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A platform for the direct hardware evolution of quadcopter controllers

We describe an experimental platform that uses an evolutionary algorithm to automatically tune the gains of a cascaded PID quadcopter controller. All parameters are tuned simultaneously, few platform assumptions are necessary, and no modeling is required. The platform is able to run back-to-back experiments for over 24 hours without human intervention. In a sample experiment, we apply the system to solve a hovering task - the behaviors generated by an initially-random population of gain vectors are evaluated and gradually improved, with the attainment of high fitness hover controllers reported within 12 hours.