Form-finding-based rolling control of the six-bar tensegrity robot

Due to the high-dimensional state space, rolling control of tensegrity robots presents a significant challenge. Existing methods mostly focus on determining how to achieve rolling locomotion but cannot obtain the desired configuration that generates rolling locomotion. To address this issue, we propose a critical configuration search method by introducing the rolling conditions. In our approach, we utilize Particle Swarm Optimization (PSO) technique to carry out the calculation step of the Force Density Method (FDM) which results in the desired Cartesian coordinates of the robot. Subsequently, the trajectory tracking controller is designed by employing the inverse dynamics. The method is validated through numerical simulations and physical experiments on a six-bar tensegrity robot. The results indicate that our method enables an effective search for a desired configuration in terms of the critical configuration and generates expected control inputs to promote rolling locomotion.