Yanis Diallo

Papers

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About

Yanis Diallo is a leading researcher at the intersection of intelligent robotics and industrial automation, with a primary focus on advancing autonomous systems for complex, dynamic environments. His work centers on two critical areas: robust obstacle detection and avoidance for mobile robots, and seamless human-robot collaboration in remanufacturing contexts. Diallo’s most impactful contribution is the development of NAV-YOLO, a novel deep learning framework for mobile robot obstacle detection and avoidance, specifically tailored for the challenging conditions of aircraft Maintenance, Repair, and Overhaul (MRO) hangars. This work, which has garnered 9 citations since its 2024 publication, addresses the critical need for robots to navigate environments cluttered with objects of varying shapes and sizes. Additionally, his case-study on a human-robot interaction platform for remanufacturing demonstrates a practical, application-driven approach to integrating collaborative robots into industrial workflows. By tackling real-world problems in high-stakes settings like aircraft hangars, Diallo’s research is paving the way for safer, more efficient automation in maintenance and manufacturing industries.