Decentralized Multirobotic Fish Pursuit Control With Attraction-Enhanced Reinforcement Learning

Abstract

Adaptive and efficient cooperative control is a crucial capability for multirobotic fish systems, as it can substantially enhance their performance in complex underwater tasks. The pursuit and evasion dynamics in such topics have gained significant attention from the scientific community. In this article, we present a novel adaptive algorithm tailored specifically for cooperative pursuit among multirobotic fish systems. Benefiting from the integration of attraction mechanisms and reinforcement learning techniques, the proposed method empowers the robotic fish to make adaptive decisions based on local observations and environmental cues. Meanwhile, a state transition environment has been customized to the unique dynamics of robotic fish, equipping the cooperative pursuit strategy to fulfill practical application requirements and facilitate adaptation across diverse platforms. Besides, based on the curriculum learning approach, a decentralized pursuit policy is also formulated and implemented within the developed robotic fish system. Simulations and real-world experiments have validated the efficiency and adaptability of this cooperative pursuit strategy. This research offers valuable insights and contributions to the exploration of cooperative control in multirobotic fish systems, addressing the critical challenge of achieving adaptive and efficient coordination in complex underwater environments.