MOMDP solving algorithms comparison for safe path planning problems in urban environments

This paper tackles a problem of UAV safe path planning in an urban environment where the onboard sensors can be unavailable such as GPS occlusion. The key idea is to perform UAV path planning along with its navigation an guidance mode planning where each of these modes uses different set of sensors and whose availability and performance are environment-dependent. It is supposed to have a-priori knowledge in a form of gaussians mixture maps of obstacles and sensors availabilities. These maps allow the use of an Extended Kalman Filter (EKF) to have an accurate state estimate. This paper proposes a planner model based on Mixed Observability Markov Decision Process (MOMDP) and EKF. It allows the planner to propagate such probability map information to the future path for choosing the best action minimizing the expected cost.