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About

Michelangelo Nigro is a leading researcher in intelligent robotic manipulation, with a focus on autonomous assembly and cooperative robotics. His work lies at the intersection of computer vision, deep learning, and industrial automation, addressing critical challenges in precision manufacturing. Nigro’s major contributions include pioneering vision-based methods for the classic "Peg-in-Hole" problem, where he integrates 3D surface reconstruction and CNN-based hole detection to enable robots to perform high-precision assembly tasks despite positional uncertainties. His most cited paper (31 citations) demonstrates this approach, while subsequent work extends it to automotive body parts using semantic segmentation. Nigro has also advanced multi-robot systems, notably through cooperative manipulation of unknown objects using omnidirectional UAVs (25 citations) and vision-based robot-to-robot object handover without explicit communication. His research directly impacts Industry 4.0, particularly in the automotive sector, by fusing AI with advanced vision techniques to create more flexible, autonomous manufacturing systems. With a growing citation record and a focus on real-world industrial applications, Nigro’s work is shaping the future of intelligent, vision-guided robotics in complex production environments.