Particle swarm optimization-based central patter generator for robotic fish locomotion

This paper proposes particle swarm optimization-based central pattern generator (CPG) to generate rhythmic signals for fish-like locomotion of robotic fish. The robotic fish's wave form approximates fish's traveling wave. Since each joint angle of the robotic fish is modeled by a periodic function, it can be easily produced by a CPG. A CPG consists of biological neural oscillators, which can produce coordinated rhythmic signals by using simple input signals. The proposed CPG uses a neural oscillator for each joint of a robotic fish. To optimize the parameters of the CPG which determine the output signals, particle swam optimization (PSO) is employed. The effectiveness of the proposed CPG is demonstrated by computer simulation and real experiment with the robotic fish Fibo, developed in the Robot Intelligence Technology Lab., KAIST.