Senior Capstone Project Raven: Study of an Autonomous System Design for Power Line Inspection Based on the Quanser QDrone Platform

This paper presents a senior capstone research experience in developing an autonomous system using the Quanser QDrone to perform above-ground autonomous power line inspections. The power line infrastructure is exposed to various extreme weather conditions that create an operational concern for the utility companies. Frequent inspections ensure the safe operation of a power transmission grid. There are mainly two methods of inspections, i.e. ground and air. The ground inspections are often slow and difficult due to the rough terrain, utility pole height and inaccessible remote areas. The aerial inspections are accomplished by deploying helicopters that are expensive to operate, maintain, and repair. Lightweight, portable and easy to deploy and use in even hard-to-reach locations, the Unmanned Aerial Vehicles (UAV) are being widely adopted by energy and utility companies for power line inspections due to their safety and cost-effective inspection processes. UAV inspections of utility tower structures and power lines reduce corrective maintenance costs and improve asset life, while avoiding hazardous manhours. Detailed visual imagery captured by drones allows for easier, safer, and faster identification and analysis of structural defects, hotspots, and other anomalies. The objectives of this Senior Capstone were twofold: 1) To develop an autonomous UAV system to detect and track power lines and utility poles to perform fault inspections of their electrical and material components; 2) To investigate the regulations for autonomous aerial vehicle operations. The proposed algorithm used a state machine to make decisions for searching, identifying or flying along utility poles and power lines. The proposed system was implemented using Mathworks MATLAB and Simulink with Quarc, a third party toolbox designed by Quanser, enabling real time applications with the QDrone. Project Raven was funded by the Autonomous Vehicle System (AVS) Laboratory under the supervision of Dr. Michael Frye, PI and Director of the AVS Lab at the University of the Incarnate Word located in San Antonio, Texas. This project provided the senior engineering students an invaluable opportunity to apply their existing technical knowledge, improve their time management, communication skills, and work as a team on a real-world problem.