Motion Planning in an Uncertain Environment: Application to an Unmanned Helicopter

In this work we present a methodology for intelligent motion planning in an uncertain environment using a non-local sensor, such as radar. This methodology is applied to an unmanned helicopter navigating a cluttered urban environment. We show that the problem of motion planning in a 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 motion 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 a non-local sensor into the problem formulation, which significantly accelerates the convergence of the estimation and planning algorithms. Further we show that the motion 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 motion planning for an unmanned helicopter in a partially known model of the Texas A&M campus