Conditioned Basis Array Factorization: An Approach to Gait Pattern Extraction

Abstract

Snakes locomote through sophisticated coordinated motions of their many degrees of freedom (DoFs). The exhibited regularity of their body undulation implies the existence of low dimensional representations of snake gaits. We posit that investigating the underlying motion patterns will lead to insights for understanding how animals control low-level joint motions in a coupled fashion to achieve behavior-level control targets. To study snake motions in a concise way, we develop a novel modal decomposition algorithm called conditioned basis array factorization (CBAF). Unlike most modal decomposition algorithms, CBAF uses analytical bases which can be identified with temporal, spatial, and behavioral (eg, moving in a straight line, turning, etc.) components of snake motions. Applying CBAF to shape change data collected from a series of snake behaviors results in analytical representations of the recorded motions. These analytical representations provide insight into biological system models, as well as generate families of gaits for snake robots. Although this work focuses on snakes, the generality of the analysis techniques suggest that a similar approach can be used as an effective motion generation technique for any system whose locomotion is kinematic in nature.