Marine, Autonomy and Robotic Systems (MARS)

Introduction

UCL Mechanical Engineering’s MARS Research Group advances the science and engineering of next-generation maritime systems through interdisciplinary innovation in naval architecture, marine engineering, and intelligent autonomous technologies.

We pioneer AI-enabled design, robotics, and high-performance computational methods to address complex hydrodynamic, structural, and operational challenges across the marine sector. Our research integrates advanced marine design, digital twins, optimisation and control of complex systems, and data-driven decision-making to enhance safety, resilience, and efficiency at sea.

We are committed to accelerating the transition toward clean energy and net-zero maritime operations, with expertise spanning offshore renewable energy systems, electrified and hybrid marine power systems, hydrogen and alternative fuels, and sustainable vessel design.

Through cutting-edge work in structural health monitoring, integrity management of large-scale marine and offshore structures, and autonomous marine robotics, we develop reliable and practical solutions for ships, offshore platforms, and emerging maritime infrastructure.

By combining state-of-the-art computational science, AI, robotics, and sustainable engineering, the group delivers transformative technologies that shape the future of global maritime transport, offshore energy, and ocean systems.

Marine Research Groups

Find out more about our work through our various research groups, labs and projects.

Our researchers and academics
Yuanchang Liu Associate Professor Chair of Marine Research
David Andrews Professor of Engineering Design
Richard Bucknall Kennedy Professor of Mechanical Engineering Head of Department
Helen Czerski  Professor of the Environment and Society
Andrea Grech La Rosa Lecturer (Teaching) Engineering Design and Industry Engagement
Alistair Greig Professor of Marine Engineering
Ema Muk-Pavic Professor (Teaching)
Jeom-Kee Paik Professor of Marine Technology
Tom Smith Senior Research Fellow in Naval Architecture
Giles Thomas Professor of Maritime Engineering
Peng Wu Lecturer (teaching) in Propulsion Systems Design/Integration
Yao Zhang Lecturer in Marine/Maritime Digitalisation and Automation

Our research highlights

1. “Uncertainty-Aware Maritime Point Cloud Detector (U-MPCD) for Autonomous Surface Vehicles”. Xie, Yongchang; Wu, Peng; Englot, Brendan; Nanlal, Cassandra; Liu, Yuanchang; (2025) Uncertainty-Aware Maritime Point Cloud Detector (U-MPCD) for Autonomous Surface Vehicles. IEEE Journal of Oceanic Engineering pp. 1-23. (In press). 10.1109/joe.2025.3612726
2. “Power characterization modeling of lithium-ion batteries under shipboard swaying conditions”. Luo, Yingbing; Wu, Peng; Kong, Laiqiang; Zhang, Hailong; Fang, Sidun; Liao, Ruijin; (2025) Power characterization modeling of lithium-ion batteries under shipboard swaying conditions. In: (Proceedings) Power Systems and Electrical Technology. IEEE (In press). https://discovery.ucl.ac.uk/id/eprint/10211302/
3. “The reflection of a planar impulsive shock wave at a liquid–gas interface”. Smith, T.A. and Bempedelis, N. (2024), Journal of Fluid Mechanics, 999, p. A34. doi: 10.1017/jfm.2024.935.
4. “Non-causal economic model predictive control for ocean wave energy maximisation based on wave-to-wire model”. Teng Gao, Yao Zhang, Tahsin Tezdogan, Ocean Engineering, Volume 344, 2026, 123610, ISSN 0029-8018, https://doi.org/10.1016/j.oceaneng.2025.123610.
5. “Interaction between a uniform current and a submerged cylinder in a marginal ice zone.” Yang, YF; Wu, GX; Ren, K; (2024) Interaction between a uniform current and a submerged cylinder in a marginal ice zone. Journal of Fluid Mechanics, 984 , Article A50. 10.1017/jfm.2024.255.
6. “Vessel trajectory classification from a graph network perspective”. Kutin, Nana; Bucknall, Richard; Wu, Peng; Liu, Yuanchang; (2025) Vessel trajectory classification from a graph network perspective. Advanced Engineering Informatics, 69 (D) , Article 104082. 10.1016/j.aei.2025.104082.

Our latest news

1. UCL Marine Research Group secures two projects in Department for Transport’s CMDC Round 6 competition

UCL Marine Research Group is part of two winning consortia in the latest Clean Maritime Demonstration Competition (CMDC), supported by the UK Department for Transport, with total awarded funding over £1 million.

These projects aim to accelerate innovation in maritime decarbonisation and digitalisation:

• Project 1: Tugbeam: Electrification, automation and efficient maintenance for tugboats (Dr Yuanchang Liu, Dr Peng Wu, and Dr Yao Zhang).
• Project 2: GREENPORTSIDE (Generating Resilient Energy Ecosystem in Newhaven Port Operations for Renewable Transition, Sustainable Infrastructure, and Decarbonisation through Electrification) (led by Dr Yao Zhang)

Read the full funding announcement

2. Marine, Autonomy and Robotic Systems (MARS) group won new grant funded by Department for Transport

As part of the total £1.8M funding, Marine, Autonomy and Robotic Systems (MARS) group recently won a TRIG research grant funded by Department for Transport through the Connected Places Catapult.

The project, titled “Deep Reinforcement Learning for Optimal Weather Routing of Wind-assisted Ships” develops an AI-powered routing system to help ships with wind-assisted propulsion reduce fuel use and emissions. By combining real-world weather data, vessel performance models, and advanced machine learning, it delivers smarter, greener voyage planning. The solution supports the maritime industry’s shift toward low-carbon, energy-efficient operations.

Focus: Maritime Decarbonisation

UCL team: Dr Peng Wu - project lead, Dr Yuanchang Liu - project co-lead, Nana Kutin - research assistant.

3. New funding awarded for Arctic Ice Forecasting Project

UCL researchers have been awarded funding from the NERC Arctic Office under the Supporting Impactful UK Arctic Science Engagement scheme for the project titled “HyAIce-Dr: A Hybrid Hydrodynamic-AI Framework for Predicting Ice Floe Drift in the Arctic Marginal Ice Zone”.

The project will develop an AI-enhanced, physically interpretable modelling framework to improve short-term forecasts of drifting sea ice in the Arctic Marginal Ice Zone. By combining hydrodynamic theory, machine learning, and observational data, the team aims to deliver an open-access tool to support safer Arctic navigation, hazard warnings, and environmental monitoring.

Focus: Arctic science, climate & maritime safety
UCL Team: Dr Yifeng Yang, Professor Helen Czerski, Dr Yuanchang Liu

Case study highlight: Reducing underwater radiated noise from small vessels

Small vessels are creating significant underwater noise that affects marine life, especially in sensitive coastal areas. In this video, Dr Tom Smith and colleagues at UCL Mechanical Engineering explore innovative solutions to reduce noise from small boats, focusing on propeller cavitation and other noise sources.