Motion Planning Under Uncertainty: Application to an Unmanned Helicopter

A methodology is presented in this work for intelligent motion planning in an uncertain environment using a non-local sensor, such as a radar. This methodology is applied to an unmanned helicopter navigating a cluttered urban environment. It is shown that the problem of motion 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 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. The 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, the motion planning and estimation problems, possess special structure which can be exploited to significantly reduce the computational burden of the associated algorithms. The methodology is applied to the problem of motion planning for an unmanned helicopter through a partially known model of the Texas A&M campus and the testing is done on a flight simulator.