Uncertainty based online planning for UAV missions in GPS-denied and cluttered environments

Abstract

This research is a novel approach to enabling Unmanned Aerial Vehicle (UAV) navigation and target finding and tracking missions under uncertainty in cluttered and GPS-denied environments. A novel framework, implemented as a modular system, formulates the missions as online Partially Observable Markov Decision Processes (POMDP). The online POMDP computes a motion policy that balances multiple mission objectives optimally. The motion policy is updated while flying based onboard sensor observations. This research provides an enabling technology for UAV missions such as search and rescue, biodiversity assessment, underground mining and infrastructure inspection in challenging and natural environments.