A Platform That Directly Evolves Multirotor Controllers
描述了一个利用差分进化自动发现高性能多旋翼控制器的实验平台，所有参数同时调优，无需建模，在真实多旋翼上演化，保证控制器实际可用。
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.