Robotics

Entry requirements

Bachelor of Science degree with 2:1 honours degree in Mechanical Engineering or a related subject.

Alternatively, a Bachelor's degree with 2:1 honours degree in any subject will be considered if a subsequent Master of Science has been taken in a relevant area.

Applicants with a 2:2 will only be considered if the Master's has been achieved with Merit.

Months of entry

January, April, July, October

Course content

The Centre for Robotics Research in the Department of Engineering has an international reputation in the field of robotics, mechanisms, kinematics, and their applications to medical science and production. Since 2010 Robotics has been part of the Department of Engineering, taking advantage of natural opportunities and synergies, continuing its record of prolific publication in prestigious journals, and securing several millions of pounds in research funding.

Staff are reputed nationally and internationally in kinematics, robotics and mechatronics, and collaborate closely with the KCL Health Schools (including St Thomas’ and Guy’s Hospitals) and numerous industrial partners. The Centre is renowned for its multidisciplinary research at the interfaces between Robotics, Computer Science and Biomedical Engineering.

The mission of the Centre for Robotics Research is to develop world-leading solutions to critical challenges faced in society where robot-centric approaches can improve outcomes. Projects largely revolve around medical themes, while recent research efforts have expanded that remit to include horticulture/agriculture, manufacturing and humanitarian demining. The centre brings together specialised expertise in innovative sensing and manipulation technologies, reconfigurable and flexible metamorphic robotic platforms, variable stiffness and soft-body materials, dynamic control methodologies for multi-robot teams and progressive modes of human-robot integration. Many of the activities emphasize interaction with people, from haptic controllers to surgical instrumentation to prosthetic devices and rehabilitation equipment, incorporating machine learning, trajectory planning and shared decision making to provide adaptive interfaces that improve performance over time.