Energy-Efficient Industrial Internet of UAVs for Power Line Inspection in Smart Grid

Zhou, Zhenyu; Zhang, Chuntian; Xu, Chen; Xiong, Fei; Zhang, Yan; Umer, Tariq

doi:10.1109/tii.2018.2794320

Cited by 151 publications

(71 citation statements)

References 32 publications

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“…A wide variety of applications has taken advantage of the increasing technological advancement of unmanned aerial vehicles (UAVs). Currently, such aircrafts are used to perform distinct actions in the most diverse areas, e.g., environmental scanning in terms of remote sensing [1] and mapping [2]; impact analysis of natural disasters and establishment of temporary emergency networks [3]; search and rescue procedures [4]; civil infrastructure for visual inspection of buildings [5], highways [6] and power line [7]; mobile crowd sensing [8]; among others.…”

Section: Introductionmentioning

confidence: 99%

Zigbee Protocol-Based Communication Network for Multi-Unmanned Aerial Vehicle Networks

et al. 2020

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This paper proposes a communication protocol for Unmanned Aerial Vehicles (UAVs) using ZigBee technology. A review of the state-of-the-art of Flying Ad-hoc Networks (FANETs) and its main respective technologies is presented in detail. A comparison among Long Term Evaluation (LTE), WiFi, and ZigBee is performed, thus showing that ZigBee stands out as a good alternative to scenarios without proper infrastructure. A Raspberry Pi 3 Model B board associated with module XBEE PRO S3B 915 MHz is embedded in a UAV model DJI Phantom 3 Standard to carry out the tests. The obtained results show that the adopted protocol is capable of sending and receiving images between the UAV and the ground station. Tests are also performed using a flying aircraft, where it is demonstrated that the transmission is successfully executed for all cases and the communication protocol operates accurately. In addition, a brief analysis of the time interval required by the process is presented, as there are no significant differences among the existing scenarios. INDEX TERMS Unmanned aerial vehicles, communication networks, protocols, zigbee, mobile ad-hoc networks.

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Section: Introductionmentioning

confidence: 99%

Zigbee Protocol-Based Communication Network for Multi-Unmanned Aerial Vehicle Networks

et al. 2020

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“…Due to such characteristics, UAVs have been really useful in a number of fields and applications, like remote sensing, real-time monitoring, disaster management, border and crowd surveillance, military applications, delivery of goods, or precision agriculture [10,11]. The use of UAVs has been also suggested for providing services in a number of industrial applications, like critical infrastructure inspections [12][13][14][15][16], sensor monitoring [17,18], automatic cargo transport [19,20], or logistics optimization of UAV swarms [21]. In the case of UAVs that operate in industrial environments, they require certain characteristics that may differ substantially from other applications [22].…”

Section: Introductionmentioning

confidence: 99%

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

et al. 2019

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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.

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“…Autonomous transmission tower inspection has always been a hot issue in the field of robotics. In the last decades, related researches are mainly conducted based on the following two popular platforms 1,2 : unmanned aerial vehicles (UAVs) [3][4][5] and rolling on wires robots (RWR). [6][7][8] In RWR inspection, the inspection is conducted by a robot climbing along transmission lines suspended by a pole tower.…”

Section: Introductionmentioning

confidence: 99%

A monocular vision–based perception approach for unmanned aerial vehicle close proximity transmission tower inspection

Bian

Hui

Zhao

et al. 2019

International Journal of Advanced Robotic Systems

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Employing unmanned aerial vehicles to conduct close proximity inspection of transmission tower is becoming increasingly common. This article aims to solve the two key problems of close proximity navigation-localizing tower and simultaneously estimating the unmanned aerial vehicle positions. To this end, we propose a novel monocular vision-based environmental perception approach and implement it in a hierarchical embedded unmanned aerial vehicle system. The proposed framework comprises tower localization and an improved point-line-based simultaneous localization and mapping framework consisting of feature matching, frame tracking, local mapping, loop closure, and nonlinear optimization. To enhance frame association, the prominent line feature of tower is heuristically extracted and matched followed by the intersections of lines are processed as the point feature. Then, the bundle adjustment optimization leverages the intersections of lines and the point-to-line distance to improve the accuracy of unmanned aerial vehicle localization. For tower localization, a transmission tower data set is created and a concise deep learning-based neural network is designed to perform real-time and accurate tower detection. Then, it is in combination with a keyframe-based semi-dense mapping to locate the tower with a clear line-shaped structure in 3-D space. Additionally, two reasonable paths are planned for the refined inspection. In experiments, the whole unmanned aerial vehicle system developed on Robot Operating System framework is evaluated along the paths both in a synthetic scene and in a real-world inspection environment. The final results show that the accuracy of unmanned aerial vehicle localization is improved, and the tower reconstruction is fast and clear. Based on our approach, the safe and autonomous unmanned aerial vehicle close proximity inspection of transmission tower can be realized.

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Energy-Efficient Industrial Internet of UAVs for Power Line Inspection in Smart Grid

Cited by 151 publications

References 32 publications

Zigbee Protocol-Based Communication Network for Multi-Unmanned Aerial Vehicle Networks

Zigbee Protocol-Based Communication Network for Multi-Unmanned Aerial Vehicle Networks

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

A monocular vision–based perception approach for unmanned aerial vehicle close proximity transmission tower inspection

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