Multi agent hybrid dynamical systems are a natural model for collaborative missions in which several steps and behaviors are required to achieve the goal of the mission. Missions are tasks featuring interacting subtasks, such as the decision of where to search, how to search, and when to transition from a search behavior to a rescue behavior. Control in hybrid systems is poorly understood. Theoretical results on state reachability rely on restrictive assumptions which hinder formal verification and optimization of such systems. Further difficulties arise if there are no a priori ordering or termination conditions on the intermediate steps and behaviors. We present a flexible framework to enable decentralized multi agent hybrid control and demonstrate its efficacy in a class of multi-region search and rescue scenarios. We also demonstrate the importance of dynamic target modeling at both levels of the hybrid state, i.e. estimating which region targets are in, how search behavior affects this estimate, and how the targets move between and within regions.
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