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Human-robot collaboration has gained a notable prominence in Industry 4.0, asthe use of collaborative robots increases efficiency and productivity in theautomation process. However, it is necessary to consider the use of mechanismsthat increase security in these environments, as the literature reports thatrisk situations may exist in the context of human-robot collaboration. One ofthe strategies that can be adopted is the visual recognition of thecollaboration environment using machine learning techniques, which canautomatically identify what is happening in the scene and what may happen inthe future. In this work, we are proposing a new framework that is capable ofdetecting robotic arm keypoints commonly used in Industry 4.0. In addition todetecting, the proposed framework is able to predict the future movement ofthese robotic arms, thus providing relevant information that can be consideredin the recognition of the human-robot collaboration scenario. The proposedframework is based on deep and extreme learning machine techniques. Resultsshow that the proposed framework is capable of detecting and predicting withlow error, contributing to the mitigation of risks in human-robotcollaboration.

A framework for robotic arm pose estimation and movement prediction based on deep and extreme learning models