Emiliano Traini

Papers

Total Citations

H-Index

About

Emiliano Traini is a leading researcher at the intersection of Industry 4.0, autonomous mobile robotics (AMR), and collaborative robotics (cobots), with a focus on optimizing energy efficiency in automated logistics systems. His most cited work, "Prediction and estimation model of energy demand of the AMR with cobot for the designed path in automated logistics systems" (2021, 17 citations), introduces a novel framework for forecasting energy consumption of AMR-cobot teams along predefined paths—a critical contribution to sustainable, cost-effective smart manufacturing. By integrating flexibility and real-time data, Traini’s models enable factories to reduce operational costs while maintaining high throughput. His research bridges the gap between theoretical robotics and practical industrial deployment, addressing key challenges in internal logistics automation. Traini’s work has been recognized for its direct applicability to Industry 4.0 ecosystems, earning citations from peers in robotics, energy management, and supply chain optimization. His contributions are shaping the next generation of adaptive, energy-aware logistics systems, making him a pivotal figure in advancing autonomous industrial operations.