首页|University of Zielona Gora Researchers Update Knowledge of Machine Learning (Wor king toward Solving Safety Issues in Human-Robot Collaboration: A Case Study for Recognising Collisions Using Machine Learning Algorithms)
University of Zielona Gora Researchers Update Knowledge of Machine Learning (Wor king toward Solving Safety Issues in Human-Robot Collaboration: A Case Study for Recognising Collisions Using Machine Learning Algorithms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news reporting out of Zielona Gora, Poland, by New sRx editors, research stated, "The monitoring and early avoidance of collisions in a workspace shared by collaborative robots (cobots) and human operators is cr ucial for assessing the quality of operations and tasks completed within manufac turing." Financial supporters for this research include Polish Ministry of Science. The news correspondents obtained a quote from the research from University of Zi elona Gora: "A gap in the research has been observed regarding effective methods to automatically assess the safety of such collaboration, so that employees can work alongside robots, with trust. The main goal of the study is to build a new method for recognising collisions in workspaces shared by the cobot and human o perator. For the purposes of the research, a research unit was built with two UR 10e cobots and seven series of subsequent of the operator activities, specifical ly: (1) entering the cobot's workspace facing forward, (2) turning around in the cobot's workspace and (3) crouching in the cobot's workspace, taken as video re cordings from three cameras, totalling 484 images, were analysed. This innovativ e method involves, firstly, isolating the objects using a Convolutional Neutral Network (CNN), namely the Region-Based CNN (YOLOv8 Tiny) for recognising the obj ects (stage 1). Next, the Non-Maximum Suppression (NMS) algorithm was used for f iltering the objects isolated in previous stage, the * * k* * -means clustering method and Simple Online Real-Time Tracking (SORT) approach were used for separa ting and tracking cobots and human operators (stage 2) and the Convolutional Neu tral Network (CNN) was used to predict possible collisions (stage 3). The method developed yields 90% accuracy in recognising the object and 96.4% accuracy in predicting collisions accuracy, respectively."
University of Zielona GoraZielona GoraPolandEuropeAlgorithmsCyborgsEmerging TechnologiesMachine LearningNano-robotRobotRobotics
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