Prediction-based dynamic target interception using discrete Markov chains

Abstract

In this paper we present a novel model for the prediction of the future states of dynamic targets as stochastic processes with associated learned transition probabilities. An accompanying control algorithm for target interception in the absence of prior knowledge using discrete Markov Chains is also presented. Based on the predicted states of the target the control algorithm leads to interception strategies for which the length of path of the pursuer is typically less than in the straightforward target pursuit case. The work has application to target interception using autonomous vehicles where the target and environment are unknown and dynamic. © 2010 Springer-Verlag.