Feasible Rolling Trajectory Generation and Control for Tensegrity Robots

Abstract

Due to the multi-node and multi-contact motion characteristics of tensegrity robots, existing methods fail to generate feasible reference rolling trajectories, and controllers are also limited to open-loop approaches. To address this issue, we utilize motion decomposition to extract the motion phase that should be the primary focus. Subsequently, we propose a method combining form-finding-based critical configuration search and polynomial trajectories to generate feasible trajectories. Then, an iLQR controller that accounts for reducing actuator load is designed for trajectory tracking control. A key distinction from existing methods is that our approach eliminates the need for reset operations after each rolling cycle. The results of simulations and physical experiments demonstrate that the robot achieves continuous rolling, with improvements of 18.3% in speed and 34.4% in actuation load compared to existing works.