Shallow water environments pose daunting scenarios for the operation of Unmanned Underwater Vehicles (UUVs), due to significantly larger wave disturbances being present in comparison to a typical deep sea situation. Performing inspection and maintenance tasks at close quarters in these conditions requires reliable control methods robust to external disturbances, allowing accurate position and attitude control, an aspect which classical control methods are often lacking. Improved performance can be achieved through predictive control methods, however, these require accurate and time-efficient estimations of the hydrodynamic forces produced by the immediate ocean environment around the vehicle. Considering this, we present a low-order model for faster-than-real time estimation of the wave-induced hydrodynamic forces acting on a submerged vehicle in various sea state conditions. The model is thoroughly corroborated by experimental tests, performed using a Remotely Operated Vehicle (ROV) situated at shallow depth whilst subjected to realistic sea wave disturbances. Validation between simulations and the collected experimental data showed a maximum normalised mean error deviation of 0.16 and 0.27 for surge and heave forces respectively, and 0.34 for the pitching moment. This empirical evidence demonstrates that accurate predictions of wave-generated forces can be produced through low-order models at a speed suitable for incorporation within predictive control architectures.

Deployment of the Internet of Things (IoT) in real industrial scenarios is fraught with unseen challenges and planning intricacies. The inside engineering and methodologies behind a successful Industrial IoT (IIoT) setup are seldom discussed. In this paper, we describe a systems approach to identify, integrate, and deploy the different IIoT modules in a bottom-up manner. The process that go from first identifying and labelling the various assets to be monitored, to their abstraction and integration for a given industrial scenario, forms the major crux of the work presented in this paper. We deployed several sensor nodes in the Offshore Renewable Energy Catapult site, located in Blyth, UK. Each sensor node monitored a specific asset and communicated via LoRaWAN to our local Data Hub. A simple query interface and visualisation dashboard allowed real-time data assessment and rapid asset monitoring. To make the proposed approach easy to comprehend and applicable to other scenarios, we extracted a minimal translational bottom-up flowchart. The systems approach described here offers a robust methodology to plan out a real-world IIoT deployment.

Public perceptions of Robotics and Artificial Intelligence (RAI) are important in the acceptance, uptake, government regulation and research funding of this technology. Recent research has shown that the public's understanding of RAI can be negative or inaccurate. We believe effective public engagement can help ensure that public opinion is better informed. In this paper, we describe our first iteration of a high throughput in-person public engagement activity. We describe the use of a light touch quiz-format survey instrument to integrate in-the-wild research participation into the engagement, allowing us to probe both the effectiveness of our engagement strategy, and public perceptions of the future roles of robots and humans working in dangerous settings, such as in the off-shore energy sector. We critique our methods and share interesting results into generational differences within the public's view of the future of Robotics and AI in hazardous environments. These findings include that older peoples' views about the future of robots in hazardous environments were not swayed by exposure to our exhibit, while the views of younger people were affected by our exhibit, leading us to consider carefully in future how to more effectively engage with and inform older people.