Observability-based path-planning and flow-relative control of a bioinspired sensor array in a Karman vortex street

Abstract

This paper describes the use of a bioinspired array of pressure sensors to estimate and control flow-relative position using potential flow theory and a recursive Bayesian filter. Inspired by the lateral-line neuromasts found in fish, the sensing scheme is validated using off-the-shelf pressure sensors. First, the strength and location of a stationary spiral vortex are estimated and closed-loop control of relative position is demonstrated experimentally. Second, we identify an optimal path through a Karman vortex street using empirical observability. Finally, the vorticity and location of the Karman vortex street is estimated and closed-loop control to the optimal path is demonstrated experimentally. This work is a precursor to an autonomous robotic fish sensing the wake of another fish and/or performing pursuit and schooling behavior.