Motion planning and control for autonomous surface vehicles

Research in autonomous surface vehicle (ASV) has been gaining attention in the recent decade due to the increasing need of reconnaissance in hostile environments and maturity of the technology. However, one of the few remaining problems yet to be solved is the accurate and robust navigation in constrained environment, where the capability of conventional navigation devices such as radar, GPS and LiDAR are largely compromised. This study will maintain a holistic view and seek the solution to these issues by developing an 'intelligent' navigation system. The main task of the study is to develop an autonomous navigation system for an ASV using the state-of-the-art machine learning algorithms. It involves activities in developing an neurobiologically inspired algorithm to generate the navigation paths conform to regulations and developing an interface with navigational instruments as well as the directional control system. This project will be undertaken within the department of mechanical engineering and will also work closely with industrial and academic partners. A unique opportunity will be provided for the PhD student to interact with both national and international researchers.

Person Specification
Knowledge, Education, Qualification and Training
Essential: Applicants should have a first or second class UK honours degree, a mater degree or equivalent in control engineering, computer science, electronic engineering or similar.

Experiences:
• Essential: Good problem solving and analytical skills;
• Essential: Knowledge of computer vision and robotics;
• Essential: Evidence of programming with Matlab and/or C/C++;
• Essential: Good written and oral communication skills;
• Desirable: Experience in robotic sensory fusion and machine learning.

Closing Date and Start Date
We will be continuously having informal discussion with interested candidates until this position is filled.

Application Process
Eligible applicants should first contact Dr Yuanchang Liu, ([Email Address Removed]) quoting the Job reference. Please enclose a cover letter (including the names and contact details of two referees), one-page research statement and two pages CV. The supervisory team will arrange interviews for short-listed candidates. After interview, the successful candidate will be required to formally apply online via the UCL website.