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About

Lorenzo Lyons is a roboticist specializing in multi-robot coordination, nonlinear control, and autonomous navigation. His most impactful work, "Distributed Nonlinear Trajectory Optimization for Multi-Robot Motion Planning" (53 citations), introduces a distributed nonlinear model predictive control (NMPC) formulation that enables teams of robots to coordinate motion planning while robustly handling communication packet losses and delays—a critical advancement for real-world deployment. Lyons further advances persistent monitoring with his "Time-Inverted Kuramoto Model Meets Lissajous Curves" (9 citations), a distributed strategy that synchronizes robot trajectories for both surveillance and target detection without collisions. In motion planning, his "Curvature-Aware Model Predictive Contouring Control" (7 citations) generalizes traditional contouring control from machining to autonomous driving, improving path tracking on curved roads. Lyons also contributes to robot reliability through battery state-of-charge estimation using cubic splines and moving horizon estimation, and to experimental platforms with the DART (Delft’s Autonomous-driving Robotic Testbed), a compact, cost-effective car-like robot for autonomous driving research. His work bridges theoretical control advances with practical multi-robot and autonomous driving applications.