首页|Studies in the Area of Robotics Reported from Beihang University (Composite Dist urbance Filtering for Interaction Force Estimation With Online Environmental Sti ffness Exploration)
Studies in the Area of Robotics Reported from Beihang University (Composite Dist urbance Filtering for Interaction Force Estimation With Online Environmental Sti ffness Exploration)
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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 originating from Hangzhou, People's Republic of China,by NewsRx correspondents, research stated, "The primary focus of this article is the interaction force estimation of robotic manipulators with environmental s tiffness identification and exploration. This issue is particularly crucial in m inimally invasive surgery, where the interaction force between the end effector and the soft tissues needs to be estimated." Funders for this research include National Natural Science Foundation of China ( NSFC), Natural Science Foundation of Zhejiang Province, Defense Industrial Techn ology Development Program of China, Key Research and Development Program of Zhej iang Province. Our news journalists obtained a quote from the research from Beihang University, "Existing methods primarily focus on observer design, exploiting only the infor mation from the robot dynamics. To utilize more information, a unified force est imation framework is proposed in this article, where the robot dynamics and forc e generation model are simultaneously taken into account. Specifically, a robot- environment coupled system model is established by regarding the interaction for ce as a state-coupled disturbance of the robot system. Based on this, a separabi lity analysis for the interaction force is conducted. To cope with the unknown s tiffness parameter and stochastic uncertainties, a novel composite disturbance f iltering scheme is developed. An expectation-maximization-based environmental st iffness exploration force observer (EEFO) is constructed for simultaneous enviro nmental stiffness identification and interaction force estimation. The performan ce of the proposed EEFO is evaluated via numerical simulations and experimental tests on a surgical robot platform."
HangzhouPeople's Republic of ChinaAs iaEmerging TechnologiesMachine LearningRobotRoboticsBeihang University
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