A comprehensive review of energy sources for unmanned aerial vehicles, their shortfalls and opportunities for improvements

publishedVersio

Conceptual Design and Analysis of Small Power Station for Supporting Unmanned Aerial Vehicle (UAV) Deployment

“Flight time” of unmanned aerial vehicle (UAV) or drone flying robot is the key component for supporting industrial activities. In practice, most battery-powered drones can fly 20 - 30 minutes for a single charging cycle. When the battery depleted, the drone is forced to come back to the station to recharge, or swap in a charged battery. However, these tasks are manually done by human multiple times. Aside from the inconvenience, human error and inappropriate force application may damage the socket compartment or loosen the locking system between battery and socket, making higher risk of the battery accidentally fall off from the socket during the flight. This research presents a “Small power station” to automatically load and unload battery from the drone’s mainframe with a constant force.  The station has two main functions: drone positioning, and six-slot-battery exchange mechanism. Product design and development (PDD) and Kano analysis method were applied to properly list necessary compartments of the designed station. Finite element analysis (FEA) and kinematic calculation were applied to virtually check whether or not the developed platform was designed in the safety boundary.  “DJI Matrice 100” drone was applied as the case study to demonstrate the proposed approach

UAVino

UAVino is a drone solution that uses aerial imagery to determine the overall plant health and water content of vineyards. In general, the system focuses on automating crop inspection by taking aerial imagery of a vineyard, conducting post-processing, and outputting an easily interpreted map of the vineyard\u27s overall health. The project\u27s key innovation is an auto-docking system that allows the drone to automatically return to its launch point and recharge in order to extend mission duration. Long term, UAVino is envisioned as a multi-year, interdisciplinary project involving both the Santa Clara University Robotics Systems Laboratory and local wineries in order to develop a fully functional drone agricultural inspection service

Autonomous environmental protection drone

During the summer, forest fires are the main reason for deforestation and the damage caused to homes and property in different communities around the world. The use of Unmanned Aerial Vehicles (UAVs, and also known as drones) applications has increased in recent years, making them an excellent solution for difficult tasks such as wildlife conservation and forest fire prevention. A forest fire detection system can be an answer to these tasks. Using a visual camera and a Convolutional Neural Network (CNN) for image processing with an UAV can result in an efficient fire detection system. However, in order to be able to have a fully autonomous system, without human intervention, for 24-hour fire observation and detection in a given geographical area, it requires a platform and automatic recharging procedures. This dissertation combines the use of technologies such as CNNs, Real Time Kinematics (RTK) and Wireless Power Transfer (WPT) with an on-board computer and software, resulting in a fully automated system to make forest surveillance more efficient and, in doing so, reallocating human resources to other locations where they are most needed.Durante o verão, os incêndios florestais constituem a principal razão do desflorestamento e dos danos causados às casas e aos bens das diferentes comunidades de todo o mundo. A utilização de veículos aéreos não tripulados (VANTs), em inglês denominados por Unmanned Aerial Vehicles (UAVs) ou Drones, aumentou nos últimos anos, tornando-os uma excelente solução para tarefas difíceis como a conservação da vida selvagem e prevenção de incêndios florestais. Um sistema de deteção de incêndio florestal pode ser uma resposta para essas tarefas. Com a utilização de uma câmara visual e uma Rede Neuronal Convolucional (RNC) para processamento de imagem com um UAV pode resultar num eficiente sistema de deteção de incêndio. No entanto, para que seja possível ter um sistema completamente autónomo, sem intervenção humana, para observação e deteção de incêndios durante 24 horas, numa dada área geográfica, requer uma plataforma e procedimentos de recarga automática. Esta dissertação reúne o uso de tecnologias como RNCs, posicionamento cinemático em tempo real (RTK) e transferência de energia sem fios (WPT) com um computador e software de bordo, resultando num sistema totalmente automatizado para tornar a vigilância florestal mais eficiente e, ao fazê-lo, realocando recursos humanos para outros locais, onde estes são mais necessários

Design of autonomous sustainable unmanned aerial vehicle - A novel approach to its dynamic wireless power transfer

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Electric UAVs are presently being used widely in civilian duties such as security, surveillance, and disaster relief. The use of Unmanned Aerial Vehicle (UAV) has increased dramatically over the past years in different areas/fields such as marines, mountains, wild environments. Nowadays, there are many electric UAVs development with fast computational speed and autonomous flying has been a reality by fusing many sensors such as camera tracking sensor, obstacle avoiding sensor, radar sensor, etc. But there is one main problem still not able to overcome which is power requirement for continuous autonomous operation. When the operation needs more power, but batteries can only give for 20 to 30 mins of flight time. These types of system are not reliable for long term civilian operation because we need to recharge or replace batteries by landing the craft every time when we want to continue the operation. The large batteries also take more loads on the UAV which is also not a reliable system. To eliminate these obstacles, there should a recharging wireless power station in ground which can transmit power to these small UAVs wirelessly for long term operation. There will be camera attached in the drone to detect and hover above the Wireless Power Transfer device which got receiving and transmitting station can be use with deep learning and sensor fusion techniques for more reliable flight operations. This thesis explores the use of dynamic wireless power to transfer energy using novel rotating WPT charging technique to the UAV with improved range, endurance, and average speed by giving extra hours in the air. The hypothesis that was created has a broad application beyond UAVs. The drone autonomous charging was mostly done by detecting a rotating WPT receiver connected to main power outlet that served as a recharging platform using deep neural vision capabilities. It was the purpose of the thesis to provide an alternative to traditional self-charging systems that relies purely on static WPT method and requires little distance between the vehicle and receiver. When the UAV camera detect the WPT receiving station, it will try to align and hover using onboard sensors for best power transfer efficiency. Since this strategy relied on traditional automatic drone landing technique, but the target is rotating all the time which needs smart approaches like deep learning and sensor fusion. The simulation environment was created and tested using robot operating system on a Linux operating system using a model of the custom-made drone. Experiments on the charging of the drone confirmed that the intelligent dynamic wireless power transfer (DWPT) method worked successfully while flying on air