Motion planning in uncertain environments with vision-like sensors

In this work we present a methodology for intelligent path planning in an uncertain environment using vision-like sensors, i.e., sensors that allow the sensing of the environment non-locally. Examples would include a mobile robot exploring an unknown terrain or a micro-UAV navigating in a cluttered urban environment. We show that the problem of path planning in an uncertain environment, under certain assumptions, can be posed as the adaptive optimal control of an uncertain Markov decision process, characterized by a known, control-dependent system, and an unknown, control-independent environment. The strategy for path planning then reduces to computing the control policy based on the current estimate of the environment, also known as the “certainty-equivalence” principle in the adaptive control literature. Our methodology allows the inclusion of vision-like sensors into the problem formulation, which, as empirical evidence suggests, accelerates the convergence of the planning algorithms. Further we show that the path planning and estimation problems, as formulated in this paper, possess special structure which can be exploited to significantly reduce the computational burden of the associated algorithms. We apply this methodology to the problem of path planning of a mobile rover in a completely unknown terrain.