Abstract
The increasing adoption of industrial robots to boost production efficiency is turning human-robot collaborative scenarios much more frequent. In this context, technical factory workers need to be safe at all times from collisions and prepare for emergencies and potential accidents. Another trend in industrial automation is the usage of machine learning techniques - specifically, deep learning algorithms - for image classification. Following these tendencies, this work evaluates the application of deep learning models to detect physical collision in human-robot interactions. Security camera images are used as the primary information source for intelligent collision detection. Unlike other proposed approaches in the literature that apply sensors like Light Detection And Ranging (LIDAR), Laser Range Finder (LRF), or torque sensors from robots, this work does not consider extra sensors, using only 2D cameras. Results show more than 99% of accuracy in the evaluated scenarios, revealing that approaches adopting deep learning algorithms could be promising for human-robot collision avoidance in industrial scenarios. The proposed models may support safety in industrial environments and reduce the impact of collision accidents.
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Acknowledgments
This work was supported by the Ericsson Research (Brazil), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and the Fundação de Amparo a Ciência e Tecnologia de Pernambuco (FACEPE).
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Silva, I.R.R. et al. (2020). Assessing Deep Learning Models for Human-Robot Collaboration Collision Detection in Industrial Environments. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham. https://doi.org/10.1007/978-3-030-61377-8_17
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