Perception and decision making for autonomous surface vessel
PhDs and postgraduate research
Funded PhD Project (UK and EU students only)
School of Energy and Electronic Engineering
4 May 2021 (12pm GMT)
Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these cover tuition fees at the UK rate for three years and a stipend in line with the UKRI rate (£15,609 for 2021/22). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables.
The work on this project could involve:
- Develop an algorithm based on multi-sensor fusion to reliably perceive the environment/objects around the autonomous ship in various restricted environments, such as choppy or foggy.
- Develop an autonomous vessel decision-making algorithm based on reinforcement learning to realise autonomous driving in complex waterways and climates.
- Integrate and test the developed algorithms in a ship simulator or experimental ship prototype.
Unlike the industry that still takes a wait-and-see attitude towards autonomous cars, autonomous vessels(AV) are more comfortable to be applied to various industrial applications, such as marine monitoring, cargo/passenger transportation, border patrols, etc. It has brought billions of pounds of benefits to the industry, and the trend is keeping increasing year by year.
As a famous port city, it has unique geographical advantages and a mature shipbuilding environment to develop its shipbuilding industry. Therefore, the headquarters of many autonomous ship companies are located in Portsmouth and its surrounding areas, such as L3 Harris, etc. The University of Portsmouth also benefits from this. In the past few years, the mentor team of this project has led several projects in cooperation with the autonomous ship industry. The topics of these projects cover high-reliability positioning, environment awareness, fault diagnosis, etc. Funding for these projects comes from Innovate UK, EU, etc., and over £500k.
The challenge that restricts the further application of AV is that the intelligence level of AV in tricky weather or environments needs to be further improved. For example, rainy or foggy days will bring the challenge to the environmental perception of AV, and driving in a crowded and choppy channel is also a big challenge for the decision-making of AV. In this context, this project was proposed.
The successful candidate is expected to develop reliable perception algorithms based on multi-sensor fusion and decision-making algorithms based on reinforcement learning that can handle complex driving scenarios. These two algorithms will be integrated and tested on a simulator or an experimental prototype vessel.
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
You should have good knowledge of machine learning and Python or Matlab programming skills.
How to apply
We’d encourage you to contact Dr Hongjie Ma (email@example.com) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
If you want to be considered for this funded PhD opportunity you must quote project code SENE5940521 when applying.