首页|Studies from Shanghai Jiao Tong University Yield New Information about Robotics (Toolpath Smoothing With Reduced Curvature and Synchronized Motion for Hybrid Robots)

Studies from Shanghai Jiao Tong University Yield New Information about Robotics (Toolpath Smoothing With Reduced Curvature and Synchronized Motion for Hybrid Robots)

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2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Robotics. According to news reporting from Shanghai, People’s Republic of China, by NewsRx journalists, research stated, “G01 commands are the most commonly used toolpath format in Computer Numerical Control (CNC) machining. Due to the discontinuity between adjacent linear segments, toolpath smoothing is necessary to improve machining efficiency and machined surface quality.” Financial support for this research came from National Natural Science Foundation of China (NSFC). The news correspondents obtained a quote from the research from Shanghai Jiao Tong University, “This paper proposes an analytical C3 continuous local toolpath smoothing and synchronization method for a five-axis hybrid robot. Position and orientation smoothing are decoupled, with quintic B-spline segments replacing the original linear segments. The curvatures of the toolpath are reduced under constrained smoothing errors by weaving transition splines along the linear segments instead of confining them within the corners. A synchronization method based on arc length parameterization tailored to the smoothing method is also proposed. Tool tip position and tool axis orientation are synchronized using another monotonic, C3 continuous spline, which achieves smoother joint motion along the toolpath. For the hybrid robot with highly nonlinear kinematics, improved results in feedrate scheduling considering joint constraints can be realized.”

ShanghaiPeople’s Republic of ChinaAsiaEmerging Tech- nologiesMachine LearningNano-robotRobotRoboticsShanghai Jiao Tong University

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Feb.2)
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