A.G. Marchetti

École Polytechnique Fédérale de Lausanne

Papers

Total Citations

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About

A.G. Marchetti is a leading researcher in optimal control and motion planning, with a particular focus on time-optimal path-following under uncertainty. His work addresses critical challenges in dynamic motion planning along pre-specified geometric references—applications that span milling, robot-supported measurements, and autonomous vehicle trajectory planning. Marchetti has made significant contributions by developing convex and non-convex optimal control formulations that enable efficient, real-time path-following operations even when system dynamics or environmental conditions are uncertain. His most cited paper, "Time-optimal path-following operation in the presence of uncertainty" (2016), has garnered 7 citations and serves as a foundational reference for researchers tackling robust motion planning. Beyond this, Marchetti’s research bridges theoretical optimal control with practical robotic and autonomous systems, offering algorithms that balance speed, accuracy, and safety. His work is particularly valued in industrial automation and field robotics, where precise, time-efficient maneuvers are essential. For students and researchers, Marchetti’s contributions provide a rigorous framework for understanding how to achieve optimal performance in real-world, uncertain environments—a cornerstone of modern autonomous systems.