Since photovoltaic (PV) plants require periodic maintenance, using Unmanned Aerial Vehicles (UAV) for inspections can help reduce costs. The thermal and visual inspection of PV installations is currently based on UAV photogrammetry. A UAV equipped with a Global Positioning System (GPS) receiver is assigned a flight zone: the UAV will cover it back and forth to collect images to be later composed in an orthomosaic. The UAV typically flies at a height above the ground that is appropriate to ensure that images overlap even in the presence of GPS positioning errors. However, this approach has two limitations. Firstly, it requires to cover the whole flight zone, including "empty" areas between PV module rows. Secondly, flying high above the ground limits the resolution of the images to be later inspected. The article proposes a novel approach using an autonomous UAV equipped with an RGB and a thermal camera for PV module tracking. The UAV moves along PV module rows at a lower height than usual and inspects them back and forth in a boustrophedon way by ignoring "empty" areas with no PV modules. Experimental tests performed in simulation and an actual PV plant are reported.
Visual Servoing NMPC Applied to UAVs for Photovoltaic Array Inspection
University of Alicante（阿利坎特大学）；Instituto de Automática (INAUT)（自动化研究所）；Universidad Nacional de San Juan（圣胡安国立大学）；Centro de Investigaciones de Ciencias Humanas y de la Educación (CICHE)（人文与教育科学研究中心）；Universidad Tecnológica Indoamérica（印多大理工大学）
For the purpose of inspecting power plants, autonomous robots can be built using reinforcement learning techniques. The method replicates the environment and employs a simple reinforcement learning (RL) algorithm. This strategy might be applied in several sectors, including the electricity generation sector. A pre-trained model with perception, planning, and action is suggested by the research. To address optimization problems, such as the Unmanned Aerial Vehicle (UAV) navigation problem, Deep Q-network (DQN), a reinforcement learning-based framework that Deepmind launched in 2015, incorporate…
Method of UAV Inspection of Photovoltaic Modules Using Thermal and RGB Data Fusion
Khmelnytskyi National University（赫梅利尼茨基国立大学）；Department of Informatics and Teleinformatics（信息学与电信学系）；Kazimierz Pulaski University of Ra-dom（卡齐米日·波尼亚图夫斯基大学）；National Academy of the State Border Service of Ukraine named after Bogdan Khmelnitsky（以博丹·赫梅利尼茨基命名的乌克兰国家边境服务学院）
Trường Đại học Công nghệ, Đại học Quốc gia Hà Nội（河内国家大学所属技术大学）；Trường Đại học Kinh tế - Kỹ thuật Công nghiệp（工业经济与技术大学）
Agricultural production using high technology is an inevitable trend in Vietnam. Especially for material crops which typically need large growing areas, wireless sensor networks has been clearly playing a significant role in increasing productivity, monitoring pests and diseases, mitigating the impact of climate change, and reducing the direct labor of cultivators. This paper constructs an experimental model of agricultural crop field monitoring using a combination of LoRa wireless sensor networks and unmanned aerial vehicles to collect data on conditions of weather and soil, plant health, whi…
State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co.,Ltd（国家核电安全监控技术与装备重点实验室，中国核电工程有限公司）；School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；South-tech Information Technology Co.,LTD（南方科技信息技术有限公司）
Visible light positioning (VLP) is widely believed to be a cost-effective answer to the growing demanded for robot indoor positioning. Considering that some extreme environments require robot to be equipped with a precise and radiation-resistance indoor positioning system for doing difficult work, a novel VLP system with high accuracy is proposed to realize the long-playing inspection and intervention under radiation environment. The proposed system with sufficient radiation-tolerance is critical for operational inspection, maintenance and intervention tasks in nuclear facilities. Firstly, we …
Evaluation of a Smart Mobile Robotic System for Industrial Plant Inspection and Supervision
Fraunhofer Institute for Highspeed Dynamics, Ernst-Mach-Institute (EMI)（弗劳恩霍夫高速动力学研究所恩斯特·马赫研究所）；Endress+Hauser Digital Solutions GmbH（恩德斯豪斯数字解决方案有限公司）；University of Freiburg（弗赖堡大学）；dotscene GmbH（dotscene有限公司）；Evonik Operations GmbH（赢创运营有限公司）；Telocate GmbH（Telocate有限公司）；University of Technology Nuremberg（纽伦堡工业大学）
AutoInspect: Towards Long-Term Autonomous Industrial Inspection
Oxford Robotics Institute, University of Oxford（牛津大学机器人研究所）；Remote Applications in Challenging Environments (RACE), United Kingdom Atomic Energy Authority（英国原子能局远程应用在恶劣环境中（RACE））
University of Twente（特文特大学）；Jönköping University（延雪平大学）
Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, the autonomous control of such robots is still a big open challenge due to the high number of actuators and the complex manoeuvres required. To address this problem, we investigate the usage of Deep Reinforcement Learning for achieving autonomous navigation of in-pipe robots in pipeline networks with complex topologies. Moreover, we introduce a hierarchical policy decomposition based on Hierarchical Reinforcement Lear…
Reducing the gap between general purpose data and aerial images in concentrated solar power plants
organization= Department of Applied Mathematics II, University of Seville , city= Seville , country= Spain；organization= Multi-robot \& Control Systems group, University of Seville , city= Seville , country= Spain
