首页|New Findings from Huazhong University of Science and Technology in the Area of Robotics Reported (Robotic Milling Posture Adjustment Under Composite Constraints: a Weight-sequence Identification and Optimization Strategy)
New Findings from Huazhong University of Science and Technology in the Area of Robotics Reported (Robotic Milling Posture Adjustment Under Composite Constraints: a Weight-sequence Identification and Optimization Strategy)
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NETL
NSTL
Elsevier
New research on Robotics is the subject of a report. According to news reporting originating in Wuhan, People's Republic of China, by NewsRx journalists, research stated, “Industrial robots are widely used for milling complex parts in restricted spaces owing to their multiple degrees of freedom and flexible postures. To plan posture trajectory for robot machining with high precision under multiple constraints, this study establishes composite constraint models with constraint boundary solutions.” Funders for this research include National Key Research and Development Program of China, Basic Science Center of China, National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from the Huazhong University of Science and Technology, “An improved gray relation analysis model is adopted to identify the weight-sequences among the composite constraints. The correlation degrees of the postures of the robot can be dynamically quantified between arbitrary cutter locations by applying weight sequence identification, which is conducive to fulfilling attractive orientations in artificial potential fields. In addition, this study proposes an initial posture determination strategy based on the optimization principle of minimizing the rotated energy in global postures. Consequently, an artificial potential planning model is applied to the implement posture adjustment of the robot end effector.”
WuhanPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsRobotsHuazhong University of Science and Technology
NETL
NSTL
Elsevier
