Contact Dr Dimitrios Panagiotakopoulos
Areas of expertise
- Aeronautical Systems
- Aerospace Structures
- Airworthiness
- Autonomous Systems
- Aviation Management & Operations
- Electric and Hybrid Vehicles
- Flight Physics
- Space Systems
- Vehicle Aerodynamics
- Vehicle Engineering & Mobility
Background
With over 15 years of contributions to the state-of-the-art in Air Traffic Management (ATM), currently enabling the path towards its digitalisation, Dimitri now heads the Unmanned Aircraft Systems Traffic Management (UTM) Research group at Cranfield University.
Before joining Cranfield, Dimitri worked as an ATM Systems Specialist at Air Navigation Solutions at Gatwick Airport, where he led the development of the Airport's 10-year Capacity Enhancement Roadmap to optimise and improve operations and infrastructure to enable Air Traffic Movement growth, which identified Operational Improvements from SESAR and NextGen Programmes and coordinated the different Airport stakeholders, including Airlines, Ground Handlers, Air Traffic Control to implement these solutions. During that time, Dimitri delivered a number of ATM systems to enable Airport operations and capacity growth, including an extension to its multi-lateration system, to allow a new Boeing Hangar (fitting 2 Code F aircraft) to be built without affecting operations.
Previous to this, Dimitri was a Senior Project Systems Engineer at NATS (formerly
National Air Traffic Services), where he single-handedly delivered complex
multi-million pounds Surveillance solutions to Airports, on time and on budget. This included the very first use of an X-band radar for
windfarm mitigation in the UK.
Dimitri holds a PhD from Imperial College London on Satellite Navigation
and Positioning Systems, with a thesis that developed innovative statistical
algorithms to monitor and guarantee the integrity of GNSS-based position and
navigation computations within safety-critical aircraft flight management
systems. It was the first time Extreme Value Theory was introduced in an
ATM-related field.
Research opportunities
Our Research Group is interested in leveraging emerging technological advances such as Machine Learning, data-driven solutions using smart contracts and distributed ledger technology, to enable more collaborative, efficient and optimised integrated ATM/UTM operations.
There are always opportunities for self-funded PhDs.
Contact us to find out more for funded PhD around our current and future activities.
Current activities
We are currently setting up the National Beyond Visual Line of Sight (BVLOS) Experimental Corridor (NBEC) within and around the Cranfield Airport Airspace to allow us to test and validate new Concepts, systems, procedures that enable BVLOS drone flights in non-segregated airspace. This together with our high-end simulator facilities and digital twins provides a unique live/virtual sandbox and UTM/UAM ecosystem to address the challenges that ATM/UTM integration and UAM applications present.
On-going areas of interest include:
- applying deep learning and explainable AI methods in Air Traffic Management and safety-critical applications
- intelligent operation-centric and risk-minimising UAS flight / contigency / emergency planning and management, de-confliction, collision avoidance solutions development
- data-driven Comms, Navigation and Surveillance (CNS) signal and performance analysis to derive global quality of service in rural, semi-urban and urban contexts and ascertain optimum CNS combination for specific mission
- alternative navigation mode mechanism that leverage information captured by the UAS on-board capabilities (e.g. camera, inertial navigation system (INS))
- increased automation of ATM/UTM interface, including interaction with Air Traffic Control
- data driven approach to Urban Air Mobility mission planning, traffic flow and capacity management.
Clients
Boeing, Airbus, SAAB, Thales,
Publications
Articles In Journals
- Souanef T, Al-Rubaye S, Tsourdos A, Ayo S & Panagiotakopoulos D (2023) Digital twin development for the airspace of the future, Drones, 7 (7) Article No. 484.
- Alharbi A, Petrunin I & Panagiotakopoulos D (2023) Assuring safe and efficient operation of UAV using explainable machine learning, Drones, 7 (5) Article No. 327.
- Alharbi A, Petrunin I & Panagiotakopoulos D (2023) Deep learning architecture for UAV traffic-density prediction, Drones, 7 (2) Article No. 78.
- Alharbi A, Petrunin I & Panagiotakopoulos D (2022) Modeling and characterization of traffic flow patterns and identification of airspace density for UTM application, IEEE Access, 10 130110-130134.
- Panagiotakopoulos D, Majumdar A & Ochieng WY (2014) Extreme value theory-based integrity monitoring of global navigation satellite systems, GPS Solutions, 18 (1) 133-145.
- Panagiotakopoulos D, Majumdar A & Ochieng W (2009) Characterizing the distribution of safety occurrences in aviation: an approach using extreme value theory, Transportation Research Record, 2106 (2106) 129-140.
Conference Papers
- Alharbi A, Petrunin I & Panagiotakopoulos D (2022) Traffic flow prediction for UTM application: a deep learning approach. In: 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), Portsmouth, Virginia, 18-22 September 2022.
- Doumard T, Gañán Riesco F, Petrunin I, Panagiotakopoulos D, Bennett C & Harman S (2022) Radar discrimination of small airborne targets through kinematic features and machine learning. In: 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), Portsmouth, Virginia, 18-22 September 2022.
- Warrier A, Al-Rubaye S, Panagiotakopoulos D, Inalhan G & Tsourdos A (2022) Interference mitigation for 5G-connected UAV using deep Q-learning framework. In: 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), Portsmouth, Virginia, 18-22 September 2022.
- Karyotakis MK, Panagiotakopoulos D, Braithwaite G & Tsourdos A (2021) Aspects and challenges of unmanned aircraft systems safety assurance and certification for advanced operations. In: AIAA Aviation 2021 Forum, Virtual Event, 2-6 August 2021.
- Nanos N, Kagan Isik O, Verdeguer Moreno R, Petrunin I, Panagiotakopoulos D & Tsourdos A (2021) UAV path planning optimization based on GNSS quality and mission requirements. In: AIAA SciTech Forum 2021, Virtual Event, 19-21 January 2021.
- Warrier AS, Al-Rubaye S, Panagiotakopoulos D, Inalhan G & Tsourdos A (2021) Seamless handover in urban 5G-UAV systems using entropy weighted method. In: ICMIWC 2021: 15th International Conference on Mobile Internet and Wireless Communication, Rome, 13-14 December 2021.
- Sanchez Hernandez C, Ayo S & Panagiotakopoulos D (2021) An explainable artificial intelligence (xAI) framework for improving trust in automated ATM tools. In: 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC), San Antonio, 3-7 October 2021.
- Alharbi A, Petrunin I & Panagiotakopoulos D (2021) Identification and characterization of traffic flow patterns for UTM application. In: 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC), San Antonio, 3-7 October 2021.
- Panagiotakopoulos D, Williamson A, Petrunin I, Harman S, Quilter T, Williams-Wynn I, Goudie G, Watson N, Vernall P, Reid J, Puscius E, Cole A & Tsourdos A (2021) Developing drone experimentation facility: progress, challenges and cUAS consideration. In: 2021 21st International Radar Symposium (IRS), Berlin, 21-22 June 2021.
- Alharbi A, Poujade A, Malandrakis K, Petrunin I, Panagiotakopoulos D & Tsourdos A (2020) Rule-based conflict management for unmanned traffic management scenarios. In: 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), San Antonio, 11-15 October 2020.
- Ryan R, Al-Rubaye S, Braithwaite G & Panagiotakopoulos D (2020) The legal framework of UTM for UAS. In: 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), San Antonio, 11-15 October 2020.
