Autonomous Search and Rescue Rotorcraft Mission Stochastic Planning with Generic DBNs

This paper proposes an original generic hierarchical framework in order to facilitate the modeling stage of complex autonomous robotics mission planning problems with action uncertainties. Such stochastic planning problems can be modeled as Markov Decision Processes [5]. This work is motivated by a real application to autonomous search and rescue rotorcraft within the ReSSAC project at ONERA. As shown in Figure 1.a, an autonomous rotorcraft must fly and explore over regions, using waypoints, and in order to find one (roughly localized) person per region (dark small areas). Uncertainties can come from the unpredictability of the environment (wind, visibility) or from a partial knowledge of it: map of obstacles, or elevation map etc. After a short presentation of the framework of structured Markov Decision Processes (MDPs), we present a new original hierarchical MDP model based on generic Dynamic Bayesian Network templates. We illustrate the benefits of our approach on the basis of search and rescue missions of the ReSSAC project.