Francesco Amigoni

Francesco Amigoni is affiliated with the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano. His research focuses on robotics, multi-agent systems, and machine learning, with applications in autonomous systems, anomaly detection, and energy systems. He has contributed to advancements in multi-agent coordination, path planning, and predictive maintenance through interdisciplinary approaches.

Key research areas include:

• Multi-Agent Systems: Task allocation, cooperative robotics, and deadlock prevention in dynamic environments
• Machine Learning: Anomaly detection, transfer learning, and neural networks for battery lifecycle analysis
• Autonomous Robotics: Exploration strategies, SLAM algorithms, and map completeness estimation
• Data Mining: NLP-driven classification of road accidents and healthcare system analysis

His work bridges theoretical foundations with real-world applications, addressing challenges in energy systems, environmental monitoring, and healthcare management. Recent studies highlight innovations in lithium-ion battery prognostics and swarm robotics fault detection.