Fault tolerant adaptive mission planning with semantic knowledge representation for autonomous underwater vehicles

This paper proposes a novel approach for autonomous mission plan recovery for maintaining operability of unmanned underwater vehicles. It combines the benefits of knowledge-based ontology representation, autonomous partial ordering plan repair and robust mission execution. The approach uses the potential of ontology reasoning in order to orient the planning algorithms adapting the mission plan of the vehicle. It can handle uncertainty and action scheduling in order to maximize mission efficiency and minimise mission failures due to external unexpected factors. Its performance is presented in a set of simulated scenarios for different concepts of operations for the underwater domain. The paper concludes by showing the results of a trial demonstration carried out on a real underwater platform. The results of this paper are readily applicable to land and air robotics.