Uncertainty Measured Markov Decision Process in Dynamic Environments

Abstract

Successful robot path planning is challenging in the presence of visual occlusions and moving targets. Classical methods to solve this problem have used visioning and perception algorithms in addition to partially observable markov decision processes to aid in path planning for pursuit-evasion and robot tracking. We present a predictive path planning process that measures and utilizes the uncertainty present during robot motion planning. We develop a variant of subjective logic in combination with the Markov decision process (MDP) and provide a measure for belief, disbelief, and uncertainty in relation to feasible trajectories being generated. We then model the MDP to identify the best path planning method from a list of possible choices. Our results show a high percentage accuracy based on the closest acquired proximity between a target and a tracking robot and a simplified pursuer trajectory in comparison with related work.