An islands-of-fitness compact genetic algorithm approach to improving learning time in swarms of flapping-wing micro air vehicles

Abstract

Insect-Scale Flapping-Wing Micro-Air Vehicles (FW-MAVs) may be particularly sensitive to degradation of pose and position control caused by ongoing or pre-existing damage to the airframes. Previous work demonstrated that in-flight recovery of sufficient pose and position control precision via use of an adaptive oscillator component inside traditional SISO controllers. This work will replace previously used oscillator learning algorithms with a hyperplane sampling Evolutionary Algorithm (EA) that employs cross-vehicle islands-of-fitness. It will be demonstrated that this strategy allows swarms of vehicles to cooperatively, and more quickly, find and correct for simulate manufacturing errors that appear in all vehicles - even in the presence of randomized vehicle specific errors that are not common to all vehicles in the swarm. The paper will present specific simulation results demonstrating efficacy of this scheme and discussion of future applications of islands-of-fitness methods in this problem domain. © 2013 Springer-Verlag.