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About

Davide Anghinolfi’s research lies at the intersection of robotics, optimization, and artificial intelligence, with a primary focus on the automated design and deployment of large-scale artificial skin for humanoid robots. His work tackles the formidable challenge of customizing tactile sensor skins to fit robots of varying shapes while minimizing wiring complexity—a problem he has modeled as a minimum-cost Constrained Spanning Forest, proven to be NP-hard. Anghinolfi’s major contributions include pioneering heuristic approaches, particularly Ant Colony Optimization (ACO), to solve the optimal wiring problem in robotic skin design. His most-cited paper, “On the Problem of the Automated Design of Large-Scale Robot Skin” (2013, 30 citations), establishes automated procedures for skin deployment, while subsequent studies systematically evaluate pheromone structures in ACO algorithms to enhance solution quality and efficiency. Through rigorous experimental validation, he has demonstrated how tailored pheromone designs can significantly improve optimization outcomes for complex, real-world wiring constraints. Anghinolfi’s work has garnered over 50 citations, reflecting its impact on advancing scalable, cost-effective robotic skin systems. His achievements offer a compelling blueprint for integrating metaheuristic optimization into robotics, inspiring students and researchers to explore automated design solutions for embodied AI.