Andrea Zanelli

Microsystems (United Kingdom), University of Freiburg

Papers

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About

Andrea Zanelli is a leading researcher in optimization-based control and motion planning for autonomous systems, with a primary focus on enabling safe, real-time robot navigation in dynamic environments. His major contributions lie at the intersection of nonlinear model predictive control (NMPC) and motion generation, where he has developed computationally efficient frameworks that guarantee collision avoidance even in the presence of moving agents like humans or other robots. His most cited work, "CIAO⁎: MPC-based Safe Motion Planning in Predictable Dynamic Environments" (2020, 36 citations), introduces a pioneering approach that integrates MPC with search-based planning to ensure safety in shared workspaces—a critical advancement for collaborative robotics. In parallel, his paper "Least Conservative Linearized Constraint Formulation for Real-Time Motion Generation" (2020) addresses the challenge of reducing conservatism in constraint handling, enabling faster and more agile robot maneuvers without sacrificing safety. Zanelli’s work is notable for bridging theoretical rigor with practical real-time implementation, directly impacting fields such as autonomous driving, warehouse logistics, and human-robot interaction. His contributions continue to shape how robots perceive and react to unpredictable surroundings, making him a key figure in the evolution of intelligent, safety-critical autonomous systems.