Simplifying Gait Design via Shape Basis Optimization

Abstract

Gaits are crucial to the performance of locomotors. However, it is often difficult to design effective gaits for complex locomotors. Geometric mechanics offers powerful gait design tools, but the utilities of these tools have been limited to systems with two joints. Using shape basis functions, it is possible to approximate the kinematics of complex locomotors using only two shape variables. As a result, the tools of geometric mechanics can be used to study complex locomotion in an intuitive way. The choice of shape basis functions plays an important role in determining gait kinematics, and therefore the performance of a locomotor. To find appropriate basis functions, we introduce the shape basis optimization algorithm, an algorithm that iteratively improve basis functions to find effective kinematic programs. Applying this algorithm to a snake robot resulted a novel gait, which improves its speed of swimming in granular materials.