Welcome to ARQ: Advanced Robotics @ Queen Mary
[University Centre of Excellence]
The Centre for Advanced Robotics @ Queen Mary (ARQ) is a cross-faculty multidisciplinary research centre aiming to bring Queen Mary robotics activities under one roof, increasing visibility, facilitating collaboration, public engagement and moving towards critical mass. We are a major robotics research centre in the UK. One of the main aims of ARQ is to amalgamate, facilitate and promote research and teaching in Advanced Robotics in the widest sense. Our ambition is to underpin existing Queen Mary robotics research activities, to create a focal point for research in robotics and to provide world-class under-/postgraduate research and teaching in robotics. In 2019 we got the "Centre of Excellence Status" of Queen Mary University of London.
Our research portfolio (over £4m since 2017) includes projects funded by the European Research Commission, UK Research and Innovation, British Council, industrial partners and other organisations. Currently, ARQ includes over 20 core and associated academics members and over 35 researchers and PhD students. Every year we are involved in supervision of over 80 undergraduate and master level final year projects. We are running several teaching programmes in robotics.
Our research areas cover robot design and mechatronics, human-robot interaction, control and systems engineering, autonomous systems, field robotics, sensing and biomedical mechatronics. Our core specialised robotics teams are: Team Robotix (Kaspar Althoefer), CRISP - Cognitive Robotics and Intelligent Systems for the People (Lorenzo Jamone), HAIR - Human Augmentation and Interactive Robotics (Ildar Farkhatdinov), Robotic Systems Group (Dr Ketao Zhang). See more associated teams here. Our publications can be found here.
ARQ's activities span across the School of Electronic Engineering and Computer Science (EECS), the School of Engineering and Material Science (SEMS), School of Biological and Chemical Sciences and Medical faculty. The Centre is led by Professor Kaspar Althoefer (EECS/SEMS).