Collective Intelligent Reinforcement Learning for Biomimetic Clapping Trajectory Optimization

The foreflippers of sea lions enable highly efficient and agile locomotion through a unique clapping propulsion mechanism. Nonetheless, identifying the optimal kinematics for these movements constitutes a significant challenge, owing to the highly nonlinear and dynamic fluid-structure interactions. To address this, we present a collective intelligent reinforcement learning method for generating clapping trajectories of a bionic foreflipper. The approach begins by modeling biological motions using periodic triaxial motions based on closed Bézier curves, enabling the representation of essential clapping dynamics with a small set of parameters. Subsequently, we propose a collective intelligent reinforcement learning algorithm to efficiently optimize the parameterized clapping trajectory. A comparative evaluation against conventional optimization algorithms reveals accelerated convergence and superior performance. The optimal trajectories are validated against biological motion data, confirming their physiological plausibility. Simulation results show that a significant 75% improvement in thrust impulse and a 70% reduction in convergence time compared to the baseline reinforcement learning method.