Areas of expertise

  • Aeronautical Systems
  • Air Transport Management
  • Aircraft Design
  • Autonomous Systems
  • Space Systems


Rodolphe is currently pursuing a Ph.D. program in Aerospace at Cranfield University, focusing on the development of Tactical conflict Resolution solutions based on Machine Learning for Unmanned Aerial Systems Traffic Management systems.

The objective of the research is to propose an AI-based solver for U-Space Service Providers, Suppliers, and Air Traffic Controllers to effectively resolve conflicts between Unmanned Aerial Systems operating within the urban airspace, specifically addressing complex conflict situations and separation assurance with conformance volume constraints considerations.

The envisioned solution aims to develop an autonomous solver that can solve conflict in the airspace, providing a supportive role for Air Traffic Control Officers and U-Space Service Providers.

He actively contributes to various projects aimed at driving the digitalisation of aviation and the advancement of Advanced Air Mobility. One notable achievement includes leading the software integration of synthetic U-spaces services into an ATM academic simulator for the Air Mobility Urban - Large Experimental Demonstrations (AMU-LED) project firstly conducted in the UK in June 2021.

Research opportunities

Rodolphe Fremond's research interests lie at the intersection of aerospace engineering, artificial intelligence, and unmanned aerial systems traffic management. He is particularly focused on the application of Multi-Agent Reinforcement Learning (MARL) for Tactical Conflict Resolution in U-Space, aiming to enhance the safety and efficiency of UAS operations in increasingly crowded airspace. His work explores the development of scalable, autonomous systems that can effectively ensure separation assurance as well as in-flight deconfliction. Rodolphe is also interested in the digital transformation of aviation and in the future flight services.

Current activities

Generalisation of a Multi-agent Reinforcement Learning solver for solving general conflict in tactical flight phase conditions.

Integration of advanced U-space services into an ATM&UTM simulator.

Improvement of this ecosystem.


  • SESAR Joint Undertaking


Articles In Journals

Conference Papers