Importance and Applications of Robotic and Autonomous Systems (RAS) in Railway Maintenance Sector: A Review <s> RAS in Rolling-Stock Fluid Servicing <s> Abstract Maintenance is an efficient and cost effective way to keep the function of the product available during the product lifecycle. Automating maintenance may drive down costs and improve performance time; however capturing the necessary information required to perform certain maintenance tasks and later building automated platforms to undertake them is very difficult. This paper looks at the creation of a novel methodology tasked with firstly the capture and classification of maintenance tasks and finally conceptual design of platforms for automating maintenance. <s> BIB001 </s> Importance and Applications of Robotic and Autonomous Systems (RAS) in Railway Maintenance Sector: A Review <s> RAS in Rolling-Stock Fluid Servicing <s> This paper presents findings of a case study conducted to introduce industrial robots into automatic train coupler inspection of Siemens Class 380 rolling-stock. The targets are localized by coalescing RGB and time of flight (ToF) sensor data. The study examines several supervised machine learning techniques to improve the overall accuracy of 3D point clouds. A cost factor which reflects root mean square, mean absolute error and coefficient of determination is defined to evaluate the performance of the learning algorithms. The best-suited models are further validated using simulation data and selected to include in overall robotic sensing system. <s> BIB002 </s> Importance and Applications of Robotic and Autonomous Systems (RAS) in Railway Maintenance Sector: A Review <s> RAS in Rolling-Stock Fluid Servicing <s> Rail traffic in passenger miles [in the UK] is projected to increase by 100% over the next 30 years, which presents a considerable challenge for the current infrastructure to perform the regular fluid servicing tasks. Developing robotic autonomous systems for train fluid servicing is a prospect for which no solutions currently exist. Therefore, the economic and technical feasibility of a robotic autonomous system to perform several key fluid servicing tasks on passenger train vehicles is investigated. The fluid servicing tasks chosen include those that to a significant degree are repetitive or hazardous for humans to perform, and therefore if performed by a robotic autonomous system will release service personnel to focus on more suitable tasks. The economic and technical cases presented strongly support the use of a robotic autonomous system for fluid servicing of trains. Generally available robotic autonomous system technology has reached a state of development capable of delivering what is required once reliable couplings and fluid hose technologies have been developed for this application. Overall, the findings are that fluid servicing capacity will at least double for around 15% of the cost of an equivalent manual servicing facility, which represents a substantially attractive business case. There will be modest technical challenges to be overcome that will add unknown cost elements such as modifications to vehicle fluid ports for robotic autonomous system compatibility and development of long power hoses. <s> BIB003
Thus far, the authors were unable to locate a RAS, which is successfully implemented in a rolling-stock fluid changing task. However, there are several ongoing studies to introduce RAS in rolling-stock fluid changing tasks. Atherton et al BIB003 have conducted a technical feasibility study in introducing RAS to passenger train fluids, such as fresh water, coolant, screen wash, fuel, effluent, and wheel sand servicing tasks. One of the challenges identified in this research is the different sizes and the positioning of fluid ports in fleets. Through this study, it has been concluded that the implementation of RAS in passenger train fluid servicing task is viable. Further, two preliminary conceptual designs, which consist of a Cartesian robot and an articulated robot, are proposed. Further, Farnsworth and Tomiyama BIB001 looked into automated transmission fluid changing task of rolling stock with an industrial engineering aspect. They have introduced a novel methodology to capture, classify, and design automated platforms for industrial maintenance based on a case study that was conducted on Bombardier Class 222 diesel train transmission fluid changing process. In contrast, work that was carried out by Vithanage et al.  emphasizes the technical aspect of the gear fluid changing tasks as compared to other high-level investigations conducted. Through this research, they have successfully adopted Aggregated Channel Feature, an advanced machine learning algorithm to detect the drain and filler plugs of Siemens Class 380 rolling-stock main gearbox and curved coupler. Further, they have fused multiple sensors and introduced an Artificial Neural Network, which consists Levenberg-Marquardt learning algorithm to localize relatively smaller components in partially organised worlds, such as railway maintenance depots with ±1 mm accuracy BIB002 .