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About

Daniele Pillan is a leading researcher in the field of robotic spray painting, with a primary focus on automating and optimizing path and trajectory planning for industrial robots. His major contributions include the development of a novel path-constrained trajectory planning strategy, which has garnered 60 citations, and an automated optimum path generation system that eliminates the need for manual, self-learning programming—a significant advancement for manufacturing efficiency. Pillan’s work also addresses complex challenges such as planning smooth orientation trajectories using B-spline quaternion curves, ensuring velocity and acceleration continuity while avoiding singularities. His research has consistently pushed the boundaries of robotic painting, with key papers published between 2010 and 2025, accumulating over 120 citations. Notably, his 2025 paper introduces a cutting-edge method for orientation planning, demonstrating his ongoing commitment to solving real-world industrial problems. Pillan’s achievements are particularly impactful for students and researchers interested in robotics, automation, and manufacturing, as his work directly enhances the precision, efficiency, and autonomy of robotic systems in painting applications.