Ray-inspired sensing and morphing: bio-inspired sensor arrays inform a neural network for enhanced manoeuvrability and extended endurance

On this project, you will advance the state of the art in ray robots by implementing a bio-inspired sensing and morphing robotic ray.  

Recent developments in ray robots have demonstrated effective swimming despite their limited morphing kinematics and conventional control. However, a significant challenge for morphing water and air vehicles is finding the optimal configuration for various conditions and missions, and accurately determining the vehicle’s state when morphing interacts with different flow states. 

Current ray robots typically have limited degrees of freedom, actuating their wings (pectoral fins) by either undulating with low amplitude, slow travelling waves or by oscillating the wing with a large amplitude flap. They also use conventional sensing which provides little information on the vehicle’s flow state. 

On this project you will develop a bio-inspired robotic ray that will perform a wide range of kinematic movements inspired by real ray kinematics and will use an array of pressure sensors that mimic the sensor distribution profiles of both undulation-dominant and oscillation-dominant ray species. A neural network will be used for state estimation and performance measurements, while load cell measurements will assess the system’s closed-loop performance in improving manoeuvrability and endurance. 

As part of this project, you will construct a multi-actuator wing/fin; integrate a distributed sensor array; conduct system-characterisation tests in our Recirculating Water Tunnel (RWT) facility; train a supervised-learning network to estimate the wing’s state and forces using our high-performance computing facility, Iridis; and evaluate the system’s performance in the RWT facility. 

For informal enquiries please get in touch with the Dr Sergio Araujo-Estrada – [email protected] 

Funding for this project is offered by the Centre for Doctoral Training in Complex Integrated Systems for Defence & Security (CISDnS), which will recruit motivated and inquisitive candidates across the themes of Digital, Physical and Biological systems to provide a diverse and interconnected cohort training environment. You can read more about the Centre and the training programme at https://cisdns-cdt.ac.uk/  

To discuss aspect related to the CISDnS CDT please contact the directorate – [email protected]  

This PhD studentship is open only to UK applicants. This project is suitable for applicants with Mechanical engineering or robotics-related first degrees, some experience with control systems, and basic knowledge of machine learning. Desirable skills: previous experience with aquatic sensors and/or actuation systems in water.  

CISDnS is committed to promoting equality, diversity and inclusivity. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break or are transitioning into a new role. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. 

Closing date: 5th May 2025

Entry requirements: A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent). Following the diversity objectives of CISDnS, we will accept many forms of equivalent prior learning.

Funding: Full-time studentships will cover UK tuition fees and an enhanced tax-free stipend of approx. £24,700 per year for 4 years, along with a substantial budget for research, travel, and centre activities.  

How to apply

Search for a Postgraduate Programme of Study (soton.ac.uk).  

Select Full-time or Part-time, programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “iPhD Complex Integrated Systems in Defence & Security (Full-Time)” 

In Section 2 of the application form you should insert the name of the supervisor Dr Sergio Araujo-Estrada.

Applications should include:

curriculum vitae giving details of your academic record and stating your research interests

name two current academic referees together with an institutional email addresses in the Reference section of the application form. On submission of your online application your referees will be automatically emailed requesting they send a reference to us directly by email.

your academic transcript and degree certificate (translated if not in English) – if you have completed both a BSc & an MSc, we require both.

include a short statement of your research interests in the Personal Statement section of the application form.