Abstract
This paper presents implementation details of AI methods used for safety in human robot collaborative scenarios, based on fuzzy logic and state-of-the-art deep learning-based perception. For semantic representation of the environment, a scene graph encodes the environment surrounding the robots with information from camera, which is then fed to risk management system for safety analysis purpose. Transfer learning effects have been observed when starting training based on weights pre-trained on Image Net and COCO data-sets. A fuzzy logic solution for risk analysis, evaluation and mitigation has been compared to a neuro-fuzzy approach. Experiments have been performed in a physically realistic 3D simulation of a warehouse environment to evaluate which configuration presents the best performance for robotic perception and risk mitigation.
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