Field Robotics and Learning Group

Meriem Ben Miled and Drone Robotics and Innovation Logo UCL

Our group is dedicated to advancing autonomous systems across field research in three core areas: Reliable Sensing and Perception, Intelligent Planning and Decision Making, and Advanced Robust Control. Our mission is to develop resilient technologies that enhance system reliability by integrating sophisticated sensing, perception, and adaptive control frameworks. By leveraging the latest advances in machine learning, optimization, and control theory, we address real-world challenges, enabling autonomous platforms to navigate and operate effectively in complex, dynamic environments.

news

Mar 18, 2025	🌊 Data Acquisition in River Thames
Feb 28, 2025	📃 New Applied Ocean Research published by UCL and Loughborough University
Dec 04, 2024	Dr. Yuanchang Liu delivers a talk at RiTA 2024 Conference
Nov 28, 2024	📰🎉 Our ALADDIN demonstrator project was featured in the Lloyd's Register Foundation newsletter!
Nov 25, 2024	Dr. Yuanchang Liu is appointed to the Editorial Board of Applied Ocean Research
Nov 05, 2024	Delivered the first workshop for our Clean Maritime Project funded by MarRI-UK
Nov 01, 2024	Kicked off an Innovate UK Smart Shipping Acceleration Fund funded project
Oct 14, 2024	FRLG @ IROS 2024!
Oct 01, 2024	The group welcomed two new members, Yang Hu and Junfeng Xue, to join!
Sep 23, 2024	Paper published to Ocean Engineering by Yongchang Xie
Aug 21, 2024	PhD defense by Meriem Ben Miled
Jul 18, 2024	🛠📋 UCL Festival of Engineering 2024

selected publications

T-ASE

An End-to-End Deep Reinforcement Learning Based Modular Task Allocation Framework for Autonomous Mobile Systems

Song Ma, Jingqing Ruan, Yali Du , and 2 more authors

IEEE Transactions on Automation Science and Engineering, 2024

Bib

@article{ma2024end,
  title = {An End-to-End Deep Reinforcement Learning Based Modular Task Allocation Framework for Autonomous Mobile Systems},
  author = {Ma, Song and Ruan, Jingqing and Du, Yali and Bucknall, Richard and Liu, Yuanchang},
  journal = {IEEE Transactions on Automation Science and Engineering},
  year = {2024},
  publisher = {IEEE},
}

AAAS

Adaptive Unsupervised Learning-Based 3D Spatiotemporal Filter for Event-Driven Cameras

Meriem Ben Miled, Wenwen Liu, and Yuanchang Liu

Research, 2024

Bib

@article{miled2024adaptive,
  title = {Adaptive Unsupervised Learning-Based 3D Spatiotemporal Filter for Event-Driven Cameras},
  author = {Miled, Meriem Ben and Liu, Wenwen and Liu, Yuanchang},
  journal = {Research},
  volume = {7},
  pages = {0330},
  year = {2024},
  publisher = {AAAS},
}

IROS

ShorelineNet: An Efficient Deep Learning Approach for Shoreline Semantic Segmentation for Unmanned Surface Vehicles

Linghong Yao, Dimitrios Kanoulas, Ze Ji , and 1 more author

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021

Bib

@article{yao2021shorelinenet,
  title = {ShorelineNet: An Efficient Deep Learning Approach for Shoreline Semantic Segmentation for Unmanned Surface Vehicles},
  author = {Yao, Linghong and Kanoulas, Dimitrios and Ji, Ze and Liu, Yuanchang},
  journal = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  volume = {},
  pages = {5403-5409},
  year = {2021},
  publisher = {IEEE},
}

T-ASE

Anisotropic GPMP2: A Fast Continuous-Time Gaussian Processes Based Motion Planner for Unmanned Surface Vehicles in Environments With Ocean Currents

Jiawei Meng, Yuanchang Liu, Richard Bucknall , and 2 more authors

IEEE Transactions on Automation Science and Engineering, 2022

Bib

@article{meng2022anisotropic,
  title = {Anisotropic GPMP2: A Fast Continuous-Time Gaussian Processes Based Motion Planner for Unmanned Surface Vehicles in Environments With Ocean Currents},
  author = {Meng, Jiawei and Liu, Yuanchang and Bucknall, Richard and Guo, Weihong and Ji, Ze},
  journal = {IEEE Transactions on Automation Science and Engineering},
  volume = {19},
  pages = {3914-3931},
  year = {2022},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
}

Applied Ocean

Image segmentation in marine environments using convolutional LSTM for temporal context

Kasper Foss Hansen, Linghong Yao, Kang Ren , and 3 more authors

Applied Ocean Research, 2023

Bib

@article{hansen2023image,
  title = {Image segmentation in marine environments using convolutional LSTM for temporal context},
  author = {Hansen, Kasper Foss and Yao, Linghong and Ren, Kang and Wang, Sen and Liu, Wenwen and Liu, Yuanchang},
  journal = {Applied Ocean Research},
  volume = {139},
  pages = {103709},
  year = {2023},
  publisher = {Elsevier},
}

Oceans Engineering

Reliable LiDAR-based ship detection and tracking for Autonomous Surface Vehicles in busy maritime environments

Yongchang Xie, Cassandra Nanlal, and Yuanchang Liu

Ocean Engineering, 2024

Bib

@article{xie2024reliable,
  title = {Reliable LiDAR-based ship detection and tracking for Autonomous Surface Vehicles in busy maritime environments},
  author = {Xie, Yongchang and Nanlal, Cassandra and Liu, Yuanchang},
  journal = {Ocean Engineering},
  volume = {312},
  pages = {119288},
  year = {2024},
  publisher = {Elsevier},
}