO) is proposed. Finally, the experiment results show the effectiveness of our methods in solving the multi-robot pursuit problem.

• Publication year

  2024

• Venue

  Youth Academic Annual Conference of Chinese Association of Automation

• Publication date

  2024-06-07

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/YAC63405.2024.10598424
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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