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

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.