Unmanned aerial vehicle | EPFL Graph Search

An unmanned aerial vehicle (UAV), commonly known as a drone, is an aircraft without any human pilot, crew, or passengers on board. UAVs were originally developed through the twentieth century for military missions too "dull, dirty or dangerous" for humans, and by the twenty-first, they had become essential assets to most militaries. As control technologies improved and costs fell, their use expanded to many non-military applications. These include aerial photography, precision agriculture, forest fire monitoring, river monitoring, environmental monitoring, policing and surveillance, infrastructure inspections, smuggling, product deliveries, entertainment, and drone racing. Terminology Many terms are used for aircraft which fly without any persons on board. The term drone has been used from the early days of aviation, being applied to remotely flown target aircraft used for practice firing of a battleship's guns, such as the 1920s Fairey Queen and 1930s de Havilla

The Application of Fuel-Cell and Battery Technologies in Unmanned Aerial Vehicles (UAVs): A Dynamic Study

Claire Marie Isabelle Bernier, Hossein Pourrahmani, Jan Van Herle

The harmful impacts of fossil-fuel-based engines on the environment have resulted in the development of other alternatives for different types of vehicles. Currently, batteries and fuel cells are being used in the automotive industry, while promising progress in the maritime and aerospace sectors is foreseen. As a case study in the aerospace sector, an unmanned aerial vehicle (UAV) was considered. The goal and the novelty of this study are in its analysis of the possibility of providing 960 W of power for a UAV with a weight of 14 kg using a hybrid system of a lithium-ion (Li-ion) battery and proton-exchange membrane fuel cell (PEMFC). The dynamic performance of the system was analyzed considering three different load profiles over time in an optimized condition. PEMFC was the main supplier of power, while the battery intervened when the power load was high for the PEMFC and the system demanded an immediate response to the changes in power load. Additionally, the impacts of the operating temperature and the C-rate of the battery were characterized by the state of the charge of the battery to better indicate the overall performance of the system.

MDPI2022

A survey on tactical control algorithms for path tracking unmanned surface vehicles

Murat Kumru

In this study, a survey on control allocation algorithms in tactical level control for path tracking unmanned surface vehicles (USV) is conducted. The strategic goal in the path tracking problem is to assist navigation solution of an unmanned underwater vehicle (UUV). USV with the help of its onboard acoustic sensors, tracks UUV according to constant bearing guidance rule. The survey on tactical controllers comprises comparisons of tracking performances of USVs under proportional-integral-derivative, pole placement, feedback linearization and sliding mode controllers according to the strategic goal. The parameters of the controllers are tuned with a common elitist genetic algorithm optimization infrastructure. The disturbance rejection capabilities of the controllers are discussed through Monte Carlo simulations of USVs within various wave disturbances as well.

IEEE2017

Vision-Based Sense and Avoid Algorithms for Unmanned Aerial Vehicles

Steven Adriaan Roelofsen

The field of Unmanned Aerial Vehicles (UAVs), also known as drones, is rapidly growing, both in terms of size and of number of applications. Civil applications range from mapping, inspection, search and rescue, taking aerial footage, to art show, entertainment and more. Currently, most applications have a human pilot supervising or controlling the vehicles, but UAVs are expected to gain more autonomy with time. To fly in general airspace, used by both general and commercial aviation, a high level of autonomy is required from UAVs. A core functionality required to fly in general airspace is the UAVs' ability to detect and avoid collisions with other aircraft or objects. This functionality is handled by a so called Sense And Avoid (SAA) system. From among several sensors investigated to be used for a SAA system, a vision-based sensor is seen as a good candidate for a SAA system due to its ability to detect and identify a large variety of objects, as well as being close to the human's main mean to detect aircraft and other objects. To be as general as possible, this work focuses on non-cooperative algorithms that do not take assumptions on the motion of other aircraft. This thesis presents algorithms for a vision-based SAA system. It focuses on the relationship between sensing and avoidance, and how the limitations of one constrain the second. In particular, this thesis studies the consequences of the limited Field Of View (FOV) of a camera sensor on the collision avoidance algorithms. Given the assumptions above, the sensing and tracking of other UAVs is performed using cameras with fish-eye lenses that have a large enough FOV for the collision avoidance algorithms to guarantee to be collision-free. The detection of other UAVs is performed using two methods: a marker-based or a marker-less computer vision algorithms. Using the measurements from the computer vision algorithm, the positions and velocities of neighboring UAVs are tracked using a Gaussian mixture probability hypothesis density filter. This tracking algorithm is able to track multiple UAVs while requiring little computational resources, therefore representing a suitable candidate for on-board deployment. In this work, it is mathematically proven that the motion of an UAV has to be constrained according to the FOV of its sensor. Following that result, several collision avoidance algorithms are adapted to ensure collision-free navigation when used with a sensor with a limited FOV. Sensory limitations such as noise, lag, limited range and FOV, and their effects on the performance of collision avoidance algorithms are studied. Experimental work using high-fidelity simulation and real robots shows that algorithms that only use position information from the sensors are overall more reliable, although less efficient (in terms of distance traveled or trajectory smoothness) than algorithms that also use velocity estimates from the sensing system.

EPFL2017

Vision-Based Sense and Avoid Algorithms for Unmanned Aerial Vehicles

Steven Adriaan Roelofsen

EPFL2017