Brain-Map Based Carangiform Swimming Behaviour Modeling and Control in a Robotic Fish Underwater Vehicle

Abstract

Fish swimming demonstrates impressive speeds and exceptional characteristics in the fluid environment. The objective of this paper is to mimic undulatory swimming behaviour and its control of a body caudal fin (BCF) carangiform fish in a robotic counterpart. Based on fish biology kinematics study, a 2-level behavior based distributed control scheme is proposed. The high-level control is modeled by robotic fish swimming behavior. It uses a Lighthill (LH) body wave to generate desired joint trajectory patterns. Generated LH body wave is influenced by intrinsic kinematic parameters Tail-beat frequency (TBF) and Caudal amplitude (CA) which can be modulated to change the trajectory pattern. Parameter information is retrieved from a fish memory (cerebellum) inspired brain map. This map stores operating region information on TBF and CA parameters obtained from yellow fin tuna kinematics study. Based on an environment based error feedback signal, robotic fish map selects the right parameters value showing adaptive behaviour. A finite state machine methodology has been used to model this brain-kinematic-map control. The low-level control is implemented using inverse dynamics based computed torque method (CTM) with dynamic PD compensation. It tracks high-level generated and encoded patterns (trajectory) for fish-tail undulation. Three types of parameter adaptation for the two chosen parameters have been shown to successfully emulate robotic fish swimming behavior. Based on the proposed control strategy joint-position and velocity tracking results are discussed. They are found to be satisfactory with error magnitudes within permissible bounds.