Safe path planning for UAV urban operation under GNSS signal occlusion risk

This paper introduces a concept of safe path planning for UAV’s autonomous operation in an urban environment where GNSS-positioning may become unreliable or even unavailable. If the operation environment is a priori known and geo-localized, it is possible to predict a GNSS satellite constellation and hence to anticipate its signal occlusions at a given point and time. Motivated from this, our main idea is to utilize such sensor availability map in path planning task for ensuring UAV navigation safety. The proposed concept is implemented by a Partially Observable Markov Decision Process (POMDP) model. It incorporates a low-level navigation and guidance module for propagating the UAV state uncertainty in function of the probabilistic sensor availability. A new definition of cost function is introduced in this model such that the resulting optimal policy respects a user-defined safety requirement. A goal-oriented version of Monte-Carlo Tree Search algorithm, called POMCP-GO, is proposed for POMDP solving. The developed safe path planner is evaluated on two simple obstacle benchmark maps as well as on a real elevation map of San Diego downtown, along with GPS availability maps