首页|New Data from Nanjing University of Aeronautics and Astronautics Illuminate Find ings in Robotics (Enabling Collaborative Assembly Between Humans and Robots Usin g a Digital Twin System)
New Data from Nanjing University of Aeronautics and Astronautics Illuminate Find ings in Robotics (Enabling Collaborative Assembly Between Humans and Robots Usin g a Digital Twin System)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Robotics. According to news reporting originating from Nanjing, People's Republi c of China, by NewsRx correspondents, research stated, "Human-robot collaboratio n (HRC) systems are intelligent systems that guide robots to collaborate with hu mans based on a cognitive understanding of human intention, ensuring safe, flexi ble, and efficient collaboration between humans and robots in shared workspaces. In industrial settings, the current methods for constructing a human digital tw in model rely on motion capture devices that require personnel to wear cumbersom e equipment, which goes against the principle of flexible interaction advocated for HRC." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), China Postdoctoral Science Foundation, Jiangsu Provincial Po st-doctoral Excellence Program. Our news editors obtained a quote from the research from the Nanjing University of Aeronautics and Astronautics, "Furthermore, the current methods do not model humans and robots in a unified space, which is both unintuitive and inconvenient for perceiving and understanding the overall environment. To address these limi tations, this paper proposes a digital twin system for HRC. This system facilita tes the construction of a digital twin scene, the mapping from the real space to the virtual space, and the planning and execution of collaborative strategies f rom the virtual to the real space. Designed explicitly for common workstation se ttings, a robust human mesh recovery algorithm is introduced to address the chal lenge of reconstructing occluded human bodies. Additionally, uncertainty estimat ion is employed to enhance the action recognition algorithm, ensuring a controll able level of risk in the recognition process. Experimental results demonstrate the superiority of the proposed methods over baseline methods."
NanjingPeople's Republic of ChinaAsi aEmerging TechnologiesMachine LearningNano-robotRoboticsNanjing Univer sity of Aeronautics and Astronautics
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