Model-Predictive Control With Inverse Statics Optimization for Tensegrity Spine Robots

Abstract

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. To overcome these issues, this work presents two controllers for tensegrity spine robots, using model-predictive control (MPC), and demonstrates the first closed-loop control of such structures. The first of the two controllers is formulated using only state tracking with smoothing constraints. The second controller, newly introduced in this work, tracks both state and input reference trajectories without smoothing. The reference input trajectory is calculated using a rigid-body reformulation of the inverse kinematics of tensegrity structures, and introduces the first feasible solutions to the problem for certain tensegrity topologies. This second controller significantly reduces the number of parameters involved in designing the control system, making the task much easier. The controllers are simulated with 2D and 3D models of a particular tensegrity spine, designed for use as the backbone of a quadruped robot. These simulations illustrate the different benefits of the higher performance of the smoothing controller versus the lower tuning complexity of the more general input-tracking formulation. Both controllers show noise insensitivity and low tracking error, and can be used for different control goals. The reference input tracking controller is also simulated against an additional model of a similar robot, thereby demonstrating its generality.

In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs

“…Most importantly, future work can incorporate these models as part of predictive control or planning algorithms for positioning of untethered soft limbs. Dynamics models are needed for advanced techniques such as model predictive control for soft robots ( Sabelhaus et al, 2021 ), which demonstrate advantages in robustness and safety over model-free approaches. Whether robot designs include the proposed temperature sensor, or simply use PWM duty cycle in the neural network, our results imply that predictions are possible.…”

TsgFEM: Tensegrity Finite Element Method

“…Moreover, the many advantages of tensegrity have also attracted researchers to find new ways to design soft robotics, i.e., six-bar tensegrity robot (Booth et al, 2020;K. Wang et al, 2020), robotic spine (Sabelhaus et al, 2020), morphing wings (M. , robotic fish (B. Chen & Jiang, 2019), debris capturing robot (Feng et al, 2021). The statics and dynamics analysis of tensegrity structures is essential to get insight into the structure properties.…”

Trajectory Optimization for Thermally-Actuated Soft Planar Robot Limbs

“…Trajectory optimization offers two distinct benefits in comparison to feedback without pre-planned trajectories [15]. First, an optimized trajectory verifies dynamic feasibility of a motion a-priori, a common requirement and significant challenge for many state-feedback techniques (such as modelpredictive control [16]) in soft and flexible robots. Second, trajectory optimization allows for creating feasible trajectories for different goals: for example, minimum energy expenditure versus minimum time [15].…”