Adaptive Task-Oriented Locomotion Control of a 2D Planar Robotic Fish Model Using Deep Reinforcement Learning and Sensory-Feedback CPG Network

Abstract

Autonomous locomotion in robotic fish requires task-dependent control capabilities under changing environmental conditions. This paper proposes a hierarchical simulation-based control framework for a two-joint robotic fish in a two-dimensional (2D) planar environment. This framework integrates the twin delayed deep deterministic policy gradient (TD3) algorithm with a sensory-feedback central pattern generator (CPG). A nonlinear planar dynamic model is designed as the learning environment, and a CPG network generates rhythmic undulatory swimming. The CPG network generates smooth locomotor patterns, while the TD3 policy performs high-level neuromotor modulation for task-dependent behavior. In the target-reaching benchmark, TD3–CPG achieves a 100.0% success rate with a Wilson 95% confidence interval (CI) of [96.30%, 100.00%], outperforming benchmark models. The proposed controller is also evaluated with obstacle avoidance in target reaching and station keeping under current disturbances. In circular obstacle avoidance, TD3–CPG achieves a 98.0% success rate and a 98.0% safe-pass rate, whereas the multiple rectangular obstacle scenarios yield an overall success rate of 91.7% over 96 trials. In station keeping, the controller achieves stay ratios of 87.57 ± 12.81% under constant current and 96.88 ± 10.79% under gust current, while keeping the mean target distance below the 0.25 m station keeping radius in both cases. Within the adopted 2D planar simulation environment, the obtained results demonstrate that the proposed method exhibits task-dependent maneuvering performance within the evaluated scenarios.