How using aerial drones to deliver projects during Covid lockdown led to a 'new normal'

The paper illustrates how unmanned aerial vehicle surveys were used to support the designs for refurbishment of seven rural bridges in Wales during a Covid-19 lockdown in 2020. The hundreds of high-resolution photos captured for each structure were used to produce photo-realistic three-dimensional photogrammetry models using automated processes. Although born of necessity, the reduction in cost, duration and disruption together with the elimination of risks associated with conventional surveys show the case for wider adoption beyond the Covid-19 pandemic.

SYSTEM FOR PROVIDING AUTOMATIC NAVIGATION OF AN UNMANNED AERIAL VEHICLE IN THE VICINITY OF AN AIRFIELD

Рассмотрены вопросы разработки системы, способной обеспечивать автоматическую навигацию беспилотного летательного аппарата в окрестности аэродрома без использования дополнительных датчиков. Рассмотрен алгоритм решения этой задачи с использованием бортовой монокулярной системы технического зрения, функционирующей в диапазоне 1,55 мкм. Для обеспечения навигации беспилотный летательный аппарат оснащен системой информационного обмена, а в районе точки взлета-посадки в качестве наземных источников (маяков) предложено использовать полупроводниковые лазеры с некогерентным излучением длиной волны 1,55 мкм, которые обеспечивают работу системы в простых метеоусловиях. Путем измерений угла азимута в двух точках траектории движения беспилотного летательного аппарата вычисляются его координаты местоположения относительно взлетно-посадочной полосы, а также угол курса необходимый для выхода в начальную точку глиссады снижения. Ввиду того, что погрешности измерений обусловлены ошибками измерений угла азимута, курса и скорости полета, ошибками измерения временных интервалов в данной работе пренебрегаем. Полученные графики показывают, что погрешности измерения координат беспилотного летательного аппарата минимальны при пролете напротив маяка и резко возрастают при удалении от него, что обусловлено погрешностью измерения азимута и дальности. При этом измерение местоположения беспилотного летательного аппарата необходимо выполнять на минимальном удалении от маяка The article discusses the development of a system capable of providing automatic navigation of an unmanned aerial vehicle in the vicinity of an airfield without the use of additional sensors. We considered an algorithm for solving this problem using an onboard monocular vision system operating in the range of 1.55 microns. To ensure navigation, the unmanned aerial vehicle is equipped with an information exchange system, and in the area of the take-off and landing point, we propose to use semiconductor lasers with incoherent radiation with a wavelength of 1.55 microns, which ensure the operation of the system in simple weather conditions, as ground sources (beacons). By measuring the azimuth angle at two points of the trajectory of the unmanned aerial vehicle, we calculated its location coordinates relative to the runway, as well as the course angle necessary to reach the starting point of the descent glide path. Since measurement errors are caused by errors in measuring the azimuth angle, course and flight speed, we neglected errors in measuring time intervals in this work. The obtained graphs show that the errors in measuring the coordinates of an unmanned aerial vehicle are minimal when flying in front of the lighthouse and increase sharply when moving away from it, which is due to the error in measuring azimuth and range. At the same time, the measurement of the location of the unmanned aerial vehicle must be carried out at a minimum distance from the lighthouse

Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks

Federated learning (FL) allows UAVs to collaboratively train a globally shared machine learning model while locally preserving their private data. Recently, the FL in edge-aided unmanned aerial vehicle (UAV) networks has drawn an upsurge of research interest due to a bursting increase in heterogeneous data acquired by UAVs and the need to build the global model with privacy; however, a critical issue is how to deal with the non-independent and identically distributed (non-i.i.d.) nature of heterogeneous data while ensuring the convergence of learning. To effectively address this challenging issue, this paper proposes a novel and high-performing FL scheme, namely, the hierarchical FL algorithm, for the edge-aided UAV network, which exploits the edge servers located in base stations as intermediate aggregators with employing commonly shared data. Experiment results demonstrate that the proposed hierarchical FL algorithm outperforms several baseline FL algorithms and exhibits better convergence behavior.

Mangrove Above-Ground Biomass and Carbon Stock in the Karimunjawa-Kemujan Islands Estimated from Unmanned Aerial Vehicle-Imagery

Blue carbon ecosystems in the Karimunjawa Islands may play a vital role in absorbing and storing the releasing carbon from the Java Sea. The present study investigated mangrove above-ground biomass (AGB) and carbon stock in the Karimunjawa-Kemujan Islands, the largest mangrove area in the Karimunjawa Islands. Taking the aerial photos from an Unmanned Aerial Vehicle combined with Global Navigation Satellite System (GNSS) measurements, we generated Digital Surface Model (DSM) and Digital Terrain Model (DTM) with high accuracy. We calculated mangrove canopy height by subtracting DSM from DTM and then converted it into Lorey’s height. The highest mangrove canopy is located along the coastline facing the sea, ranging from 8 m to 15 m. Stunted mangroves 1 m to 8 m in height are detected mainly in the inner areas. AGBs were calculated using an allometric equation destined for the Southeast and East Asia region. Above-ground carbon biomass is half of AGB. The AGB and carbon biomass of mangroves in the Karimunjawa-Kemujan Islands range from 8 Mg/ha to 328 Mg/ha, and from 4 MgC/ha to 164 MgC/ha, respectively. With a total area of 238.98 ha, the potential above-ground carbon stored in the study area is estimated as 16,555.46 Mg.

HoloGCS : Mixed Reality-based Ground Control Station for Unmanned Aerial Vehicle

Ground control station (GCS) is a system for controlling and monitoring unmanned aerial vehicle (UAV). In current GCS, the device used are considered as complex environment. This paper proposes a video streaming and speech command control for supporting mixed reality based UAV GCS using Microsoft HoloLens. Video streaming will inform the UAV view and transmit the raw video to the HoloLens, while the HoloLens steers the UAV based on the displayed UAV field of view (FoV). Using the HoloLens Mixed Reality Tool-Kit (MRTK) speech input, UAV speech control from the HoloLens was successfully implemented. Finally, experimental results based on video streaming and speech command calculation of the throughput, round-time trip, latency and speech accuracy tests are discussed to demonstrate the feasibility of the proposed scheme.