Evolving control for distributed micro air vehicles

We focus on the task of large area surveillance. Given an area to be surveilled and a team of micro air vehicles (MAVs) with appropriate sensors, the task is to dynamically distribute the MAVs appropriately in the surveillance area for maximum coverage based on features present on the ground, and to adjust this distribution over time as changes in the team or on the ground occur. We have developed a system that learn rule sets for controlling the individual MAVs in a distributed surveillance team. Since each rule set governs an individual MAV, control of the overall behavior of the entire team is distributed; there is no single entity controlling the actions of the entire team. Currently, all members of the MAV team utilize the same rule set; specialization of individual MAVs through the evolution of unique rule sets is a logical extension to this work. A genetic algorithm is used to learn the MAV rule sets