Artificial Intelligence Architecture Based on Planar LiDAR Scan Data to Detect Energy Pylon Structures in a UAV Autonomous Detailed Inspection Process

Abstract

The technological advances in Unmanned Aerial Vehicles (UAV) related to energy power structure inspection are gaining visibility in the past decade, due to the advantages of this technique compared with traditional inspection methods. In the particular case of power pylon structure and components, autonomous UAV inspection architectures are able to increase the efficacy and security of these tasks. This kind of application presents technical challenges that must be faced to build real-world solutions, especially the precise positioning and path following for the UAV during a mission. This paper aims to evaluate a novel architecture applied to a power line pylon inspection process, based on the machine learning techniques to process and identify the signal obtained from a UAV-embedded planar Light Detection and Ranging -LiDAR sensor. A simulated environment built on the GAZEBO software presents a first evaluation of the architecture. The results show an positive detection accuracy level superior to 97% using the vertical scan data and 70% using the horizontal scan data. This accuracy level indicates that the proposed architecture is proper for the development of positioning algorithms based on the LiDAR scan data of a power pylon.

A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)

“…By enabling UAVs to perform tasks autonomously, AI has made them more efficient and effective in a variety of applications. Some of the most significant contributions and works related to using AI in UAVs include object detection [131][132][133][134][135] and tracking, path planning [33], autonomous navigation [92,136], swarm intelligence [33,122], image and video analysis [133,135], and cybersecurity [137].…”

“…One-way AI and ML algorithms can be used in UAVs is through the development of advanced sensor systems. For example, computer vision and LIDAR can be integrated into UAVs to provide real-time data to the AI system [136]. This allows the UAV to make decisions based on its environment and dynamically react to changes.…”