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About

Iacopo Gentilini is a robotics and optimization researcher whose work sits at the intersection of combinatorial optimization, motion planning, and robotic systems. He is best known for his contributions to the Traveling Salesman Problem with Neighborhoods (TSPN), a generalization of the classic TSP in which each destination occupies a region rather than a fixed point — a formulation with direct applications in robotics, unmanned aerial vehicles, and autonomous systems. His 2012 MINLP-based solution to the TSPN, which has accumulated 79 citations, established a rigorous mathematical framework for this NP-hard problem and has become a foundational reference in the field. Building on this work, Gentilini extended the TSPN to multi-goal path planning for mobile robots and redundant robotic manipulators, addressing the dual challenge of optimizing task sequencing while navigating infinite feasible configurations arising from kinematic redundancy. His 2014 paper on the generalized TSPN in mobile robotics (36 citations) further broadened the practical reach of his theoretical contributions. He has also explored space robotics, developing model-free force-feedback control strategies to simulate free-floating environments for testing space-based robotic systems. Across his body of work, Gentilini has made lasting contributions to how autonomous and redundant robotic systems efficiently plan and execute complex multi-goal tasks.