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, incorporates both deep learning and Q-learning. To overcome problems with current procedures, the research proposes a power plant inspection system incorporating UAV autonomous navigation and DQN reinforcement learning. These training processes set reward functions with reference to states and consider both internal and external effect factors, which distinguishes them from other reinforcement learning training techniques now in use. The key components of the reinforcement learning segment of the technique, for instance, introduce states such as the simulation of a wind field, the battery charge level of an unmanned aerial vehicle, the height the UAV reached, etc. The trained model makes it more likely that the inspection strategy will be applied in practice by enabling the UAV to move around on its own in difficult environments. The average score of the model converges to 9,000. The trained model allowed the UAV to make the fewest number of rotations necessary to go to the target point.
Dense 3D Reconstruction for Visual Tunnel Inspection using Unmanned Aerial Vehicle
Institute for Infocomm Research (IR), A*STAR（新加坡科技研究局资讯通信研究院）；Nanyang Technological University (NTU)（南洋理工大学）；University of Illinois at Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）；Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）
Spiking Neural Network Actor-Critic Proximal Policy Optimization Control for Autonomous UAV Navigation Through Constrained Openings in Civil Infrastructure and Buildings
Alma Mater Europaea University（欧洲阿尔马马特大学）；AGH University of Krakow（克拉科夫AGH科技大学）
Indoor Positioning System based on Visible Light Communication for Mobile Robot in Nuclear Power Plant
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）
Geometric Look-Angle Shaping Strategy for Enclosed Inspection
SYSTEC – Research Centre for Systems and Technologies（SYSTEC系统与技术研究中心）；ARISE – Advanced Production and Intelligent Systems（ARISE先进生产与智能系统）；Faculdade de Engenharia, Universidade do Porto（工程学院，葡萄牙波尔图大学）
Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges
New Jersey Institute of Technology（新泽西理工学院）；Universiti Tenaga Nasional（国家能源大学）；Western Michigan University（西密歇根大学）；Tafila Technical University（塔菲拉技术大学）；University of Idaho（爱达荷大学）
UAV-based Visual Remote Sensing for Automated Building Inspection
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）；University of Petroleum and Energy Studies（石油与能源研究大学）
A Hybrid Cable-Driven Robot for Non-Destructive Leafy Plant Monitoring and Mass Estimation using Structure from Motion
Institute for Robotics and Intelligent Machines, Georgia Tech（佐治亚理工学院机器人与智能机器研究所）；CNRS IRL 2958, Georgia Tech Lorraine（法国国家科学研究中心2958国际联合实验室，佐治亚理工洛林分校）；School of Civil and Environmental Engineering, Georgia Tech（佐治亚理工学院土木与环境工程学院）
LiDAR-based Quadrotor for Slope Inspection in Dense Vegetation
University of Hong Kong（香港大学）；Geotechnical Engineering Office（土力工程处）；Civil and Engineering Development Department（土木工程拓展署）；The Government of Hong Kong SAR（香港特别行政区政府）
GATSBI: An Online GTSP-Based Algorithm for Targeted Surface Bridge Inspection and Defect Detection
National Institute of Standards and Technology（美国国家标准与技术研究院）；Rice University（莱斯大学）；Colorado State University（科罗拉多州立大学）；University of Colorado（科罗拉多大学）；National Research Institute for Metals（国立金属材料研究所）
Autonomous UAV Pipeline Near-proximity Inspection via Disturbance-Aware Predictive Visual Servoing
School of Automation, Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University（自动化学院、CSE测量与控制重点实验室、教育部、东南大学）；Department of Aeronautical and Automotive Engineering, Loughborough University（航空与汽车工程系、罗汉布大学）；Research Centre for Low Altitude Economy and the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University（低空经济研究中心和航空与航空工程系、香港理工大学）
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）
BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions
Center for Robotics, University of Bonn（柏林洪堡大学机器人中心）；Institute of Sugar Beet Research（甘蔗研究院）；Lamarr Institute for Machine Learning and Artificial Intelligence（Lamarr人工智能与机器学习研究所）
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）
