Evolving Control for Micro Aerial Vehicles (MAVs)

This paper further explores the use of a genetic algorithm for the purposes of evolving the control systems for distributed micro aerial vehicles (MAVs). The control systems are essentially evolved rule sets which take into account target surveillance area coverage and built in sensors in the MAV. As in previous work where the overall task is distributed, the same rule set is contained in each MAV. The primary extension to previous work is to make the MAV representation and fitness function evaluation more realistic for a more useful rule set evolution. Secondary extensions would be to parallelize the rule set evolution process.