首页|Research from Hubei University of Technology in Robotics Provides New Insights (Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating)

Research from Hubei University of Technology in Robotics Provides New Insights (Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating)

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A new study on robotics is now available. According to news reporting out of Wuhan, People’s Republic of China, by NewsRx editors, research stated, “At present, aircraft radome coating cleaning mainly relies on manual and chemical methods.” Financial supporters for this research include Hubei Natural Science Foundation; Wuhan Key Research And Development Plan. The news editors obtained a quote from the research from Hubei University of Technology: “In view of this situation, this study presents a trajectory planning method based on a three-dimensional (3D) surface point cloud for a laser-enabled coating cleaning robot. An automated trajectory planning scheme is proposed to utilize 3D laser scanning to acquire point cloud data and avoid the dependence on traditional teaching-playback paradigms. A principal component analysis (PCA) algorithm incorporating additional principal direction determination for point cloud alignment is introduced to facilitate subsequent point cloud segmentation. The algorithm can adjust the coordinate system and align with the desired point cloud segmentation direction efficiently and conveniently. After preprocessing and coordinate system adjustment of the point cloud, a projection-based point cloud segmentation technique is proposed, enabling the slicing division of the point cloud model and extraction of cleaning target positions from each slice. Subsequently, the normal vectors of the cleaning positions are estimated, and trajectory points are biased along these vectors to determine the end effector’s orientation.”

Hubei University of TechnologyWuhanPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRobotics

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Feb.19)
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