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About

Chiara Fulgenzi is a robotics researcher whose work has made significant contributions to autonomous navigation in dynamic and uncertain environments. Her research sits at the intersection of probabilistic modeling, motion planning, and mobile robotics, with a particular focus on enabling robots to navigate safely alongside moving obstacles such as pedestrians and vehicles. Fulgenzi's most influential contribution, "Dynamic Obstacle Avoidance in Uncertain Environments Combining PVOs and Occupancy Grid" (2007, 213 citations), introduced a compelling fusion of Probabilistic Velocity Obstacles with dynamic occupancy grids — a framework that addressed critical limitations in existing systems by explicitly accounting for obstacle dynamics and perceptual uncertainty. Her subsequent work extended this foundation by incorporating Gaussian processes and Rapidly-exploring Random Trees (RRT) for probabilistic path planning (2008, 129 citations), and later leveraging Hidden Markov Models to predict pedestrian motion patterns (2009, 63 citations). Throughout her research, Fulgenzi consistently championed probabilistic representations over deterministic approaches, arguing that richer uncertainty modeling yields safer and more reliable robot behavior. Her risk-based motion planning framework (2010) further consolidated this philosophy into practical navigation strategies. With over 450 cumulative citations, her work remains a valuable reference for researchers tackling the enduring challenge of safe autonomy in real-world, unpredictable spaces.