首页|Research from Carinthia University of Applied Sciences Has Provided New Data on Machine Learning (Physics Guided Machine Learning Approach to Safe Quasi-Static Impact Situations In Human-Robot Collaboration)
Research from Carinthia University of Applied Sciences Has Provided New Data on Machine Learning (Physics Guided Machine Learning Approach to Safe Quasi-Static Impact Situations In Human-Robot Collaboration)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting originating from Villach , Austria, by NewsRx correspondents, research stated, "Following the performance and force limitation method of the ISO/TS 15066 standard, safety of a human-rob ot collaboration task is assessed for critical situations assuming quasi-static impact." The news correspondents obtained a quote from the research from Carinthia Univer sity of Applied Sciences: "To this end, impact forces and pressures are experime ntally measured and compared with limit values specified by ISO/TS 15066. Conseq uently, such a safety assessment must be repeated whenever something changes in the collaborative workspace or the task, which severely limits the flexibility o f collaborative systems. To overcome this problem, in this paper a physics guide d machine learning (ML) method for prediction of peak impact forces, within pred efined modification dimensions of collaborative applications, is proposed. Along with a pose-dependent linearized model, an ensemble of boosted decision tree (B DT) in combination with a feed-forward neural network (NN) is trained with peak impact forces measured at a UR10e robot covering the range of interest."
Carinthia University of Applied SciencesVillachAustriaEuropeCyborgsEmerging TechnologiesMachine LearningRo botRobotics
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