A multi-robot-based fault detection system for railway tracks is proposed to eliminate manual human visual inspection. A hardware prototype is designed to implement a master–slave robot mechanism capable of detecting rail surface defects, which include cracks, squats, corrugations, and rust. The system incorporates ultrasonic sensor inputs coupled with image processing using OpenCV and deep learning algorithms to classify the surface faults detected. The proposed Convolutional Neural Network (CNN) model fared better compared to the Artificial Neural Network (ANN), random forest, and Support Vector Machine (SVM) algorithms based on accuracy, R-squared value, F1 score, and Mean-Squared Error (MSE). To eliminate manual inspection, the location and status of the fault can be conveyed to a central location enabling immediate attention by utilizing GSM, GPS, and cloud storage-based technologies. The system is extended to a multi-robot framework designed to optimize energy utilization, increase the lifetime of individual robots, and improve the overall network throughput. Thus, the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is simulated using 100 robot nodes, and the corresponding performance metrics are obtained.

提出了一种基于多机器人的铁路轨道故障检测系统，以消除人工视觉检查。设计了一个硬件原型，以实现能够检测轨道表面缺陷（包括裂缝，下蹲，起皱和生锈）的主从机器人机制。该系统结合了超声传感器输入以及使用OpenCV和深度学习算法进行的图像处理，以对检测到的表面缺陷进行分类。与人工神经网络（ANN），随机森林和支持向量机（SVM）基于精度，R平方值，F1得分和均方误差（SVM）的算法相比，提出的卷积神经网络（CNN）模型效果更好（ MSE）。为了消除人工检查，可以将故障的位置和状态传送到中央位置，从而可以通过使用GSM立即引起注意，GPS和基于云存储的技术。该系统扩展到多机器人框架，旨在优化能源利用率，延长单个机器人的使用寿命并提高整体网络吞吐量。因此，使用100个机器人节点模拟了低能耗自适应聚类层次结构（LEACH）协议，并获得了相应的性能指标。