Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2311.08942

Thermally-Resilient Soft Gripper for On-Orbit Operations

Fernando Ruiz·Begona Arrue·Anibal Ollero
View original source ↗Read source document ↗Export metadata ↓

Abstract

Research in soft manipulators has significantly enhanced object grasping capabilities, thanks to their adaptability to various shapes and sizes. Applying this technology to on-orbit servicing, especially during the capture and containment stages of active space debris removal missions, might offer a secure, adaptable, and cost-effective solution compared to the trend of increasing the degrees of freedom and complexity of the manipulator (e.g. ClearSpace, Astroscale). This work aims to conduct an experimental proof of concept, for which challenges such as radiation, vacuum, and microgravity are significant, but the predominant issue is ensuring effective operation in the extreme temperature swings, where flexible materials may exhibit cryogenic crystallization or drastic shifts in their elasticity. This work addresses this challenge through an initial stage of analytical modeling of the thermal dynamics inside the manipulator in orbit; which is then used for the development of a first experimental prototype tested with liquid nitrogen and heat guns. The multi-layered design for Low Earth Orbit (LEO) leverages the properties of TPU at low infill rates for lightweight inherent flexibility, silicone rubber ensuring structural integrity, PTFE (Teflon) for unparalleled thermal stability, and aerogel for insulation. The tendon-actuated servo-driven gripper is tested in the laboratory by varying the shape and size of objects during the grasping. The results, based on servomotor force metrics to assess the flexible manipulator's adaptability and object capture efficiency across temperature changes, affirm the concept's viability. Forces increase up to 220$\%$ in cryogenic conditions and decrease by no more than 50$\%$ at high temperatures.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fernando Ruiz, Begona Arrue, Anibal Ollero. 2024-03-25. Thermally-Resilient Soft Gripper for On-Orbit Operations. https://arxiv.org/abs/2311.08942

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Co-Investment with Payoff-Sharing Mechanism for Cooperative Decision-Making in Network Design Games

Network-based systems are inherently interconnected, with the design and performance of subnetworks being interdependent. However, the decisions of self-interested operators may lead to suboptimal outcomes for users and the overall system. This paper explores cooperative mechanisms that can simultaneously benefit both operators and users. We address this challenge using a game-theoretical framework that integrates both non-cooperative and cooperative game theory. In the non-cooperative stage, we propose a network design game in which subnetwork decision-makers strategically design local infrastructures. In the cooperative stage, co-investment with payoff-sharing mechanism is developed to enlarge collective benefits and fairly distribute them. To demonstrate the effectiveness of our framework, we conduct case studies on the Sioux Falls network and real-world public transport networks in Zurich and Winterthur, Switzerland. Our evaluation considers impacts on environmental sustainability, social welfare, and economic efficiency. The proposed framework provides a foundation for improving interdependent networked systems by enabling strategic cooperation among self-interested operators.

eess.SY↗
arXiv2026

Generalizable Optimal Control with Transformers: One Policy Across Diverse Systems

Classical optimal control designs a separate controller for each plant. Even for the Linear Quadratic Regulator (LQR), every new model must be identified and its Riccati equation re-solved. We ask whether a single learned policy can instead serve an entire family of systems, and we show that one transformer can. We train the policy to imitate optimal LQR state feedback across a collection of heterogeneous Multiple-Input, Multiple-Output (MIMO) Linear Time-Invariant (LTI) systems that differ in their state and input dimensions and in their cost objectives. A shared representation lets the same parameters control every member of the family. It combines system-wise standardization, zero-padding and masking across dimensions, and an explicit encoding of the cost matrices. At run time, the policy maps a short window of recent states and the specified cost to a control action. It uses no plant matrices and identifies the dynamics implicitly from the state history. We evaluate on $28$ simulated systems over $9{,}675$ closed-loop rollouts, and no unstable rollout was observed in any of them. On the systems seen during training, it attains a median relative sub-optimality of $0.022\%$, even under parameter perturbations of up to $\pm10\%$. It transfers to unseen systems with lightweight fine-tuning, reaching a median sub-optimality of $0.19\%$. These results support transformers as generalizable near-optimal controllers for structured families of linear systems.

eess.SY↗
arXiv2026

Two-Timescale Asymptotic Simulations of Hybrid Inclusions with Applications to Stochastic Hybrid Optimization

Convergence properties of model-free two-timescale asymptotic simulations of singularly perturbed hybrid inclusions are developed. A hybrid inclusion combines constrained differential and difference inclusions to capture continuous (flow) and discrete (jump) dynamics, respectively. Sufficient conditions are established under which sequences of iterates and step sizes constitute a two-timescale asymptotic simulation of such a system, with limiting behavior characterized via weakly invariant and internally chain-transitive sets of an associated boundary layer and reduced system. To illustrate the applicability of these results, conditions are given under which a two-timescale stochastic approximation of a hybrid optimization algorithm asymptotically recovers the behavior of its deterministic counterpart.

eess.SY↗