Morphological optimization for tensegrity quadruped locomotion

Abstract

The increasing complexity of soft and hybrid-soft robots highlights the need for more efficient methods of minimizing machine learning solution spaces, and creative ways to ease the process of rapid prototyping. In this paper, we present an initial exploration of this process, using hand-chosen morphologies. Four different choices of muscle groups will be actuated on a tensegrity quadruped called MountainGoat: three for a primarily spine-driven morphology, and one for a primarily leg-driven morphology, and the locomotion speed will be compared. Each iteration of design seeks to reduce the total number of active muscles, and consequently reduce the dimensionality of the problem for machine learning, while still producing effective locomotion. The reduction in active muscles seeks to simplify future rapid prototyping of the robot.