Robust Velocity Control for Minimum Steady State Uncertainty in Persistent Monitoring Applications

Abstract

We present a velocity controller for persistent monitoring applications that minimizes the maximum eigenvalue of the Kalman filter covariance for any initial sensing position and any initial covariance. A set of points of interest in the environment can be measured along a closed static path by an autonomous, mobile robotic sensing platform. We model the environmental phenomenon at the points of interest as a Wiener process that is estimated by a Kalman filter. We propose a Greedy Knockdown Algorithm to determine the optimal number of observations for each point of interest per cycle and formulate the problem as a linear program with a set of robustness constraints. In simulation, the proposed controller is compared to constant velocity and existing first-order velocity controllers in the literature. The proposed method outperforms existing methods across test cases with a range of different parameters: number of points of interest, noise level of the observation model, and maximum velocity.