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This paper introduces a biomimetic wave-spiral robot with dual-mode propulsion and proposes a deep reinforcement learning (DRL) based autonomous switching tracking control algorithm. The algorithm uses two independent DRL policy networks and an adaptive mode switcher to enable stable tracking control. Simulation and physical experiments demonstrate a 95% task completion rate and a 29.5% improvement in operation time compared to traditional methods when tracking dynamic targets.
A biomimetic robot that swims like a fish and switches gaits autonomously can track targets 30% faster than traditional methods.
Drawing inspiration from piscine locomotion strategies, biomimetic underwater robotic systems demonstrate enhanced operational efficiency and stealth capabilities when executing target tracking missions within dynamic aquatic environments. However, challenges related to their operational speed and control precision hinder the timely and accurate completion of tasks. In this letter, a biomimetic wave-spiral robot with dual-mode propulsion capabilities is developed, and an autonomous switching tracking control algorithm based on deep reinforcement learning (DRL) is proposed. First, the kinematic models of the fins and fin rays in both spiral and wave modes are derived, and a multi segmented splicing design is employed to produce multi-modal fins, resulting in an integrated propulsion structure compatible with both high-speed and high-maneuverability modes. Second, a tracking control system consisting of two independent DRL policy networks and an adaptive mode switcher is proposed to realize autonomous and stable tracking control. Finally, simulation and swimming pool experiments demonstrate that the wave-spiral robot equipped with the dual-mode adaptive switching algorithm significantly achieved a 95% task completion rate and an average reward of 0.85 in the target tracking control experiment. Moreover, it demonstrates a 29.5% improvement in operation time when dealing with state disturbances and dynamic targets compared with the traditional control method. This study provides a promising and robust solution for developing tracking control for multi-mode biomimetic robots.
