Enhancing Manueverability via Gait Design

Locomotion gait design typically focuses on optimizing for various notions of efficiency, such as cost of transport or speed. Equally important is the ability to modulate a gait (whether optimal or not) to steer the system. In drag-dominated locomotion, geometric mechanics provides an elegant and practical framework for both goals---gait design and gait modulation. This framework gives tools for approximating the net displacement of robotic systems over cyclic gaits and optimizing for the most efficient gaits according to a user-specified cost. In this work, we propose both local and global gait morphing algorithms for modulating a nominal gait to provide efficient, single-parameter steering control. Using a simplified swimmer, we numerically compare the two approaches and show that for modest turns, the local approach, while suboptimal, nevertheless proves effective for steering control. A potential advantage of the local approach is that it can be readily applied to soft robots or other systems where local approximations to the constraint curvature can be garnered from data, but for which obtaining an exact global model is infeasible. This work was also submitted to ICRA 2022 (in review).