Adaptive Mission Planning: The Embedded OODA Loop

This paper proposes a novel technique for autonomous mission plan recovery in order to increase 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 is based on a combination of unrefinement and refinement stages on the plan-space domain in order to adapt declarative mission plans. It can handle uncertainty and action scheduling in order to maximise 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