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About

Paolo Bellandi is a researcher whose work centers on the intersection of computer vision and industrial robotics, with a particular focus on developing advanced vision systems that enhance the performance and flexibility of robotic automation. His research has made meaningful contributions to the field of pick-and-place robotics, exploring how the strategic combination of 2D and 3D vision technologies can overcome longstanding limitations in speed, accuracy, and adaptability within robotic cells. Among his most recognized contributions are the Roboscan and Optoranger systems, published in 2013 and accumulating 28 and 25 citations respectively, which demonstrate his expertise in designing practical, application-driven solutions for bin picking — one of the most technically demanding challenges in industrial automation. His earlier work, including a multi-camera 2D vision system developed for a drink-serving robotic cell (2011, 12 citations), reflects a consistent commitment to real-world deployment and performance characterization. Across his body of work, Bellandi has championed the integration of complementary sensing modalities, showing that hybrid 2D-3D approaches yield measurable gains over single-modality systems. His research offers valuable insights for engineers and students seeking to bridge the gap between computer vision research and scalable industrial robotics applications.