Model-Predictive Control with Inverse Statics Optimization for Tensegrity Spine Robots
For researchers working on tensegrity robots, this work provides the first feasible closed-loop control solutions, addressing a key bottleneck in controlling these high-dimensional nonlinear systems.
The paper presents two controllers for tensegrity spine robots using model-predictive control and inverse statics optimization, achieving low tracking error and noise insensitivity in simulations. This is the first closed-loop control of such structures.
Robots with flexible spines based on tensegrity structures have potential advantages over traditional designs with rigid torsos. However, these robots can be difficult to control due to their high-dimensional nonlinear dynamics and actuator constraints. This work presents two controllers for tensegrity spine robots, using model-predictive control (MPC) and inverse statics optimization. The controllers introduce two different approaches to making the control problem computationally tractable. The first utilizes smoothing terms in the MPC problem. The second uses a new inverse statics optimization algorithm, which gives the first feasible solutions to the problem for certain tensegrity robots, to generate reference input trajectories in combination with MPC. Tracking the inverse statics reference input trajectory significantly reduces the number of tuning parameters. The controllers are validated against simulations of two-dimensional and three-dimensional tensegrity spines. Both approaches show noise insensitivity and low tracking error, and can be used for different control goals. The results here demonstrate the first closed-loop control of such structures.