Towards bridging the reality gap between tensegrity simulation and robotic hardware

Mirletz, Brian T.; Park, Inwon; Quinn, Roger D.; SunSpiral, Vytas

doi:10.1109/iros.2015.7354134

Cited by 28 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tensegrity simulation tools, such as the NASA Tensegrity Robotics toolkit [25], could be adapted to design and even evolve [20] modular tensegrity robots.…”

Section: Discussionmentioning

confidence: 99%

Bio-inspired Tensegrity Soft Modular Robots

Zappetti

Mintchev

Shintake

et al. 2017

Biomimetic and Biohybrid Systems

View full text Add to dashboard Cite

In this paper, we introduce a design principle to develop novel soft modular robots based on tensegrity structures and inspired by the cytoskeleton of living cells. We describe a novel strategy to realize tensegrity structures using planar manufacturing techniques, such as 3D printing. We use this strategy to develop icosahedron tensegrity structures with programmable variable stiffness that can deform in a three-dimensional space. We also describe a tendon-driven contraction mechanism to actively control the deformation of the tensegrity modules. Finally, we validate the approach in a modular locomotory worm as a proof of concept.

show abstract

“…Tensegrity simulation tools, such as the NASA Tensegrity Robotics toolkit [25], could be adapted to design and even evolve [20] modular tensegrity robots.…”

Section: Discussionmentioning

confidence: 99%

Bio-inspired Tensegrity Soft Modular Robots

Zappetti

Mintchev

Shintake

et al. 2017

Biomimetic and Biohybrid Systems

View full text Add to dashboard Cite

show abstract

“…Model-based closed-loop control has been mostly limited to low-dimensional structures [23], [24], [25], [13], [26]. More complex and high dimensional systems have been addressed with model-free methods [16], [22], [18], [27], [28], [29] or open-loop control [30], [31], [32], [20]. In order to use a tensegrity spine with Laika, a model-based closed-loop tracking controller was developed by the authors in [12] and is improved upon in this work.…”

Section: A Tensegrity Robots and Controlmentioning

confidence: 99%

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

Sabelhaus

Zhao

Zhu

et al. 2021

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

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.

show abstract

“…Figure 2 illustrates the capabilities of SUPERball. This is the platform we focus on in the current paper, given a model of the robot in a physics engine (SunSpiral, 2012), which has been verified against the physical prototype (Mirletz et al, 2015b). Motion planning for such systems is needed to perform tasks with long horizons, such as goal-directed navigation, obstacle-avoiding locomotion, or purposeful deformation of a tensegrity robot.…”

Section: Tensegrity Roboticsmentioning

confidence: 99%

Kinodynamic planning for spherical tensegrity locomotion with effective gait primitives

Littlefield

Surovik

Vespignani

et al. 2019

The International Journal of Robotics Research

View full text Add to dashboard Cite

Tensegrity-based robots can achieve locomotion through shape deformation and compliance. They are highly adaptable to their surroundings, and are lightweight, low cost, and physically robust. Their high dimensionality and strongly dynamic nature, however, can complicate motion planning. Efforts to date have primarily considered quasi-static reconfiguration and short-term dynamic motion of tensegrity robots, which do not fully exploit the underlying system dynamics in the long term. Longer-horizon planning has previously required costly search over the full space of valid control inputs. This work synthesizes new and existing approaches to produce dynamic long-term motion while balancing the computational demand. A numerical process based upon quasi-static assumptions is first applied to deform the system into an unstable configuration, causing forward motion. The dynamical characteristics of the result are then altered via a few simple parameters to produce a small but diverse set of useful behaviors. The proposed approach takes advantage of identified symmetries on the prototypical spherical tensegrity robot, which reduce the number of needed gaits but allow motion along different directions. These gaits are first combined with a standard search method to achieve long-term planning in environments where the developed gaits are effective. For more complex environments, the various motion primitives are paired with the fall-back option of random valid actions and are used by an informed sampling-based kinodynamic motion planner with anytime properties. Evaluations using a physics-based model for the prototypical robot demonstrate that modest but efficiently applied search effort can unlock the utility of dynamic tensegrity motion to produce high-quality solutions.

show abstract

Towards bridging the reality gap between tensegrity simulation and robotic hardware

Cited by 28 publications

References 28 publications

Bio-inspired Tensegrity Soft Modular Robots

Bio-inspired Tensegrity Soft Modular Robots

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

Kinodynamic planning for spherical tensegrity locomotion with effective gait primitives

Contact Info

Product

Resources

About