Degree type
PhD
Closing date
27 March 2023
Campus
Launceston
Citizenship requirement
Domestic/International
About the research project
Brief Description: Recently the autonomous surface vessels have been developed used the more and more widely in forms of autonomous ferries, fully-electrically operated propulsion vessels, and observation class autonomous surface boats. In order to increase their autonomy, many intelligent algorithms have been developed.
The project involves development of novel path planning/re-planning and guidance navigation control algorithm for an ASV with machine learning approach. A trend in machine learning algorithms has recently shown its smart and intelligent property to increase autonomy and adaptation of autonomous vehicles operating in dynamic changing environments, especially with moving obstacles. A smart and intelligent real-time path planning/replanning and control algorithm will be proposed by applying a machine learning method. The machine learning method to be selected will be the most applicable in autonomous surface vessel for a full mission, such as lawn mowing survey pattern with sampling, search and rescue mission, or object collision avoidance control mission. Once the proposed algorithm has been formulated it will be verified and validate by both numerical simulation and field experiments in real-world conditions using a model scaled surface vessel with good manoeuvrability.
Purposes and Targets: The main purpose is to solve the problem of online real-time implementation of complete coverage path replanning and trajectory tracking control of ASVs due to the changing environments and obstacles existing in the transections of mission, especially the moving obstacles like surrounding boats and vessels. The target is to formulate the proposed algorithm, and then verify and validate it by both numerical simulation and field experiments using a real-world ASV under real-world changing environment.
Primary Supervisor
Meet Dr Hung NguyenFunding
Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:
- a living allowance stipend of $31,500 per annum (2023 rate, indexed annually) for 3.5 years
- a relocation allowance of up to $2,000
- a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)
If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.
As part of the application process you may indicate if you do not wish to be considered for scholarship funding.
Eligibility
Applicants should review the Higher Degree by Research minimum entry requirements.
Selection Criteria
The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.
Additional essential selection criteria specific to this project:
- Understanding of guidance navigation and control of a marine vehicle (surface vessel)
- Experience in programming, especially real-time programming, with various languages such as MATLAB/Simulink, C/C++/C#, Python
Additional desirable selection criteria specific to this project:
- Understanding of marine (surface) vehicle hydrodynamics/dynamics
- Prior knowledge and skills as well as experience in control, instrumentation and electrical/electronic engineering
Application process
There is a three-step application process:
- Select your project, and check you meet the eligibility and selection criteria;
- Contact the Primary Supervisor, Dr Hung Nguyen to discuss your suitability and the project's requirements; and
- Submit an application by the closing date listed above.
- Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
- As part of your application, you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.
Following the application closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.
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