Memetic Mission Management

Many operations require an area search function, including search-and-rescue, surveillance, hazard detection, structure or site inspection and agricultural spraying. Furthermore, these area search applications often involve varying vehicle and environmental conditions. This article explores using memetic computing to optimize 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. Memetic computing is well suited for this type of application, as it can provide rapid, high-quality solutions for complex problems. New memetic methods for search area decomposition, task allocation and path planning are presented, with simulated and real-world results utilizing the Boeing Vehicle Swarm Technology Laboratory.