Abstract
Mission planning and execution for autonomous vehicles is crucial for their effective and efficient operation during scientific exploration, or search and rescue missions, to mention a few. Automated Planning has shown to be a useful tool for ''high level'' mission planning, that is, allocating tasks to vehicles while following given constraints (e.g., energy, collision avoidance).
In this paper, we focus on making mission planning flexible and robust. That is, a human mission coordinator can modify tasks during the mission execution, so the tasks have to be dynamically reallocated during the process.
Moreover, we assume that communication might not be reliable when vehicles are ''outside'', i.e., performing the tasks, and thus we enforce vehicles to come back to their safe spots regularly. To address these requirements, we have developed two models, namely ''all tasks'' and ''one round'', and integrated them to the control software.
We have evaluated our approach in a field experiment focused on a mine-hunting scenario.