Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1632598

Tension stiffened and tendon actuated manipulator

Doggett, William R.·Dorsey, John T.·Jones, Thomas C.·King, Bruce D.
View original source ↗Read source document ↗Export metadata ↓

Abstract

A manipulator includes first and second link arms which are connected together by a hinge. A cable actuation and tensioning system is provided between the link arms and includes a spreader arm, a driven element attached to each link arm and a cable engaged with the respective driven element and attached to the spreader arm. At least one of the arms, or all of the arms, can telescope to shorten or lengthen the arm(s) by movement of the system. The spreader arm may also be configured to slide relative to the first and second link arms to change the position of the spreader arm relative to the first and second link arms.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Doggett, William R., Dorsey, John T., Jones, Thomas C., King, Bruce D.. 2020-02-25. Tension stiffened and tendon actuated manipulator. https://www.osti.gov/biblio/1632598

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

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗