Towards a MOMDP model for UAV safe path planning in urban environment

This paper tackles a problem of UAV safe path planning in an urban environment in which UAV is at risks of GPS signal occlusion and obstacle collision. The key idea is to perform the UAV path planning along with its navigation and guidance mode planning, where each of these modes uses different sensors whose availability and performance are environment-dependent. A partial knowledge on the environment is supposed to be available in the form of probability maps of obstacles and sensor availabilities. This paper proposes a planner model based on Mixed Observability Markov Decision Process (MOMDP). It allows the planner to propagate such probability map information to the future path for choosing the best action. This paper provides a MOMDP model for the planner with an approximation of the belief states by Mixture of Gaussian functions