Dynamic Motion Planning for Aerial Surveillance on a Fixed-Wing UAV

摘要

We present an efficient path planning algorithm for an Unmanned AerialVehicle surveying a cluttered urban landscape. A special emphasis is onmaximizing area surveyed while adhering to constraints of the UAV and partiallyknown and updating environment. A Voronoi bias is introduced in theprobabilistic roadmap building phase to identify certain critical milestonesfor maximal surveillance of the search space. A kinematically feasible butcoarse tour connecting these milestones is generated by the global pathplanner. A local path planner then generates smooth motion primitives betweenconsecutive nodes of the global path based on UAV as a Dubins vehicle andtaking into account any impending obstacles. A Markov Decision Process (MDP)models the control policy for the UAV and determines the optimal action to beundertaken for evading the obstacles in the vicinity with minimal deviationfrom current path. The efficacy of the proposed algorithm is evaluated in anupdating simulation environment with dynamic and static obstacles.