Dependable Autonomous Systems
Lead Research Organisation:
Queen's University Belfast
Department Name: Sch of Electronics, Elec Eng & Comp Sci
Abstract
With the rollout of cost effective Internet-of Things (IoT) devices, the potential for high intelligent, autonomous systems has never been greater and brings the potential for revolutionising systems in smart infrastructure, industrial systems, etc. The objective is to enhance the capability of intelligent, embedded solutions and explore alternative network solutions such as a mobile ad hoc network (MANET) for the creation of dependable autonomous systems. Practical solutions using modern artificial Intelligent (AI) and embedded system hardware will be created as part of the project.
- Undertake research into enhancing intelligent functionality into autonomous systems by incorporating learning and reality capability into embedded systems.
- Create MANET solutions to provide enhanced networking capability for manufacturing systems that can work in challenging environments.
- Generate a working practical solution using distributed field programmable gate array (FPGA) computing platforms.
- With the rollout of cost effective Internet-of Things (IoT) devices, the potential for high intelligent, autonomous systems has never been greater and brings the potential for revolutionising systems in smart infrastructure, industrial systems, etc.
- The objective is to enhance the capability of intelligent, embedded solutions and explore alternative network solutions such as a mobile ad hoc network (MANET) for the creation of dependable autonomous systems. Practical solutions incorporating modern artificial Intelligence (AI) and embedded system hardware will be created as part of the project.
The PhD studentship is part of a major 20-20 world class universities consortium in Dependable Autonomous Systems focused on smart manufacturing and robotics, aimed at boosting innovation, the quality of research, and talent development between the UK and Jiangsu province, China. It involves Queen's University, universities of Liverpool and York, Southeast University, Soochow University and Nanjing University of Science and Technology. The funding will provide the opportunity for student exchanges to allow collaboration with the UK and Chinese partners.
- Undertake research into enhancing intelligent functionality into autonomous systems by incorporating learning and reality capability into embedded systems.
- Create MANET solutions to provide enhanced networking capability for manufacturing systems that can work in challenging environments.
- Generate a working practical solution using distributed field programmable gate array (FPGA) computing platforms.
- With the rollout of cost effective Internet-of Things (IoT) devices, the potential for high intelligent, autonomous systems has never been greater and brings the potential for revolutionising systems in smart infrastructure, industrial systems, etc.
- The objective is to enhance the capability of intelligent, embedded solutions and explore alternative network solutions such as a mobile ad hoc network (MANET) for the creation of dependable autonomous systems. Practical solutions incorporating modern artificial Intelligence (AI) and embedded system hardware will be created as part of the project.
The PhD studentship is part of a major 20-20 world class universities consortium in Dependable Autonomous Systems focused on smart manufacturing and robotics, aimed at boosting innovation, the quality of research, and talent development between the UK and Jiangsu province, China. It involves Queen's University, universities of Liverpool and York, Southeast University, Soochow University and Nanjing University of Science and Technology. The funding will provide the opportunity for student exchanges to allow collaboration with the UK and Chinese partners.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509541/1 | 01/10/2016 | 30/09/2021 | |||
| 2114431 | Studentship | EP/N509541/1 | 01/10/2018 | 31/03/2022 | Jack Steers |