Mechanism Design and Simulation of the ULTRA Spine: A Tensegrity Robot

Sabelhaus, Andrew P.; Ji, Hao; Hylton, Patrick; Madaan, Yakshu; Yang, ChanWoo; Agogino, Alice M.; Friesen, Jeffrey M.; SunSpiral, Vytas

doi:10.1115/detc2015-47583

Cited by 29 publications

(35 citation statements)

References 34 publications

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“…Point mass locations {a 1 ...a 4 , a 5 } shown in red. In this work, certain coordinates (X-axis and θ, γ rotations) are flipped from the right-hand convention in order to match a simulation environment used in prior research [20], [11], [10].…”

Section: A Vertebra Topologymentioning

confidence: 99%

“…There is one equation in (11) for each position and angle states of the entire robot, i.e., the dimension of eqn. (11) is half the size of the state vector. The constants for each of summations in (8)(9)(10)(11) are summarized in Table II for both models.…”

Section: E Dynamics Solutionmentioning

confidence: 99%

“…The Laika project, named for the first dog in space, is an ongoing research effort to develop a quadruped robot with a flexible, actuated body that can be controlled to track specific movements in closed-loop [10]. Laika is designed to use a flexible, actuated spine as its body, developed as a form of the previously-investigated ULTRA Spine (Underactuated Lightweight Tensegrity Robot Spine) [11], [12]. This work presents two controllers for closed-loop trajectory tracking of Laika's spine using model-predictive control (MPC), extending the benefits of the first controller [12] to a more general approach in the second.…”

Section: Introductionmentioning

confidence: 99%

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

Sabelhaus

Zhao

Zhu

et al. 2021

IEEE Trans. Contr. Syst. Technol.

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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.

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Section: A Vertebra Topologymentioning

confidence: 99%

Section: E Dynamics Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sabelhaus

Zhao

Zhu

et al. 2021

IEEE Trans. Contr. Syst. Technol.

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“…For the sake of exhaustiveness, the direct kinetostatic problem is solved here with  1 not necessarily equal to  2 . Accordingly, the two equations (5) and (6) are used to solve the equilibrium conditions, in which F 3 and L 2 are replaced with F 4 and L 2 , respectively. To get simpler expressions, the second equation is subtracted from the first one.…”

Section: Symmetric Design and Equal Forcesmentioning

confidence: 99%

“…They can also be used as preliminary models in musculo-skeleton systems to analyze animal and human movements [4,5]. A spine can be modelled by stacking a number of suitable tensegrity modules, see for example [6]. Figure 1 illustrates some configurations of a serial assembly of planar tensegrity mechanisms similar to the ones studied in this paper, suitable for a plane model of a spine or of an animal neck.…”

Section: Introductionmentioning

confidence: 99%

Kinetostatic analysis and solution classification of a class of planar tensegrity mechanisms

Wenger¹,

Chablat²

2018

Robotica

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Tensegrity mechanisms are composed of rigid and tensile parts that are in equilibrium. They are interesting alternative designs for some applications, such as modelling musculo-skeleton systems. Tensegrity mechanisms are more difficult to analyze than classical mechanisms as the static equilibrium conditions that must be satisfied generally result in complex equations. A class of planar one-degree-of-freedom tensegrity mechanisms with three linear springs is analyzed in detail for the sake of systematic solution classifications. The kinetostatic equations are derived and solved under several loading and geometric conditions. It is shown that these mechanisms exhibit up to six equilibrium configurations, of which one or two are stable, depending on the geometric and loading conditions. Discriminant varieties and cylindrical algebraic decomposition combined with Groebner base elimination are used to classify solutions as a function of the geometric, loading and actuator input parameters.

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