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About

Paolo Bevilacqua is a robotics researcher whose work sits at the intersection of motion planning, human-robot interaction, and autonomous navigation. His research is particularly focused on developing intelligent systems for assistive robots and autonomous vehicles operating in dynamic, human-populated environments — a technically demanding domain that carries real-world implications for mobility assistance and warehouse automation. Among his most influential contributions is a series of works on reactive planning and comfort-aware path planning for assistive robotic platforms, including a robotic walking assistant designed to guide users safely through crowded spaces (earning 43 and 39 citations respectively). His 2021 work on physics-inspired neural networks for human motion prediction (28 citations) demonstrates his drive to bridge data-driven approaches with principled modeling for safer robot decision-making. Bevilacqua has also made notable theoretical contributions, including an iterative dynamic programming solution to the multipoint Markov-Dubins problem and efficient clothoid spline intersection algorithms, underscoring his strength in geometric and optimal control methods. His research on multi-agent navigation and graph connectivity control for robot networks further reflects a growing interest in coordinated, socially-aware multi-robot systems. With over 190 cumulative citations, his work is shaping the future of safe, human-centered autonomous robotics.