首页|Tiangong University Reports Findings in Androids (Operational space robust imped ance control of the redundant surgical robot for minimAlly invasive surgery)

Tiangong University Reports Findings in Androids (Operational space robust imped ance control of the redundant surgical robot for minimAlly invasive surgery)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Robotics - Androids is the subject of a report. According to news originating from Tianjin, People's R epublic of China, by NewsRx correspondents, research stated, "The motion accurac y, compliance, and control smoothness for the surgical robot are of great import ance to improve the safety of human-robot interaction. However, the end effector that interacts with soft tissue during surgery affects the dynamics of the robo t." Our news journalists obtained a quote from the research from Tiangong University , "The control performance of the controller may be decreased if the changing dy namics are not identified and updated in time. This paper proposes a robust impe dance controller for the redundant remote center of motion manipulator influence d by external disturbances, including external torque, uncertainties, and unmode led terms in the dynamics. To achieve the desired impedance, a continuously swit ching sliding manifold is proposed. When the sliding manifold is driven to zero, the motion error will converge to a bounded region. This can overcome the adver se effects of external disturbances while guaranteeing motion accuracy and compl iance. Chattering of the sliding mode control is Alleviated through the formulat ed continuously switching sliding manifold and integrated nonlinear disturbance observer. Simulations and experiments demonstrate that the proposed controller h as excellent motion accuracy, compliance, and control smoothness."

TianjinPeople's Republic of ChinaAsi aAndroidsEmerging TechnologiesHealth and MedicineHuman-Robot InteractionMachine LearningRobotRoboticsSurgery

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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年,卷(期):2024.(Sep.30)