Improvement of autonomous railway monitoring robot prototype as fault detection on railway

One of the causes of accidents on trains to date has been caused by damage of railways such as cracked rails, broken rails and other rail defects. Currently, the inspection of the railway line is still carried out manually by the railway inspection officer or Petugas Penilik Jalan Rel (PPJ). However, there is a prototype Autonomous Railways Monitoring Robot (ARMR) which is a prototype inspection robot that can perform rail line checks automatically and be monitored remotely to detect any faults on the railway. The ARMR prototype detects faults as well as locations that are subsequently delivered to users by utilizing some Internet of Things (IoT) features. Faults detected by ARMR prototypes are faults as rail damage. But, the ARMR prototype is unable to distinguish a fault and rail connection. This final task aims to improve armr prototype capabilities to produce information in the form of depth and width of faults, and to distinguish faults and rail connections. The VL6180X sensor is a laser- based distance sensor used as a depth detector for rail faults and rail connections. The Incremental Encoder is enabled to measure the width of the rail fault. The result of ten experiments at 20 cm/s, the ARMR prototype was able to produce 78 mm fault depth size information with an error value of 1,67%, produce 5 mm wide fault width information with an error value of 2,48 % and be able to distinguish faults and connections with 72 % accuracy for the comparison of the distance between the ARMR prototype and the distance shown on google maps.One of the causes of accidents on trains to date has been caused by damage of railways such as cracked rails, broken rails and other rail defects. Currently, the inspection of the railway line is still carried out manually by the railway inspection officer or Petugas Penilik Jalan Rel (PPJ). A prototype of Autonomous Railways Monitoring Robot (ARMR) is a prototype of inspection robot that can perform rail line checks automatically and be monitored remotely to detect any faults on the railway. The robot detects the width of the rail fault by reading the number of pulse train sent by the encoder and the depth of the rail fault by reading the time delay signal received by the distance sensor. The result of rail fault and location are subsequently delivered to users by utilizing several features of the Internet of Things (IoT). Faults detected by ARMR prototype, but it is unable to distinguish a fault and rail connection. To overcome this weakness, a camera is used that will capture the shape of the fault or rail connection. The results of the experiment at a speed of 20 cm / s, the robot is able to detect the width of the rail fault of 78 mm with an error value of 1.67%, and a fault width of 5 mm with an error value of 2.48%. The robot also able to distinguish faults and connections with 72 % accuracy for the comparison of the distance between the ARMR prototype and the distance shown on google maps