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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Description rdf:about="https://repozitorij.uni-lj.si/IzpisGradiva.php?id=152541"><dc:title>Visual quality and safety monitoring system for human-robot cooperation</dc:title><dc:creator>Kozamernik,	Nejc	(Avtor)
	</dc:creator><dc:creator>Zaletelj,	Janez	(Avtor)
	</dc:creator><dc:creator>Košir,	Andrej	(Avtor)
	</dc:creator><dc:creator>Šuligoj,	Filip	(Avtor)
	</dc:creator><dc:creator>Bračun,	Drago	(Avtor)
	</dc:creator><dc:subject>human-robot cooperation</dc:subject><dc:subject>vision systems</dc:subject><dc:subject>quality</dc:subject><dc:subject>safety</dc:subject><dc:subject>assembly supervision</dc:subject><dc:description>Efficient workspace awareness is critical for improved interaction in cooperative and collaborative robotic applications. In addition to safety and control aspects, quality-related tasks such as the monitoring of manual activities and the final quality assessment of the results are also required. In this context, a visual quality and safety monitoring system is developed and evaluated. The system integrates close-up observation of manual activities and posture monitoring. A compact single-camera stereo vision system and a time-of-flight depth camera are used to minimize the interference of the sensors with the operator and the workplace. Data processing is based on a deep learning to detect classes related to quality and safety aspects. The operation of the system is evaluated while monitoring a human-robot manual assembly task. The results show that the system ensures a high level of safety, provides reliable visual feedback to the operator on errors in the assembly process, and inspects the finished assembly with a low critical error rate.</dc:description><dc:date>2023</dc:date><dc:date>2023-11-27 10:15:53</dc:date><dc:type>Članek v reviji</dc:type><dc:identifier>152541</dc:identifier><dc:language>sl</dc:language></rdf:Description></rdf:RDF>
