Evolutionary Controller Design for Area Search using Multiple UAVs with Minimum Altitude Maneuver

Simultaneous operation of multiple UAVs enables to enhance the mission accomplishment efficiency. In order to achieve this, easily scalable control algorithms are required, and swarm intelligence having such characteristics as flexibility, robustness, decentralized control, and self-organization based on behavioral model comes into the spotlight as a practical alternative. Recently, evolutionary robotics is applied to the control of UAVs to overcome the weakness of difficulties in the logical design of behavioral rules. In this paper, the neural net controller evolved by evolutionary robotics approach is applied to the control of multiple UAVs which have the mission of searching limited area as much as possible. By applying incremental evolution technique we could evolve a neural net controller which can minimize energy consumption without sacrificing the performance of area coverage and collision avoidance.