Kinematic parameter based behaviour modelling and control of a bio-inspired robotic fish

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 in a body caudal fin (BCF) carangiform fish in a robotic counterpart. Based on fish biology a 2-level behavior based control scheme is proposed. High level control is modeled by robotic fish swimming behavior. It uses a Lighthill (LH) body wave to generate desired joint trajectory patterns. LH wave has intrinsic kinematic parameters Tail-beat frequency (TBF) and Caudal amplitude (CA) which can be modulated to change this trajectory. Parameter information is retrieved from a biological fish memory inspired brain map. This map stores operating region information on TBF and CA parameters. Based on a environment based error feedback signal robotic fish map selects the right parameter/s value showing adaptive behaviour. A finite state machine methodology has been used to model this brain-kinematic-map control. Low level control is implemented using computed torque method (CTM) with dynamic PD compensation, to track encoded patterns (trajectory) for fish-tail undulation. Three types of parameter adaptation for the two chosen parameters have been shown to successfully emulate fish swimming behavior. Joint-position tracking results are found to be satisfactory. Error magnitudes are smaller and its convergence is fast.