Mobile robots are playing a significant role in Higher Education science and engineering teaching, as they offer a flexible platform to explore and teach a wide-range of topics such as mechanics, electronics and software. Unfortunately the widespread adoption is limited by their high cost and the complexity of user interfaces and programming tools. To overcome these issues, a new affordable, adaptable and easy-to-use robotic platform is proposed. Mona is a low-cost, open-source and open-hardware mobile robot, which has been developed to be compatible with a number of standard programming environments. The robot has been successfully used for both education and research at The University of Manchester, UK.

The inspection of aquatic environments is a challenging activity, which is made more difficult if the environment is complex or confined, such as those that are found in nuclear storage facilities and accident sites, marinas and boatyards, liquid storage tanks, or flooded tunnels and sewers. Human inspections of these environments are often dangerous or infeasible, so remote inspection using unmanned underwater vehicles (UUVs) is used. Due to access restrictions and environmental limitations, such as low illumination levels, turbidity, and a lack of salient features, traditional localisation systems that have been developed for use in large bodies of water cannot be used. This means that UUV capabilities are severely restricted to manually controlled low-quality visual inspections, generating non-geospatially located data. The localisation of UUVs in these environments would enable the autonomous behaviour and the development of accurate maps. This article presents a review of the state-of-the-art in localisation technologies for these environments and identifies areas of future research to overcome the challenges posed.

The AVEXIS (aqua vehicle explorer for in-situ sensing) underwater vehicles have been developed to allow characterization and monitoring of hazardous underwater environments with limited access points. A number of forms of the vehicle are being developed to assist in the decommissioning of the Sellafield nuclear facilities in Cumbria, U.K. The vehicles have been designed to be low-cost and have been constructed using novel manufacturing methods, such as 3D printing, which allows them to be built quickly and adds a high level of flexibility to the design. An acoustic communications and positioning system has also been developed which can be integrated into the vehicle or used as a stand-alone system which can be retrofitted onto existing vehicles.

Offshore wind farms will play a vital role in the global ambition of net zero energy generation. Future offshore wind farms will be larger and further from the coast, meaning that traditional human‐based operations and maintenance approaches will become infeasible due to safety, cost, and skills shortages. The use of remotely operated or autonomous robotic assistants to undertake these activities provides an attractive alternative solution. This paper presents an autonomous multirobot system which is able to transport, deploy and retrieve a wind turbine blade inspection robot using an unmanned aerial vehicle (UAV). The proposed solution is a fully autonomous system including a robot deployment interface for deployment, a mechatronic link‐hook module (LHM) for retrieval, both installed on the underside of a UAV, a mechatronic on‐load attaching module installed on the robotic payload and an intelligent global mission planner. The LHM is integrated with a 2‐DOF hinge that can operate either passively or actively to reduce the swing motion of a slung load by approximately 30%. The mechatronic modules can be coupled and decoupled by special maneuvers of the UAV, and the intelligent global mission planner coordinates the operations of the UAV and the mechatronic modules for synchronous and seamless actions. For navigation in the vicinity of wind turbine blades, a visual‐based localization merged with the location knowledge from Global Navigation Satellite System has been developed. A proof‐of‐concept system was field tested on a full‐size decommissioned wind‐turbine blade. The results show that the experimental system is able to deploy and retrieve a robotic payload onto and from a wind turbine blade safely and robustly without the need for human intervention. The vicinity localization and navigation system have shown an accuracy of 0.65 and 0.44 m in the horizontal and vertical directions, respectively. Furthermore, this study shows the feasibility of systems toward autonomous inspection and maintenance of offshore windfarms.