Adaptive task allocation for search area coverage

Many operations require an area search area function, including search-and-rescue, surveillance, hazard detection, structures or sites inspection and agricultural spraying. Furthermore, these area search applications often involve varying vehicle and environmental conditions. This paper explores the problem of optimizing the behavior of a swarm of heterogeneous robotic vehicles executing a search area coverage task. Each vehicle is equipped with a sensing apparatus and the swarm must collectively explore an occluded environment to achieve a required probability of detection for each location in the search area. The problem is further complicated with the introduction of dynamic vehicle and environmental properties making adaptability a necessary requirement in order to achieve a high level of mission assurance using unmanned vehicles. Novel methods for search space decomposition and task allocation are presented, with simulated and real-world results utilizing the Boeing Vehicle Swarm Technology Laboratory.