首页|New Robotics Study Findings Have Been Reported by Investigators at Beijing Institute of Technology (Modeling of a Six-bar Tensegrity Robot Using the Port-hamilt onian Framework and Experimental Validation)

New Robotics Study Findings Have Been Reported by Investigators at Beijing Institute of Technology (Modeling of a Six-bar Tensegrity Robot Using the Port-hamilt onian Framework and Experimental Validation)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Current study results on Robotics have been published. According to news reportingfrom Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Existing tensegrityrobot modeli ng predominantly relies on cable length as the primary control input, making it intractable forimplementation on motor-driven physical systems. In addition, th e current models lack precise formulationsfor intricate environmental interacti ons, such as ground contact forces during deformation and rollingmaneuvers.”Financial support for this research came from National Key Research and Developm ent Program ofChina.The news correspondents obtained a quote from the research from the Beijing Inst itute of Technology,“To bridge these gaps, our study proposes a practicable mod eling approach tailored for six-bartensegrity robots within the Port-Hamiltonia n framework. We address the internal forces stemming frominterconnected bars an d cables by elegantly formulating them as Hamiltonian expressions. Central to our modeling is the versatile ‘port’, encompassing contact and friction forces, an d motor-driven propulsion.These considerations exhibit a broad applicability to cable-driven tensegrity robots, facilitating the straightforwarddeployment of controllers on real-world robotic platforms. The system parameters are identifie dvia experiments on our prototype tensegrity robot, with results aligning close ly with theoretical analyses.”

BeijingPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRobotRoboticsBeijing Institute of Technology

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(MAY.6)