Air-to-Ground Surveillance Using Predictive Pursuit

Abstract

This paper introduces a probabilistic prediction model with a novel variant of the Markov decision process to improve tracking time and location detection accuracy in an air-to-ground robot surveillance scenario. While most surveillance algorithms focus mainly on controls of an unmanned aerial vehicle (UAV) and camera for faster tracking of an unmanned ground vehicle (UGV), this paper proposes a way of minimizing detection and tracking time by applying a prediction model to the first observed path taken by the UGV. We present a pursuit algorithm that addresses the problem of target (UGV) localization by combining prediction of used planning algorithm by the target, and application of the same planning algorithm to predict future trajectories. Our results show a high predictive accuracy based on a final position attained by the target and the location predicted by our model.