# 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  
Railway track constantly degrades with use. It is subject to the enormous stresses of the lateral and longitudinal forces from trains [44]. Environmental conditions such as heavy rain, extreme cold, snow, scorching heat, etc., accelerate degradation. The first significant study on the use of robotic technology for track maintenance was carried out by [PERSON] et al. The project, SysTem for Autonomous Railway TRACK operations (STAR-TRACK), aimed to studying the feasibility of using robots in track maintenance and developing a multi-robot autonomous track maintenance system for the high-speed Shinkansen line to perform two specific tasks; loosening bolts after detection and assembling new fasteners automatically [45]. Other attempts have been made to address different tasks, such as railroad crossing inspection [46], faulty rail profile detection [47], crack detection [48], etc. Table 2 shows a summary of different track inspection robots. \begin{table}
\begin{tabular}{c c c c} \hline
**Type** & **Main Function** & **Sensing Method** & **Key Technology and Contribution** \\ \hline STAR-TRACK [45] & Fastener maintenance & Monocular camera & Multi-robot autonomous track maintenance \\ \hline Train-borne system [49] & Fastener inspection & TV camera & Neural classifier for fastener detection \\ \hline Train-borne system [50] & Fastener detection & Video camera & Artificial lighting source to reduce ambiance noise \\ \hline Visual inspection system for railway (VISyR) [51] & Fastener detection & Camera & Computer vision and machine learning, a maximum speed of 200 km/h \\ \hline Train-borne system [52] & Fastener detection & Camera & Machine learning, structured lighting condition \\ \hline Manual trolley [53] & Turn out and tie detection & Multiple cameras & Slow speed, a machine learning algorithm \\ \hline RRV [54] & Track inspection & Four cameras, GPS & Defect location based on GPS \\ \hline Train-borne system [55] & Fastener detection & CCD camera, GPS & Machine learning for detection and GPS for location information \\ \hline Comprehensive track inspection vehicle (CITV) [56] & Fastener detection & Camera & Fastener quality based on image and machine learning algorithm \\ \hline Modified truck with sensor [46] & Railroad crossing inspection & Camera, LiDAR & Pattern recognition, 3D point cloud using support vector machine (SVM) \\ \hline Diagnostic analysis for railways and trams (DART) [57] & Track geometry & Track measurement & Computer-assisted diagnostic tool \\ \hline Hand-pushed inspection device [47] & Faulty rail profile detection & Multiple laser camera & 3D modeling, deep learning model for fault detection \\ \hline \end{tabular}
\end{table} 
Table 2: Track inspection robots. Fasteners fix the rails to the sleepers. If fasteners are missing, the stiffness of the track is reduced. A computer vision-based approach, based on wavelet transform (WT) and principal component analysis (PCA), was developed by [PERSON] et al. to detect the absence of fasteners [49] using a camera, which increased detection reliability than manual inspection. Another computer vision-based missing clip detection technique was developed by [PERSON] et al. using a video camera and an artificial lighting source to eliminate the inconsistent illumination effect in image processing [50]. [PERSON] et al. presented a visual inspection system for railways (VISyR) to determine the presence of fasteners using computer vision and machine learning at a maximum speed of 200 km/h [51]. Another machine learning-based fastener inspection method was proposed by [PERSON] et al., who combined unstructured and structured lighting conditions to reduce the impact of light, vibration, and obstacles [52]. This proposed method was more reliable in practical conditions as its training dataset was robust. A conceptual hand-pushed cart with computer vision and a machine learning algorithm was developed for turnout detection and tie detection [53], as shown in Figure 5a. [PERSON] et al.