首页|Study Data from Guangdong Academy of Science Provide New Insights into Robotics (Uncertainty-aware Error Modeling and Hierarchical Redundancy Optimization for Robotic Surface Machining)
Study Data from Guangdong Academy of Science Provide New Insights into Robotics (Uncertainty-aware Error Modeling and Hierarchical Redundancy Optimization for Robotic Surface Machining)
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NETL
NSTL
Elsevier
Investigators publish new report on Robotics. According to news originating from Guangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Industrial robots are commonly utilized in in-situ machining, but their inherent limitations in stiffness and positioning accuracy can pose challenges in achieving precise freeform surface milling. Previous research primarily focused on robot stiffness optimization and positioning error prediction separately, neglecting the uncertainty of robot errors and the modeling of surface profile errors.”
GuangzhouPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsRobotsGuangdong Academy of Science
NETL
NSTL
Elsevier
