Abstract
Abstract In the Industry 4.0 scenario, collaborative robots have been strongly employed for complex processes and customized production activities. Interaction-based technologies have characterized this approach assisting the operator in several process workflows. In this paper, a haptic-based touch detection strategy is described and tested to assist, in real-time, the operator using a collaborative system in a real industrial scenario, namely the welding process. To assess the performance, two main criteria were analyzed: the 3-Sigma rule and the Hampel identifier. Experimental results showed better performance of the 3-Sigma rule in terms of precision percentage (mean value of 99.9%) and miss rate (mean value of 10%) with respect to the Hampel identifier. Results confirmed the influence of the contamination level related to the dataset. This algorithm adds significant advances to enable the use of light and simple machine learning approaches in real-time applications.
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