M.Eng. Matteo Pantano
Manufacturing and Material Processes
Otto Hahn Ring 6, 81739 München
Office hours: Please send e-mail in advance.
Matteo Pantano received a Master of Engineering in Automation and IT from the Technische Universität Köln (TH Köln) in 2019. Mr. Pantano has worked in the project Rovitis 4.0 for research and project management of autonomous viticulture (Azienda Pantano). Currently, Mr. Pantano is a PhD student in the field of Human centered robotics at Technische Universität München and Siemens AG in the context of the European project SHOP4CF.
Human Robot Collaboration, Operator Knowledge management, Small Medium Enterprises, Operator 4.0, Industry 4.0, IIoT, Data driven economies, Operator empowerment, User research
- Framework for flexible bin picking to adress high-mix and low-volume production
- Software architecture for the management of operator knowledge
- A BPMN based approach for robotics programming and orchestration
- Matteo Pantano, Daniel Regulin, Benjamin Lutz, Dongheui Lee, A human-cyber-physical system approach to lean automation using an industrie 4.0 reference architecture,Procedia Manufacturing, Volume 51, 2020, Pages 1082-1090, ISSN 2351-9789, https://doi.org/10.1016/j.promfg.2020.10.152.
- Pantano, Matteo, Yurii Pavlovskyi, Erik Schulenburg, Konstantinos Traganos, Seyedamir Ahmadi, Daniel Regulin, Dongheui Lee, and José Saenz. 2022. "Novel Approach Using Risk Analysis Component to Continuously Update Collaborative Robotics Applications in the Smart, Connected Factory Model" Applied Sciences 12, no. 11: 5639. https://doi.org/10.3390/app12115639
- Lutz, Benjamin, Dominik Kisskalt, Andreas Mayr, Daniel Regulin, Matteo Pantano, and Jörg Franke. "In-situ identification of material batches using machine learning for machining operations." Journal of Intelligent Manufacturing 32, no. 5 (2021): 1485-1495.
- Hauser, Tobias, Raven T. Reisch, Philipp P. Breese, Benjamin S. Lutz, Matteo Pantano, Yogesh Nalam, Katharina Bela, Tobias Kamps, Joerg Volpp, and Alexander FH Kaplan. "Porosity in wire arc additive manufacturing of aluminium alloys." Additive Manufacturing 41 (2021): 101993.
- Pantano, M., Blumberg, A., Regulin, D., Hauser, T., Saenz, J., Lee, D. (2022). Design of a Collaborative Modular End Effector Considering Human Values and Safety Requirements for Industrial Use Cases. In: Palli, G., Melchiorri, C., Meattini, R. (eds) Human-Friendly Robotics 2021. Springer Proceedings in Advanced Robotics, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-96359-0_4
- Pantano, Matteo, Tobias Kamps, Solomon Pizzocaro, Giorgio Pantano, Matteo Corno, and Sergio Savaresi. "Methodology for Plant Specific Cultivation through a Plant Identification pipeline." In 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 298-302. IEEE, 2020.
- Pizzocaro, Solomon, Matteo Corno, Matteo Pantano, and Sergio Savaresi. "Magnetometer Aided GPS-free Localization of an Autonomous Vineyard Drone." In 2021 European Control Conference (ECC), pp. 1132-1137. IEEE, 2021.
- Biocca, M., Aiello, L., Baldoin, C., Bolzonella, C., Bugin, G., Gallo, P., Gardiman, M., Meneghetti, F., Pallottino, F., Pantano, G. and Pantano, M., 2022. Rovitis 4.0: An Autonomous Robot for Spraying in Vineyards. In International Conference on Safety, Health and Welfare in Agriculture and Agro-food Systems (pp. 176-185). Springer, Cham. https://doi.org/10.1007/978-3-030-98092-4_19
- Reisch, Raven, Tobias Hauser, Benjamin Lutz, Matteo Pantano, Tobias Kamps, and Alois Knoll. "Distance-based multivariate anomaly detection in wire arc additive manufacturing." In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 659-664. IEEE, 2020.
Pantano, Matteo. Design of a Cyber-Physical System for flexible Human Robot Collaboration, 2019.
- SHOP4CF, Smart Human Oriented Platform for Connected Factories, aims to find the right balance between cost-effective automation, repetitive tasks and involve the human workers in areas such as adaptability, creativity and agility where they create the biggest added value. Also to pursue the highly-connected factory model to reap the benefits of all the data generated within the factory.
- Rovitis 4.0 R&D Project aims to advance the state-of-the-art in agricultural robotics, focusing on the technical challenges related to the autonomous driving in grapevines, to significantly contribute to the agriculture market and, more specifically, to viticulture.