Optimized visibility motion planning for target tracking and localization

Abstract

This paper presents a visibility-based method for planning the motion of a mobile robotic sensor with bounded field-of-view to optimally track a moving target while localizing itself. The target and robot states are estimated from online sensor measurements and a set of a priori known landmarks, using an extended Kalman filter (EKF), and thus the proposed method is applicable to robots without a global positioning system. It is shown that the problem of optimizing the target tracking and robot localization performance is equivalent to optimizing the visibility or probability of detection in the EKF framework under mild assumptions. The control law that maximizes the probability of detection for a robotic sensor with a sector-shaped field-of-view (FoV) is derived as a function of the robot heading and aperture. Simulations have been conducted on synthetic experiments and the results show that the optimized-visibility approach is effective at avoiding target loss, and outperforms a state-of-the-art potential method based on robot trailer models [1].