Unlocking the Depths: The Transparent Ocean Research Programme

As an emerging technique that integrates imaging and spectroscopy, HSI enables the acquisition of spectral data from a wide frequency range along with the spatial information. Thus, it can detect minor differences in terms of temperature, moisture and chemical composition, making it a unique solution for non-intrusive inspection far beyond conventional techniques. Existing non-intrusive inspection techniques include visible cameras, ultrasound, thermal imaging and laser, and deliver information about physical integrity of structures but not on material composition. To this end, hyperspectral imaging has provided a unique way to tackle this challenge. Through capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging can detect minor differences in terms of temperature, moisture and chemical composition. As a result, it has been successfully applied in several emerging applications such as food and drink, remote sensing, arts verification and forensics. However, application of hyperspectral imaging in remanufacturing is rare, though some limited work for plastic sorting is reported. Hyperspectral imaging can be extremely useful in EoL composite (re)manufacturing, including quality grading and non-destructive evaluation, where the nature of fibre and polymer resins are often unknown.

Arctic Sea ice has become a hot topic in remote sensing as it plays a critical role in the weather and global climate systems. Every summer, the Arctic ice cap melts to what scientists call its "minimum" before colder weather begins to cause ice cover to increase. During the last few decades, Arctic temperatures have increased while annual and seasonal Arctic Ocean Sea ice cover has decreased. Thus, there is a growing need to understand how the Arctic Ocean responds to climate change caused by both natural and anthropogenic factors. Although satellites can provide reliable observations, the instrumental records extend back only a few decades and large interannual and decadal variability exists, the causes of ongoing Arctic climate change remain unclear. In this case, a precise sea ice segmentation can be extremely useful, which can be used to measure the distribution of ice floes in order to support the climate prediction and can also bring a significant impact on ice navigation and offshore activities.

Aerial remote sensing plays an important role in applied earth observation, especially for applications in land/ocean survey, precision agriculture and city planning. With the rich spectral and spatial information combined, hyperspectral imaging can provide a unique way for non-destructive inspection and monitoring of the earth surface. This will include two important tasks, land cover classification and change detection, where new AI machine learning algorithms will be developed, especially in an unsupervised manner where intensive data labelling can be skipped.

Considering the high-cost but poor structure/shape and texture features of the sonar systems for recognising obstacles on or near the sea surface, a cost-effective laser-based structure light system is proposed to capture the high-quality images in the distance for more accurate detection and classification of obstacles. By combining various cutting-edge techniques from image acquisition, enhancement, feature extraction and analytics to machine-learning based interpretation, we aim to solve a series of technical challenges. These include: 

1. Poor resolution and high-cost for imaging the objects of interest 

2. Short working range for most optic systems

3. No available solutions for reliable and accurate detection and classification of underwater obstacles

As a result, the detection range and accuracy can be significantly improved, which can further benefit refined decision-making to tackle the detected obstacles, including optimised recommendations, for example path-planning for smoother and more autonomous operations of naval missions and other maritime tasks. This is one of the three projects funded worldwide under the London Apex TechBridge Call, funded by Office of Naval Research, USA and Royal Navy, UK. 

As an emerging technique for non-destructive inspection, hyperspectral imaging has been successfully applied in various applications for quality inspection and control of materials and products, including smart manufacture, agri-food, pharmaceutical, mining, forensics and military sectors. This project will aim to apply hyperspectral imaging for characterising different metal powders, including Titanium, Aluminium, Tungsten, Steel, and Nickel. Using samples of the metal powder provided by Carpenter Additive, our researchers identified several tests which could offer valuable insight into the materials and their suitability for use in producing specific 3D printed components. 

Current ROV-based manipulation systems lack accuracy, and since their control is based on direct line-of-sight visual feedback, the capability for more complex tasks is limited, especially in low visibility conditions. Diver-based operations, although far more intuitive and adaptable, are expensive in both monetary terms and carbon footprint, and inefficient due to the limited power capabilities of humans. By combining power enhancement with more intuitive control, improved perception, and artificial intelligence (AI) driven smart sensing and data fusion, SeaSense will help to remove divers from hazardous environments during subsea operations.

This project will contribute to the future vision of offshore operations where optimised or unmanned/remote automated operations are the norm. Whilst this project is targeting subsea ROV operations where the developers believe the greatest benefits can be realised, a successful outcome could pave the way for other use-cases of haptic technology in offshore operations.