Planification de perception et de mission en environnement incertain : Application à la détection et à la reconnaissance de cibles par un hélicoptère autonome

摘要

Mobile and aerial robots are faced to the need of planning actions with incomplete information about the state of theworld. In this context, this thesis proposes a modeling and resolution framework for perception and mission planningproblems where an autonomous helicopter must detect and recognize targets in an uncertain and partially observableenvironment. We founded our work on Partially Observable Markov Decision Processes (POMDPs), because it proposes ageneral optimization framework for perception and decision tasks under long-term horizon. A special attention is given tothe outputs of the image processing algorithm in order to model its uncertain behavior as a probabilistic observationfunction. A critical study on the POMDP model and its optimization criterion is also conducted. In order to respect safetyconstraints of aerial robots, we then propose an approach to properly handle action feasibility constraints in partiallyobservable domains: the AC-POMDP model, which distinguishes between the verification of environmental properties andthe information about targets' nature. Furthermore, we propose a framework to optimize and execute POMDP policies inparallel under time constraints. This framework is based on anticipated and probabilistic optimization of future executionstates of the system. Finally, we embedded this algorithmic framework on-board Onera's autonomous helicopters, andperformed real flight experiments for multi-target detection and recognition missions.