Flexible laser-based gas monitoring in soft robotics

This PhD project aims to develop a flexible, laser-based gas monitoring platform integrated within soft robotic systems for real-time detection of hazardous gases in inaccessible environments. The research will focus on advanced laser spectroscopic sensing techniques implemented in fibre-based architectures, enabling compact, lightweight, and highly sensitive gas detection. Target gases include ammonia (NH₃), hydrogen (H₂), and methane (CH₄), all of which are critical in fuel transportation and energy infrastructure due to their flammability and toxicity.

The project will explore wavelength-selective laser spectroscopic sensing for high specificity and sensitivity, alongside fibre design optimization to enhance gas diffusion, signal strength, and mechanical resilience. Integration of the sensing fibre into soft robotic platforms will be a key challenge, requiring innovative approaches to ensure flexibility, durability, and minimal performance degradation under deformation.

The envisioned system will enable soft robots to navigate confined or hazardous environments, such as pipelines, storage facilities, or industrial plants, where human access is limited or unsafe. By embedding distributed sensing capabilities directly into the robot’s structure, the platform will provide continuous, real-time monitoring of gas leaks or accumulation.

This interdisciplinary research combines photonics, soft robotics, and sensing technologies, aiming to deliver robust, scalable solutions for industrial safety and environmental monitoring. The outcomes have the potential to significantly enhance autonomous inspection systems in energy and transportation sectors.

Primary objectives:

1. Develop fibre-based laser sensing systems for selective detection of NH₃, H₂, and CH₄
2. Design and optimize optical fibres for enhanced gas-light interaction and sensitivity
3. Integrate flexible, miniature sensing fibres into soft robotic platforms
4. Achieve real-time gas monitoring in confined or inaccessible environments
5. Improve robustness and durability of sensing systems under dynamic motions
6. Validate system in realistic operational scenarios relevant to industrial safety

Required skills: 

1. Background in optics or electrical engineering
2. Experienced in optical design and signal processing
3. Basic understanding of soft robotics or flexible systems
4. Programming skills for data acquisition and analysis (e.g., Python, MATLAB)
5. Signal processing and data interpretation skills
6. Ability to work in an interdisciplinary research environment

Please note that this advert will close as soon as a suitable candidate is found.

• a 2:1 undergraduate degree (or equivalent) in Electronic and Computer Science or Mechanical Engineering, possibly supported by an MSc Degree.
• the University’s English language requirements

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere

Further information and other funding options.