Target Area Surveillance Optimization with Swarms of Autonomous Micro Aerial Vehicles

Reconnaissance mission success within deep enemy territory or surveillance of a targeted remote area requiring fast and reliable data collection is many times a daunting task. The characteristics of particular missions dictate the area coverage, objects of interest, timing constraints, and last but not the least, the type of deployed system and its capabilities. The advances in unmanned aerial vehicles (UAV) over the past decade enhanced surveillance/reconnaissance/monitoring maneuvers for both military missions and civilian rescue operations. More recently, the UAV design and development has evolved also towards miniaturizing the actual aerial system and cooperation among a swarm of micro aerial vehicles (MAV). This work focuses on the optimization of a MAV swarm deployment in hostile territory. The reconnaissance mission success is given by the percentage of data collection within timing, distributed data storage, collision avoidance, and swarm MAV mission integrity constraints. A proof of concept simulation was built to evaluate the correctness of the swarm MAV model deployment and serves as the starting point for a larger deployment model simulation.