首页|Harbin Institute of Technology Researcher Highlights Research inRobotic Systems (A fast collision detection method based on pointclouds and stretched primitiv es for manipulator obstacle-avoidancemotion planning)

Harbin Institute of Technology Researcher Highlights Research inRobotic Systems (A fast collision detection method based on pointclouds and stretched primitiv es for manipulator obstacle-avoidancemotion planning)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on ro botic systems. According to news originatingfrom Harbin, People’s Republic of C hina, by NewsRx correspondents, research stated, “It is essentialto efficiently perform collision detection for robotic manipulators obstacle-avoidance plannin g. Existingmethods are excellent when manipulator links are simple and obstacle s are convex.”Financial supporters for this research include Natural Science Foundations of Ch ina; Yunnan PowerGrid Company.The news reporters obtained a quote from the research from Harbin Institute of T echnology: “Butthey cannot keep the accuracy and the efficiency at the same tim e when manipulator links or obstaclesare nonconvex. To decrease the computing t ime and keep a high accuracy, this article presents a collisiondetection method based on point clouds and stretched primitives (PCSP). In traditional methods, obstaclesare often represented either by a convex body or enormous amounts of p oints. But this needs a trade-offbetween the accuracy and the computing time wh en obstacles are concave. In the proposed method, werepresent obstacles and com plex manipulator links as stretched geometric bodies while simple manipulatorli nks are enclosed by capsules with different sizes. The stretched body is constru cted by the original pointcloud from sensors but it only requires a small numbe r of points to approximate the original object.”

Harbin Institute of TechnologyHarbinPeople’s Republic ofChinaAsiaRobotic SystemsRobotics

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.18)