Motion Optimization for a Robotic Fish Based on Adversarial Structured Control This paper proposes a taskbased control optimization method for the robotic fish. It is essentially an adversarial structured control consisting of a global control module and a local compensation control module. In detail, the global control module emulates an optimized central pattern generator with Evolutionary Strategy, while the local control module produces targeted compensation control signals with Soft ActorCritic. The linear summation of two control laws works for the final robotic fish control. Considering that the evolutionary computation optimization algorithms generally have the defect of falling into the local optimum, we propose a method of antagonistic training to improve the optimization performance. The effectiveness of the designed controller is validated by simulation on agents in Mujoco. Noticeably, the simulation results demonstrate that the proposed method teaches the agent fish to move to any target point with a low energy consumption, which lays a good foundation for application of reinforcement learning in real robotic fish control.

Obstacle Avoidance Control Strategy of a Biomimetic Wiredriven Robot Fish * Fish has attracted the attentions of researchers because of its excellent swimming performance in water, and biomimetic robot fish was then developed. For robot fish, it is quite significant to perceive its surroundings and then take actions automatically due to the unexpected obstacles. In this paper, a wiredriven robot fish, which can detect obstacles, is designed. We adopt Central Pattern Generator (CPG) for motion control of the robot fish which is capable for smooth transitions among different swimming modes. Moreover, an obstacle avoidance control strategy based on a rule base and a closed loop control structure based on PID algorithm are proposed, which can improve the robot fishxe2x80x99s perception and reaction abilities to the surrounding environment. Finally, experiments are carried out to examine the feasibility of the proposed control strategy.