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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.

Automated driving systems (ADSs) allow vehicles to engage in self-driving under specific conditions. Along with the potential safety benefits, the increase in productivity through non-driving-related tasks (NDRTs) is often cited as a motivation behind the adoption of ADSs. Although advances have been made in understanding both the promotion of ADS trust and its impact on NDRT performance, the influence of risk remains largely understudied. To fill this gap, we conducted a within-subjects experiment with 37 licensed drivers using a simulator. Internal risk was manipulated by ADS reliability and external risk by visibility, producing a 2 (ADS reliability) × 2 (visibility) design. The results indicate that high reliability increases ADS trust and further enhances the positive impact of ADS trust on NDRT performance, while low visibility reduces the negative impact of ADS trust on driver monitoring. Results also suggest that trust increases over time if the system is reliable and that visibility did not have a significant impact on ADS trust. These findings are important for the design of intelligent ADSs that can respond to drivers' trusting behaviors.

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