July 18, 2024
1 Solar System Way, Planet Earth, USA
Science And Technology

Reducing emissions: autonomous electric cranes transform aircraft taxiing

A new algorithm for autonomous cranes promises to revolutionise aircraft taxiing, potentially saving the aviation industry millions of dollars in fuel costs and significantly reducing emissions and noise pollution. This innovative solution, developed by Stefano Zaninotto, Dr Jason Gauci and Dr Brian Zammit of the University of Malta, is detailed in their recent study published in the journal Aerospace.

Stefano Zaninotto and his colleagues have created an algorithm designed to manage taxiing operations using autonomous cranes. “Our algorithm aims to minimise taxiing-related delays and route lengths, while maximising the efficient use of cranes,” says Zaninotto. The aviation industry has long been grappling with the environmental and economic impacts of aircraft taxiing. Traditionally, aircraft use their engines for taxiing, resulting in high fuel consumption and significant emissions. In 2022, taxiing phases made up about 1 in 7 of the total duration of intra-European flights, consuming approximately five million tonnes of fuel per year.

The new algorithm focuses on optimizing ground operations by deploying electric cranes to move aircraft between gates and runways. This system works strategically, using a centralized approach to plan all routes in advance, adjust schedules and assign cranes. In this way, it aims to eliminate traffic conflicts and improve overall efficiency.

The study highlights several key findings. Firstly, the algorithm successfully reduces taxi delays and emissions. It takes into account several factors, such as the battery charge status of the cranes and the availability of charging stations, ensuring that the cranes can operate without interruption. The system is also scalable and adaptable, having been rigorously tested across different airport layouts and traffic volumes.

Zaninotto explains: “Our system addresses the shortcomings of existing approaches by focusing on conflict-free solutions and efficient use of cranes. It can manage multiple active runways and strategically allocate cranes.” This adaptability is crucial, given the diverse and dynamic nature of airport environments.

In addition, the study's simulations indicate significant fuel savings. The researchers estimate that using cranes for taxiing can reduce fuel consumption compared to conventional methods. This not only reduces costs for airlines, but also supports environmental sustainability goals. “Reducing fuel consumption during taxiing is a critical step towards achieving carbon neutrality in aviation,” notes Zaninotto.

The system also improves safety by continuously monitoring for potential conflicts during taxiing operations. This includes checking for conflicts between the aircraft and cranes, as well as between the cranes themselves. The algorithm employs advanced techniques to ensure that all movements are coordinated and safe.

In summary, this innovative algorithm represents a major advancement in airport ground operations. By leveraging autonomous cranes, it offers a practical and effective solution to the challenges of aircraft taxiing. The researchers’ work paves the way for greener and more efficient airports, in line with global sustainability goals.

Journal reference

Zaninotto, Stefano, Jason Gauci and Brian Zammit. “An autonomous crane algorithm for taxiing unpowered aircraft.” Aerospace 2024, 11, 307. DOI: https://doi.org/10.3390/aerospace11040307

About the authors

In g. Stefano Zaninotto Stefano Zaninotto holds a Bachelor and Master’s degree in Civil and Environmental Engineering from the University of Trieste (Italy) and is about to complete his PhD in Air Traffic Management at the Institute of Aerospace Technologies, University of Malta (Malta). His doctoral research has resulted in the publication of several articles on algorithms and solutions for autonomous crane taxiing systems. Engineer Stefano Zaninotto currently works as a data analyst in the private sector, while also serving as a researcher at the Department of Aviation, Transport and Logistics and teaching at the Institute of Information and Communication Technology, both at MCAST (Malta).

Dr. Gauci Dr. Gauci holds a BSc in Electrical Engineering from the University of Malta (Malta) and a PhD in Aerospace Engineering from Cranfield University (UK). He is a Senior Lecturer at the Institute of Aerospace Technologies, University of Malta and an Adjunct Professor at Embry-Riddle Aeronautical University (worldwide). His research interests include: Unmanned Aerial Vehicles (UAVs), Machine Learning for Aviation Applications, Air Traffic Management (ATM), Avionics and Human Computer Interaction (HCI). Dr. Gauci has participated in several nationally and European funded research projects and is the author/co-author of over 35 academic papers. He is also one of the inventors on several patent applications.

Dr. Eng. Brian Zammit Dr Zammit has been involved in avionics-related research since joining the University in 2005, where he worked as a research assistant on European-funded projects. His work has led him to author and co-author several papers and patents related to aircraft operation and optimisation. Dr Zammit obtained his PhD in 2015 from the University of Malta and has since supervised, co-supervised and examined several undergraduate, masters and PhD students. Currently, he is a Senior Lecturer at the Department of Electronic Systems at the University of Malta, where he teaches Fundamentals of Electronics, Instrumentation and Data Acquisition Systems.

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