Underwater Target Tracking Control of an Untethered Robotic Fish With a Camera Stabilizer

Implementing underwater target tracking remains difficult for a free-swimming robotic fish owing to the intrinsically reciprocating motion in fishlike propulsion. In this article, we present a novel robotic fish platform with a camera stabilizing system and achieve real-time two-dimensional target tracking assisted by reinforcement learning (RL) in continuous environments. More specifically, we first develop an active visual tracking system based on cascade control structure to obtain the relative orientation between the robotic fish and the underwater target. Then, we propose a target tracking controller dealing with continuous state and action spaces based on deep RL (DRL). The controller takes the position of the target object as input and yields the motion parameters of the bioinspired central pattern generator governed robotic fish. The robustness and adaptability of the proposed controller as well as the influence of time-delays on the control system are explored via simulated experiments under different scenarios. Finally, both static and dynamic tracking experiments on the actual robotic fish demonstrate the effectiveness of the proposed mechatronic design and control methods, providing insights to executing aquatic vision-based tracking tasks.