Optimal path planning of a target-following fixed-wing UAV using sequential decision processes

In this work, we consider the optimal path of a fixed-wing unmanned aerial vehicle (UAV) tracking a mobile surface target. One of the limitations of fixed-wing UAVs in tracking mobile targets is the lack of hovering capability when the target moves much slower than the minimum UAV speed, requiring the UAV maintain an orbit about the target. In this paper, we propose a method to find the optimal policy for fixed-wing UAVs to minimize the location uncertainty of a mobile target. Using a grid-based Markov Decision Process (MDP), we use an off-line policy iteration algorithm to find an optimal UAV path in a coarse discretized state space, followed by an on-line policy iteration algorithm that applies a finer grid MDP to the region of interest to find the final optimal UAV trajectory. We validate the proposed algorithm using computer simulations. Comparing the simulation results with other methods, we show that the proposed method has up to 13% decrease in error uncertainty than ones resulted using conventional methods.