Abstract
We describe an experimental platform that uses differential evolution to automatically discover high-performance multirotor controllers. All control parameters are tuned simultaneously, no modeling is required, and, as the evolution occurs on a real multirotor, the controllers are guaranteed to work in reality. The platform is able to run back-to-back experiments for over a week without human intervention. Self-adaptive rates are shown improve solution fitness whilst (at least) maintaining convergence times. This platform is the first to allow for evolutionary robotics experimentation to occur safely and repeatedly on real multirotors. High-performance controllers are evolved despite noisy fitness evaluations, real-world sensory noise, low population sizes, and limited numbers of evolutionary generations.
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