Central Pattern Generators (CPGs) can generate robust, smooth and coordinated oscillatory signals for locomotion control of robots with multiple degrees of freedom, but the tuning of CPG parameters for a desired locomotor pattern constitutes a tremendously difficult task. This paper addresses this problem for the generation of fish-like swimming gaits with an adaptive CPG network on a multi-joint robotic fish. Our approach converts the related CPG parameters into dynamical systems that evolve as part of the CPG network dynamics. To reproduce the bodily motion of swimming fish, we use the joint angles calculated with the trajectory approximation method as teaching signals for the CPG network, which are modeled as a chain of coupled Hopf oscillators. A novel coupling scheme is proposed to eliminate the influence of afferent signals on the amplitude of the oscillator. The learning rules of intrinsic frequency, coupling weight and amplitude are formulated with phase space representation of the oscillators. The frequency, amplitudes and phase relations of the teaching signals can be encoded by the CPG network with adaptation mechanisms. Since the Hopf oscillator exhibits limit cycle behavior, the learned locomotor pattern is stable against perturbations. Moreover, due to nonlinear characteristics of the CPG model, modification of the target travelling body wave can be carried out in a smooth way. Numerical experiments are conducted to validate the effectiveness of the proposed learning rules.

A novel ostraciiform swimming, vision-based autonomous robotic fish is developed in this paper. Its feasibility and capability are shown by implementing a dynamic target following task in a swimming pool. Inspired by boxfish that is highly stable and fairly maneuverable, the robotic fish is designed and constructed by locating multiple propulsors peripherally around a rigid body. Swimming locomotion of the robotic fish is achieved through harmonic oscillations of the tail and pectoral fins. The forces and moments acting on the fins and body are analyzed and the governing motion equations are derived. Through coordinating the movements of the propulsors, several typical swimming patters are empirical designed and realized. A digital camera is integrated in the robotic fish, and the visual information is processed with the embedded microcontroller. To treat the degradation of underwater image, a continuously adaptive mean shift (Camshift) algorithm is modified to keep visual lock on the moving target. A fuzzy logic controller is designed for motion regulation of a hybrid swimming pattern, which employs synchronized pectoral fins for thrust generation and tail fin for steering. A simple target following task is designed via an autonomous robotic fish swimming after a manually controlled robotic fish with fixed distance. The swimming performance of the robotic fish is tested and the effectiveness of the proposed target following method is verified experimentally.