Path planning for active tensegrity structures

Porta, Josep M.; Hernández-Juan, Sergi

doi:10.1016/j.ijsolstr.2015.09.018

Cited by 21 publications

(5 citation statements)

References 54 publications

(65 reference statements)

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“…The bounds for the variation rates of ρ 1 and ρ 2 are chosen as ±10 /s, that is, v 11 ≤ _ ρ 1 ≤ v 12 and v 21 ≤ _ ρ 2 ≤ v 22 , where v 11 = v 21 = −10 /s and v 12 = v 22 = 10 /s. A gridding method 41 is used to convert the infinite-dimensional convex optimization problem in Equation (18) to the following finite-dimensional problem:…”

Section: Closed-loop Control Strategy For System Deploymentmentioning

confidence: 99%

“…The quasistatic control technique is an effective approach to regulating motions of various systems such as tensegrity structures, [17][18][19] space systems, 20,21 and robotics. 22,23 This open-loop control method aims to drive system dynamics close to a desired equilibrium path/trajectory, and slow system motion is commonly required to reduce oscillations in system responses.…”

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Deployment of a foldable tensegrity‐membrane system via configuration transitions using linear parameter‐varying control

Yang

2021

Struct Control Health Monit

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Controlled deployment of a foldable tensegrity-membrane system is investigated in this article. A membrane folding technique referred to as the spiral folding pattern is applied to the attached membrane to achieve compact folded shapes for the system. The foldable tensegrity-membrane system experiences transitions between two system configurations (i.e., the tensegrity configuration and the tensegrity-membrane configuration) due to the variation of membrane loading states during system deployment. A closed-loop control strategy consisting of two linear parameter-varying (LPV) controllers is designed based on control-oriented models to regulate system dynamics. A nonlinear finite element model is used to examine the control performance. Senor noise is considered in another nonlinear finite element simulation to examine the robustness of the designed controllers. K E Y W O R D Scontrol-oriented model, equilibrium conditions, foldable tensegrity-membrane systems, LPV control, nonlinear finite element model | INTRODUCTIONFolding and deployment techniques enable structures and architectures of large dimensions to be stowed during transportation and to be deployed at operational states. Such deployable structure concepts meet design requirements of large deployable spacecraft, leading to several successful applications such as the James Webb Space Telescope (JWST) 1 and NASA's Soil Moisture Active Passive (SMAP) spacecraft. 2 Folding mechanisms were employed to stow the primary and secondary mirrors, membrane sunshields, and the overall telescope to ensure that the folded JWST fits the fairing of a launch vehicle. 3 As a key system component of NASA SMAP spacecraft, a 6-m AstroMesh-Lite reflector, consisting of a truss support structure and a membrane surface, could be stowed to reduce its envelope during launch and be deployed in orbit by controlling the shape of the truss support structure. 4 Folding techniques also enable large spacecraft components such as membranes, solar arrays, and thermal protection panels to be folded to achieve compact packaged shapes. Recently, an ultralight deployable space structure composed of independent bending-stiff trapezoid strips and membranes was designed based on a two-stage packaging/folding concept. 5,6 An origami-based folding concept was recently developed to ensure that an aeroshell of a Mars entry vehicle could be stowed efficiently during the launch phase and be deployed during the entry phase to perform vehicle thermal protection. 7 An origami folding pattern was employed in the design of large spacecraft arrays to achieve self-deployment, self-stiffening, and retraction. 8 Membrane folding patterns such as rotationally skew folding, spiral folding, circumferential folding, and curved circumferential

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Section: Closed-loop Control Strategy For System Deploymentmentioning

confidence: 99%

mentioning

confidence: 99%

Deployment of a foldable tensegrity‐membrane system via configuration transitions using linear parameter‐varying control

Yang

2021

Struct Control Health Monit

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“…Dynamic tensegrity locomotion involves numerous complex factors, such as a varying contact interface with the ground, that complicate the use of model-based control. Sampling-based motion planning, which has been used for quasi-static deformation [9], can also be applied to design kinodynamic paths that demonstrate the aptitude of tensegrities on complex terrain [10]. The open-loop nature of this approach, however, is inevitably fragile to errors, and it is too computationally costly to be re-applied online.…”

Section: A Related Workmentioning

confidence: 99%

Adaptive Tensegrity Locomotion on Rough Terrain via Reinforcement Learning

Surovik,

Wang,

Bekris

2018

Preprint

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The dynamical properties of tensegrity robots give them appealing ruggedness and adaptability, but present major challenges with respect to locomotion control. Due to high-dimensionality and complex contact responses, data-driven approaches are apt for producing viable feedback policies. Guided Policy Search (GPS), a sample-efficient and model-free hybrid framework for optimization and reinforcement learning, has recently been used to produce periodic locomotion for a spherical 6-bar tensegrity robot on flat or slightly varied surfaces. This work provides an extension to non-periodic locomotion and achieves rough terrain traversal, which requires more broadly varied, adaptive, and non-periodic rover behavior. The contribution alters the control optimization step of GPS, which locally fits and exploits surrogate models of the dynamics, and employs the existing supervised learning step. The proposed solution incorporates new processes to ensure effective local modeling despite the disorganized nature of sample data in rough terrain locomotion. Demonstrations in simulation reveal that the resulting controller sustains the highly adaptive behavior necessary to reliably traverse rough terrain.

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“…Koohestani (2015) developed an iterative method for reshaping free standing and restrained tensegrity structures. Porta and Hernández-Juan (2016) proposed a method based on differential geometry for obtaining an equilibrium manifold from the infinitesimal mechanisms. However, there is no literature referring to these methods having been used in real applications.…”

Section: Introductionmentioning

confidence: 99%

Reconfiguration of multi-stage tensegrity structures using infinitesimal mechanisms

González

Luo

Shi

2019

Lat. Am. j. solids struct.

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The use of tensegrity structures in soft robotics has seen an increased interest in recent years thanks to their mechanical properties, but the control of these systems remains an open problem. This paper presents a reconfiguration strategy for actuated multi-stage tensegrity structures. The algorithm works on the principle of using the infinitesimal mechanisms of the structure to generate a path of positions along which a multistage tensegrity structure can change its shape while maintaining the self-equilibrium. Combining the force density method with a marching procedure, the solution to the equilibrium problem is given by a set of differential equations that define the kinematic constraints of the structure. Beginning from an initial stable position, the algorithm calculates a small displacement until a new stable configuration is reached, and recurrently repeats the process during a given interval of time. By means of three numerical examples, we show the efficacy of our algorithm for reconfiguring a two-stage tensegrity mast along different directions.

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Path planning for active tensegrity structures

Cited by 21 publications

References 54 publications

Deployment of a foldable tensegrity‐membrane system via configuration transitions using linear parameter‐varying control

Deployment of a foldable tensegrity‐membrane system via configuration transitions using linear parameter‐varying control

Adaptive Tensegrity Locomotion on Rough Terrain via Reinforcement Learning

Reconfiguration of multi-stage tensegrity structures using infinitesimal mechanisms

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