首页|Findings on Machine Learning Discussed by Investigators at University of Manches ter (Material Recognition Using Robotic Hand With Capacitive Tactile Sensor Arra y and Machine Learning)
Findings on Machine Learning Discussed by Investigators at University of Manches ter (Material Recognition Using Robotic Hand With Capacitive Tactile Sensor Arra y and Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting originating from Manchester, Unite d Kingdom, by NewsRx correspondents, research stated, “Autonomous manipulation u sing robot hands can benefit from tactile sensing, as it can collect information on variations in applied force and surface properties. This article presents a capacitive tactile sensor placed on the robot’s hand fingers.” Financial support for this research came from Beijing Tashan Technology Corporat ion Ltd. Our news editors obtained a quote from the research from the University of Manch ester, “Due to its unique structure and high sensitivity to material permittivit y, this sensing system can obtain capacitive data both when a robot finger is ap proaching an object and when it has touched the object. With threedimensional r eduction methods, that is, principal component analysis (PCA), independent compo nent analysis (ICA), and multidimensional scaling (MDS), a dataset is transforme d to be 2-D and then fed into two supervised classifications algorithms, that is , k-nearest neighbors (KNNs) and support vector machines (SVMs). In comparison t o previous studies, the MDS-based SVM achieves high material recognition accurac y, up to 98% for recognition of three different material classes, that is, plastic, paper, and glass using capacitance data only. Furthermore, it performs well in recognition of five different materials, that is, dry plastic, plastic with water drops, paper, dry glass, and glass with water drops. The reco gnition accuracy is as high as 93%. Computational time can be reduc ed by about 60% by combining the dimension reduction methods with classification algorithms.”
ManchesterUnited KingdomEuropeCybo rgsEmerging TechnologiesMachine LearningRobotRoboticsRobotsUniversit y of Manchester
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