Learning Path Constraints for UAV Autonomous Navigation Under Uncertain GNSS Availability

Abstract

This paper addresses a safe path planning problem for UAV urban navigation, under uncertain GNSS availability. The problem can be modeled as a POMDP and solved with sampling-based algorithms. However, such a complex domain suffers from high computational cost and achieves poor results under real-time constraints. Recent research seeks to integrate offline learning in order to efficiently guide online planning. Inspired by the state-of-the-art CAMP (Context-specific Abstract Markov decision Process) formalization, this paper proposes an offline process which learns the path constraint to impose during online POMDP solving in order to reduce the policy search space. More precisely, the offline learnt constraint selector returns the best path constraint according to the GNSS availability probability in the environment. Conclusions of experiments, carried out for three environments, show that using the proposed approach allows to improve the quality of a solution reached by an online planner, within a fixed decision-making timeframe, particularly when GNSS availability probability is low.

Towards real-time safe flight paths for urban air mobility

“…Also, the computation associated with their solution takes hours rather than seconds (i.e., a non-real-time solution). Zaninotti et al [119] proposed an offline process which returns the best path constraint to impose for an online partially observable Markov decision process (POMDP) solver. Their approach leverages a probability distribution for determining GNSS availability based on PDOP.…”