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This is one of two research projects studying unmanned aerial and surface vehicles (UAVs and USVs). Shanghai Jiao Tong University is the home institution for this project. View the Melbourne-based partner project.
Unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs) can be used collaboratively to finish a complex task so as to reduce the cost and the risk involved. Hence, it has a significant potential in maritime search and rescue, in which uncertainties in the environments are huge with the limited sensing ability in extremely tough weather conditions and the scale and complexity of rescue tasks. As the first step toward autonomous rescue, this project aims to solve open fundamental questions in maritime search and rescue by designing collaborative control algorithms between a group of USVs and UAVs by using data collected from onboard sensors.
The objectives of this project are:
- Study the data-based rapid identification method of landing landmarks to realize the intelligent environment perception of UAV's autonomous landing.
- A data-based optimization design framework is proposed to solve the problem of UAV-USV cooperative control.
- Develop data-driven optimization technology to solve the control problem of UAV and USV under uncertain conditions.
Shanghai Jiao Tong University: Professor Weidong Zhang
The University of Melbourne: Professor Ying Tan, Dr Ye Pu
*Click on the researcher's name above to learn more about their publication and grant successes.
Who we are looking for
We are seeking a PhD candidate with the following skills:
- Demonstrated research experience in the field of automation and engineering.
- Demonstrated ability to work independently and as part of the team.
- Demonstrated time and project management skills.
- Demonstrated ability to write research reports or other publications to a publishable standard (even if not published to date).
To apply for this joint PhD opportunity, and to view the entry requirements, visit How to apply. Please read the application guidelines and eligibility information for both UoM and SJTU before contacting the lead supervisors.
First published on 26 April 2022.
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