Obstacle Detection by Power Transmission Line Inspection Robot

Inspection of a power transmission line (PTL) is crucial for fault detection and maintenance of the line cable but it is a job that comes with high-occupational hazard. PTL inspection robots are developed to minimize risks to humans, while doing maintenance work on PTL cables. These robots are capable of traversing the power line cables with minimal manual intervention provided they detect the obstacles present on the line and are able to overcome them. This paper discusses a technique that incorporates image texture analysis using popular gray-level co-occurrence matrices (GLCM) algorithm for obstacle detection and then, followed by standard machine learning approach to classify the obstacles. Our results indicate that this approach can be effectively used by autonomous armed robot for traversing a PTL cable.