A modified version of this work has been published in the 2022 International Conference on Computing, Electronics & Communications Engineering (iCCECE). This work is a draft author version
The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach.
University of Bristol（布里斯托大学）；Perceptual Robotics（感知机器人公司）
We present a novel method of integrating image-based measurements into a drone navigation system for the automated inspection of wind turbines. We take a model-based tracking approach, where a 3D skeleton representation of the turbine is matched to the image data. Matching is based on comparing the projection of the representation to that inferred from images using a convolutional neural network. This enables us to find image correspondences using a generic turbine model that can be applied to a wide range of turbine shapes and sizes. To estimate 3D pose of the drone, we fuse the network outpu…
We present a method for simultaneous localisation and wind turbine model fitting for a drone performing an automated surface inspection. We use a skeletal parameterisation of the turbine that can be easily integrated into a non-linear least squares optimiser, combined with a pose graph representation of the drone's 3-D trajectory, allowing us to optimise both sets of parameters simultaneously. Given images from an onboard camera, we use a CNN to infer projections of the skeletal model, enabling correspondence constraints to be established through a cost function. This is then coupled with GPS/…
University of Maryland, College Park（马里兰大学帕克分校）；Institute for Systems Research（系统研究所）
We explore an end-to-end (including access and backhaul links) UAV-assisted wireless communication system, considering both uplink and downlink traffics, with the goal of supporting demand of the Ground Users (GUs) using the minimum number of UAVs. Moreover, in order to extend the operational (flight) time of UAVs, we exploit an energy-aware routing scheme. Our intention is to design and analyze the access and backhaul connectivity of a drone-assisted communication network for remote and crowded areas and disaster relief, while minimizing the resources required i.e., the number of UAVs.
Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning
Beijing Forestry University（北京林业大学）；Engineering Research Center for Forestry-oriented Intelligent Information Processing, National Forestry and Grassland Administration（国家林业和草原局林业智能信息处理工程研究中心）
A highly maneuverable flying squirrel drone with agility-improving foldable wings
Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH)（融合IT工程系，浦项科学技术大学）；Defence AI Center, Agency for Defense Development(ADD)（国防人工智能中心，国防开发机构）
Over 600,000 bridges in the U.S. must be inspected every two years to identify flaws, defects, or potential problems that may need follow-up maintenance. Bridge inspection has adopted unmanned aerial vehicles (or drones) for improving safety, efficiency, and cost-effectiveness. Although drones can operate in an autonomous mode, keeping inspectors in the loop is critical for complex tasks in bridge inspection. Therefore, inspectors need to develop the skill and confidence to operate drones in their jobs. This paper presents the design and development of a virtual reality-based training and asse…
Unmanned aerial vehicles (UAVs) are often used for navigating dangerous terrains, however they are difficult to pilot. Due to complex input-output mapping schemes, limited perception, the complex system dynamics and the need to maintain a safe operation distance, novice pilots experience difficulties in performing safe landings in obstacle filled environments. In this work we propose a shared autonomy approach that assists novice pilots to perform safe landings on one of several elevated platforms at a proficiency equal to or greater than experienced pilots. Our approach consists of two module…
Wind energy is expected to be one of the leading ways to achieve the goals of the Paris Agreement but it in turn heavily depends on effective management of its operations and maintenance (O&M) costs. Blade failures account for one-third of all O&M costs thus making accurate detection of blade damages, especially cracks, very important for sustained operations and cost savings. Traditionally, damage inspection has been a completely manual process thus making it subjective, error-prone, and time-consuming. Hence in this work, we bring more objectivity, scalability, and repeatability in our damag…
A UAV-assisted Wireless Localization Challenge on AERPAW
School of Electrical and Computer Engineering, University of Georgia（佐治亚大学电气与计算机工程学院）；University of North Texas（北得克萨斯大学）；NYU Tandon School of Engineering（纽约大学坦登工程学院）；Delmont Systems LLC（德尔蒙特系统有限责任公司）；North Carolina State University（北卡罗来纳州立大学）
An initial study of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan generated wind conditions after being physically instantiated by a 3D printer has recently been presented. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of eval…
Roller-Quadrotor: A Novel Hybrid Terrestrial/Aerial Quadrotor with Unicycle-Driven and Rotor-Assisted Turning
Zhejiang University（浙江大学）；School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院）；Robotics Institute of Zhejiang University（浙江大学机器人研究所）；Robotics Research Center of Yuyao City（余姚市机器人研究中心）；The State Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）；Huzhou Institute of Zhejiang University（浙江大学湖州研究院）
AI-based Drone Assisted Human Rescue in Disaster Environments: Challenges and Opportunities
Russian-Armenian University（俄罗斯-亚美尼亚大学）；Rice University（莱斯大学）；California State University, Fresno（加州州立大学弗雷斯诺分校）；Institute for Informatics and Automation Problems, National Academy of Sciences（亚美尼亚国家科学院信息学与自动化问题研究所）
Audio commands are a preferred communication medium to keep inspectors in the loop of civil infrastructure inspection performed by a semi-autonomous drone. To understand job-specific commands from a group of heterogeneous and dynamic inspectors, a model must be developed cost-effectively for the group and easily adapted when the group changes. This paper is motivated to build a multi-tasking deep learning model that possesses a Share-Split-Collaborate architecture. This architecture allows the two classification tasks to share the feature extractor and then split subject-specific and keyword-s…
Coordinated Power Smoothing Control for Wind Storage Integrated System with Physics-informed Deep Reinforcement Learning
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)（深圳市人工智能与机器人研究院）；The Hong Kong Polytechnic University（香港理工大学）；Nanyang Technological University（南洋理工大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
