Design and Experimental Learning of Swimming Gaits for a Magnetic, Modular, Undulatory Robot

Abstract

Here we developed an experimental platform with a magnetic, modular, undulatory robot (μBot) for studying fish-inspired underwater locomotion. This platform will enable us to systematically explore the relationship between body morphology, swimming gaits, and swimming performance via reinforcement learning methods. The μBot was designed to be easily modifiable in morphology, compact in size, easy to be controlled and inexpensive. The experimental platform also included a towing tank and a motion tracking system for real-time measurement of the μBot kinematics. The swimming gaits of μBot were generated by a central pattern generator (CPG), which outputs voltage signals to μBot's magnetic actuators. The CPG parameters were learned experimentally using the parameter exploring policy gradient (PGPE) method to maximize swimming speed. In the experiments, two μBot designs with the same body morphology but different caudal-fin shapes were tested. Results showed that swimming gaits with back-propagating traveling waves can be learned experimentally via PGPE, while the shape of the caudal fins had moderate influences on the learned gaits and the swimming speed. Furthermore, robot swimming speed was sensitive to the undulating frequency and the voltage magnitude of the last three posterior actuators. In contrast, swimming gaits and speed were relatively invariant to the variances within the inter-module connection weights of CPG and the voltage applied to the anterior actuator.