首页|Findings from Tokyo University of Agriculture and Technology in Robotics Reporte d (Dynamics-Based Control and Path Planning Method for Long-Reach Coupled Tendon -Driven Manipulator)

Findings from Tokyo University of Agriculture and Technology in Robotics Reporte d (Dynamics-Based Control and Path Planning Method for Long-Reach Coupled Tendon -Driven Manipulator)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on robotics have been pr esented. According to news reporting originating from Tokyo, Japan, by NewsRx co rrespondents, research stated, "The Fukushima power station in Japan was affecte d by a major earthquake and tsunami in March 2011, inspecting the primary contai nment vessel remains difficult due to high radioactivity." Funders for this research include Japan Society For The Promotion of Science. The news reporters obtained a quote from the research from Tokyo University of A griculture and Technology: "Long-reach robot arms are useful in inspecting such hazardous environments, and a coupled tendon-driven mechanism enables realizing a long, light-weight, and thin manipulator. However, high elastic elongation of tendons due to gravity may lead to unstable joint control. In this paper, we int roduce dynamics-based control as a feasible strategy for a long-reach tendon-dri ven robotic arm. Additionally, a planning method to identify the joint angle pat h ensuring stability is proposed. Considering stability analysis, the potential due to the tendon elasticity and gravity is evaluated and used as an index of jo int stability." According to the news editors, the research concluded: "The rapidly exploring ra ndom tree is used as the planning algorithm. The effectiveness of the proposed m ethod was demonstrated through the successful manipulation of a 5-kg payload by a 10-m long robotic arm."

Tokyo University of Agriculture and Tech nologyTokyoJapanAsiaEmerging TechnologiesMachine LearningRoboticsR obots

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Mar.6)