Supervisory Team: Harold Chong and Christine Evers
Project description
Autonomous robots navigate using optical systems such as cameras and Light Detection and Ranging (LiDAR) that are restricted to direct Line-Of-Sight (LOS) transmission and reception of obstacle signals, which will be used for image reconstruction. Acoustic sensors are used for Non-Line-Of-Sight (NLOS) detection, but they are constrained by the properties of the surface materials, range, and interference from natural and artificial sounds generated in its operating environment. For sound source localisation, active acoustic sensors and beacons have been used to map and guide the robot’s movement [1,2]. This method generates an image of the environment using a network of sensors under continuous communication, but the high-power consumption and lack of spatial inference for machine learning necessary for embedded artificial intelligence in autonomous robotic systems poses a technical challenge.
To address this issue, we propose a low power ultrasonic sensor that has a low form factor and can be integrated to the body of the robot for detection and ranging applications.
Therefore, the project will develop a Micro-Electro-Mechanical-System (MEMS) ultrasonic sensor with a detection range of up to 2 m and a sub-MHz frequency of 125 kHz to 500 kHz. Due to the sensor’s high frequency operation, it will have a high immunity to acoustic interference. The signals from the MEMS ultrasonic sensor will be used by machine learning algorithms developed in collaboration with a parallel PhD project, for spatial inference that exploit the ego-motion of the robot.
This project is funded through the UKRI MINDS Centre for Doctoral Training (www.mindscdt.ai). This is one of 16 PhD training centres in the UK with a unique focus on advancing AI techniques in the context of real-world engineered systems with a remit that spans novel hardware for AI, AI and machine learning, pervasive systems and IoT, and human-AI collaboration. We provide enhanced cross-disciplinary training in electronics and AI, entrepreneurship, responsible research and innovation, communication strategies, outreach and impact development as part of an integrated 4-year iPhD programme.
The MINDS CDT is based in a dedicated laboratory on Highfield Campus at the University of Southampton. The lab provides a supportive environment for individual research, ideas sharing and collaboration, and the wider campus provides access to substantial high-performance computing (including dedicated GPU servers), maker and cleanroom facilities. You will take part in our annual, student-designed innovation camps, be able to work with industry and government partners through our internship scheme and be able to take part in exchanges with international university partners.
Entry Requirements
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).
Closing date: 25 June 2021
Funding: full tuition for UK Students an enhanced stipend £18,285, tax-free per annum for 4 years.
How To Apply
Applications should be made online. Select programme type (Research), 2021/22, Faculty of Physical Sciences and Engineering, next page select “iPhD Machine Intelligence for Nano-electronic Devices and Systems. (Full time)”. In Section 2 of the application form you should insert the project title and name of the supervisor.
Applications should include:
Research Proposal
Curriculum Vitae
Two reference letters
Degree Transcripts to date
Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page
For further information please contact: [Email Address Removed]
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