Using hybrid planning for plan reparation

In this work we propose a plan repair algorithm designed to be used in a real-life setting for a team of autonomous robots. This algorithm is built on top of a hybrid planner. This planner mixes partial order planning and hierarchical planning. This allows the creation of a plan with temporal flexibility while using human knowledge to improve the search process. Simulation shows that repairing increases the number of solved problems or at least reduces the number of plans explored. The algorithm uses the same hierarchical knowledge as the underlying planner, thus needing no more human modelling to properly run. We show that using this knowledge can help the reparation, even when some half-executed abstract actions are present in the plan.