Biologically-inspired robotic (biorobotic) platforms have been successfully adapted for engineering use, but it is difficult to extend these platforms' locomotive gaits to meet optimization goals. The gait spaces of biorobotic platforms can be very large, with multiple local optima and intractable numerical models. Further, the time cost of empirical exploration is often prohibitive. Micro-genetic algorithms have been successful in developing inverse kinematics in simulation, optimizing in spaces with numerous local optima, and working quickly to optimize with low numbers of trials, but have not yet been evaluated for online robotic gait development. To address the problem of engineering gait development in a biorobotic space, a micro-genetic algorithm (μGA) is evaluated on a biorobotic pectoral fin platform. The μGA effectively optimizes in the gait space with low time costs, discovering new gaits that optimize thrust force production on the swimming fin. The μGA also reveals parameter tuning strategies for changing propulsive forces. Overall, the μGA framework is shown to be effective at online optimization in a large, complex biorobotic gait space.
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