Alessandro Bozzi
Papers
4
Total Citations
15
H-Index
3
About
Alessandro Bozzi is an emerging robotics and control systems researcher whose work spans autonomous vehicles, human-robot collaboration, and advanced motion planning. His research primarily focuses on applying sophisticated control methodologies — particularly Nonlinear Model Predictive Control (NMPC) — to real-world robotic challenges in both ground and aerial platforms. Bozzi's most impactful contribution combines NMPC with Moving Horizon Estimation to coordinate heterogeneous unmanned air-ground vehicle fleets, earning 5 citations since its 2024 publication. His work on wheeled mobile robots (WMRs) demonstrates a consistent commitment to practical navigation solutions, developing asynchronous finite state controllers and indoor path-tracking algorithms that address localization challenges in uncontrolled environments. Notably, his 2023 work on Dual Quaternion-based Dynamic Movement Primitives advances obstacle avoidance kinematics within human-robot collaboration frameworks, reflecting his broader interest in safe, intelligent systems of systems. With a cumulative citation count of 15 across four recent publications, Bozzi represents a productive early-career researcher making meaningful contributions to autonomous robotics. Students interested in model predictive control, multi-agent coordination, or collaborative robotics will find his growing body of work a valuable and technically rigorous reference point.
Research Focus
Key Achievements
Top Papers
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