Flop and roll: Learning robust goal-directed locomotion for a Tensegrity Robot

Tensegrity robots are composed of compression elements (rods) that are connected via a network of tension elements (cables). Tensegrity robots provide many advantages over standard robots, such as compliance, robustness, and flexibility. Moreover, sphere-shaped tensegrity robots can provide non-traditional modes of locomotion, such as rolling. While they have advantageous physical properties, tensegrity robots are hard to control because of their nonlinear dynamics and oscillatory nature. In this paper, we present a robust, distributed, and directional rolling algorithm, “flop and roll”. The algorithm uses coevolution and exploits the distributed nature and symmetry of the tensegrity structure. We validate this algorithm using the NASA Tensegrity Robotics Toolkit (NTRT) simulator, as well as the highly accurate model of the physical SUPERBall being developped under the NASA Innovative and Advanced Concepts (NIAC) program. Flop and roll improves upon previous approaches in that it provides rolling to a desired location. It is also robust to both unexpected external forces and partial hardware failures. Additionally, it handles variable terrain (hills up to 33% grade). Finally, results are compatible with the hardware since the algorithm relies on realistic sensing and actuation capabilities of the SUPERBall.