# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 3 Robotic Technologies Used in Railway Track Inspection and Maintenance
### Robotic Inspection Technologies in Railway
#### 3.1.1 Track Inspection
proposed another vision-based track component inspection system that could detect multiple components using 4 cameras with geo-location from onboard GPS, as shown in Figure 5b [54]. [PERSON] et al. also used machine learning and computer vision for missing fastener detection with a high-speed charge-coupled device (CCD) camera [55]. [PERSON] et al. proposed a machine learning approach based on histograms of oriented gradients (HOG) and trained the model using images collected from the comprehensive track inspection vehicle (CTIV), which could detect missing clips and classify existing clips into different categories [56]. Track geometry parameters describe the quality of the track and issues that might need repair interventions. The diagnostic analysis of railways and trams (DART) was able to point out the weak location of railway tracks by calculating and measuring multiple geometrical components using a laser scanner [57]. Later, those degraded or deteriorated \begin{table}
\begin{tabular}{c c c c} \hline
**Type** & **Main Function** & **Sensing Method** & **Key Technology and Contribution** \\ \hline Railpod [58] & Track inspection & \begin{tabular}{c} Customizable based \\ on requirements \\ \end{tabular} & \begin{tabular}{c} Easy to transport, both rail and \\ pneumatic wheel \\ \end{tabular} \\ \hline Robot trolley with sensors [59] & Fatigue cracks on track & \begin{tabular}{c} Alternating current field \\ measurement (ACFM) \\ \end{tabular} & \begin{tabular}{c} Automatic crack detection robot \\ \end{tabular} \\ \hline AutoScan [48] & Crack detection on track & \begin{tabular}{c} Electromagnetic acoustic \\ transducer (EMAT) \\ \end{tabular} & Manipulator for inspection \\ \hline RIIS1005 [22] & Multiple track defects & Camera, LiDAR & \begin{tabular}{c} Easy-to-assemble and -disassemble, \\ pattern recognition, deep learning \\ \end{tabular} \\ \hline Felix [60] & \
\begin{tabular}{c} Switches and \\ crossings (S\&C) \\ \end{tabular} & laser &
\begin{tabular}{c} Artificial vision system, wireless \\ data transfer \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 5: Track inspection with multiple cameras: (**a**) inspection trolley [53]; (**b**) modified RRV [54]. track locations for potential future defects were further inspected by a human. Using multiple laser cameras, [PERSON] et al. created 3D modeling of the rail track in order to train it and thus identify defects [47]. In recent years, multiple state-of-the-art robotic solutions have been developed for track inspection, as shown in Figure 6, which are easy to transport and reduce human involvement. Railpod, shown in Figure 6a, is an inspection platform that can perform autonomous navigation along the track or can be controlled by remote operation. Because of having both the pneumatic and rail wheels, it can be easily transported along the road [58]. [PERSON] et al. proposed a system consisting of a commercially available alternating current field measurement (ACFM) sensor attached to a trolley and a robot for detecting rolling contact fatigue cracks in rails [59], as shown in Figure 6b. AutoScan, a project from Horizon 2020, is another example of an autonomous robotic inspection platform with a manipulator for close inspection, shown in Figure 6c. Initially, rolling contact fatigue (RCF) or electromagnetic acoustic transducer (EMAT) sensors are used to find the defect [48]. Then, the operator can use the manipulator to perform further inspection by standing on the side of the rail. RIIS1005 is another intelligent track inspection robot developed by Shenhao Technology, as shown in Figure 6d [22]. The combination of deep learning, pattern recognition, and feature-matching algorithms can detect multiple defects on rail surfaces, fasteners, ties, etc. Finally, Felix, shown in Figure 6e, an automated inspection start developed by Loccioni, was designed to inspect the switches and crossings (S&C) and track with a maximum operational speed of 5 km/h [60].