Optimal measurement strategies for target tracking by a biomimetic underwater vehicle

Abstract

This study presents a novel navigation and control system allowing a biomimetic-autonomous underwater vehicle (BAUV) to track a target. A Bayesian approach using an extended Kalman filter and combined localization and environmental mapping by a BAUV are implemented. This strategy selects the best sensor measurement by choosing one of several forward-looking directions. The body of the BAUV moves in a cyclical pattern; thus, an inexpensive echo sounder can be installed on the BAUV head to detect environmental features without the need for expensive scanning devices. The localization and environmental mapping problem is then transformed into a non-linear two-point boundary value problem. Optimal policies are to maintain the accuracy of predicted states and to approach minimal observation cost by solving the control problem. A line-of-sight guidance law is utilized that drives the BAUV to the target. An approach that controls the motion of the body/caudal fin and pectoral fins of the BAUV is utilized for target tracking. Estimation, measurement, and control processes are integrated to form a working system. Experiments using a test bed BAUV confirm the effectiveness of the proposed approach.