Contact Rodolphe Fremond
Areas of expertise
- Aeronautical Systems
- Air Transport Management
- Aircraft Design
- Autonomous Systems
- Space Systems
Background
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.
Clients
- SESAR Joint Undertaking
Publications
Articles In Journals
- Fremond R, Xu Y & Inalhan G. (2025). Adaptive Multiagent Reinforcement Learning Solver for Tactical Conflict Resolution in Diverse Urban Airspace Configurations. IEEE Transactions on Aerospace and Electronic Systems, 61(2)
- Zhao J, Gong T, Nnamani C, Conrad C, Fremond R, .... (2024). Developing a stackable programme based on the advanced air mobility systems MSc course. IFAC-PapersOnLine, 58(16)
- Altun AT, Hasanzade M, Saldiran E, Guner G, Uzun M, .... (2023). AMU-LED Cranfield flight trials for demonstrating the advanced air mobility concept. Aerospace, 10(9)
- Altun AT, Hasanzade M, Saldiran E, Guner G, Uzun M, .... (2023). The development of an advanced air mobility flight testing and simulation infrastructure. Aerospace, 10(8)
Conference Papers
- Kallaka R, Zhao J, Fremond R & Tsourdos A. (2025). Hierarchical Reinforcement Learning for Multi-Objective UAV Routing Considering Operational Complexities
- Fremond R. (2024). Urban Corridor-Based Tactical Conflict Resolution with Flight Plan Adherence and Uncertainty Resilience using a Multi-Agent Reinforcement Learning Solver
- Zhao J, Conrad C, Fremond R, Mukherjee A, Delezenne Q, .... (2023). Co-simulation digital twin framework for testing future advanced air mobility concepts: a study with BlueSky and AirSim
- Fremond R, Xu Y & Inalhan G. (2022). Application of an autonomous multi-agent system using Proximal Policy Optimisation for tactical deconfliction within the urban airspace
- Fremond R, Tang Y, Bhundoo P, Su Y, Tutku A, .... (2022). Demonstrating advanced U-space services for urban air mobility in a co-simulation environment