Altitude Measurement-Based Optimization of the Landing Process of UAVs

A Decade of UAV Docking Stations: A Brief Overview of Mobile and Fixed Landing Platforms

Unmanned Aerial Vehicles have advanced rapidly in the last two decades with the advances in microelectromechanical systems (MEMS) technology. It is crucial, however, to design better power supply technologies. In the last decade, lithium polymer and lithium-ion batteries have mainly been used to power multirotor UAVs. Even though batteries have been improved and are constantly being improved, they provide fairly low energy density, which limits multirotors’ UAV flight endurance. This problem is addressed and is being partially solved by using docking stations which provide an aircraft to land safely, charge (or change) the batteries and to take-off as well as being safely stored. This paper focuses on the work carried out in the last decade. Different docking stations are presented with a focus on their movement abilities. Rapid advances in computer vision systems gave birth to precise landing systems. These algorithms are the main reason that docking stations became a viable solution. The authors concluded that the docking station solution to short ranges is a viable option, and numerous extensive studies have been carried out that offer different solutions, but only some types, mainly fixed stations with storage systems, have been implemented and are being used today. This can be seen from the commercially available list of docking stations at the end of this paper. Nevertheless, it is important to be aware of the technologies being developed and implemented, which can offer solutions to a vast number of different problems.

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Landing an unmanned aerial vehicle (UAV) autonomously and safely is a challenging task. Although the existing approaches have resolved the problem of precise landing by identifying a specific landing marker using the UAV’s onboard vision system, the vast majority of these works are conducted in either daytime or well-illuminated laboratory environments. In contrast, very few researchers have investigated the possibility of landing in low-illumination conditions by employing various active light sources to lighten the markers. In this paper, a novel vision system design is proposed to tackle UAV landing in outdoor extreme low-illumination environments without the need to apply an active light source to the marker. We use a model-based enhancement scheme to improve the quality and brightness of the onboard captured images, then present a hierarchical-based method consisting of a decision tree with an associated light-weight convolutional neural network (CNN) for coarse-to-fine landing marker localization, where the key information of the marker is extracted and reserved for post-processing, such as pose estimation and landing control. Extensive evaluations have been conducted to demonstrate the robustness, accuracy, and real-time performance of the proposed vision system. Field experiments across a variety of outdoor nighttime scenarios with an average luminance of 5 lx at the marker locations have proven the feasibility and practicability of the system.

AL-TUNE: A Family of Methods to Effectively Tune UAV Controllers in In-flight Conditions

In the paper, a family of novel real-time tuning methods for an unmanned aerial vehicle (UAV) altitude controller in in-flight conditions. The methods allow the controller’s gains to be adapted only on the basis of measurements from a basic sensory equipment and by constructing the optimization cost function in an on-line fashion with virtually no impeding computational complexity; in the case of the altitude controller as in this paper for a hexacopter, altitude measurements were used only. The methods are not dependent on the measurement level, and present the approach in a generally applicable form to tuning arbitrary controllers with low number of parameters. Real-world experimental flights, preceded by simulation tests, have shown which method should behave best in a noisy environment when e.g. wind disturbances act on a UAV while it is in autonomous flight. As the methods can potentially be extended to other control loops or controller types, making this a versatile, rapid-tuning tool. It has been shown that a well-tuned controller using the proposed AL-TUNE scheme outperforms controllers that are tuned just to stabilize the system. AL-TUNE provides a new way of using UAVs in terms of adaptivity to changing their dynamic properties and can be deployed rapidly. This enables new applications and extends the usability of fully autonomous UAVs, unlike other tuning methods, which basically require the availability of a UAV model. The core difference with respect to other research from the field is that other authors either use a model of a UAV to optimize the gains analytically or use machine learning techniques, what increases time consumption, whereas the presented methods offer a rapid way to tune controllers, in a reliable way, with deterministic time requirements.