First published 2 September 2013
Exploiting Dynamical Complexity in a Physical Tensegrity Robot to Achieve Locomotion
Mark Khazanov, Ben Humphreys, William Keat, John Rieffel
The emerging field of morphological computation seeks to understand how mechanical complexity in living systems can be advantageous, for instance by reducing the cost of control. In this paper we explore the phenomenon of morphological computation in tensegrities—unique structures with a high strength to weight ratio, resilience, and an ability to change shape. These features have great value as a robotics platform, but also make tensegrities difficult to control via conventional techniques. We describe a novel approach to the control of tensegrity robots which, rather than suppressing complex dynamics, exploits them in order to achieve locomotion. Our robots are physically embodied (rather than simulated), evolvable, and locomote at higher speeds (relative to body size) and with fewer actuators than those controlled by more conventional approaches.