Bae H, et al. Korean J Physiol Pharmacol. 2016 May;20(3):315-324. http://dx.doi.org/10.4196/kjpp.2016.20.3.315

Autonomous unmanned aerial vehicle (UAV) landing can be useful in multiple applications. Precise landing is a difficult task because of the significant navigation errors of the global positioning system (GPS). To overcome these errors and to realize precise landing control, various sensors have been installed on UAVs. However, this approach can be challenging for micro UAVs (MAVs) because strong thrust forces are required to carry multiple sensors. In this study, a new autonomous MAV landing system is proposed, in which a landing platform actively assists vehicle landing. In addition to the vision system of the UAV, a camera was installed on the platform to precisely control the MAV near the landing area. The platform was also designed with various types of equipment to assist the MAV in searching, approaching, alignment, and landing. Furthermore, a novel algorithm was developed for robust spherical object detection under different illumination conditions. To validate the proposed landing system and detection algorithm, 80 flight experiments were conducted using a DJI TELLO drone, which successfully landed on the platform in every trial with a small landing position average error of 2.7 cm.

As drones become more prevalent, their malicious use threatens public safety. To reduce this threat, an unmanned aerial vehicle (UAV) that can chase unknown drones is required. However, visual detection for the chasing flight is challenging because the target objects can be small and fly at high speed. Additionally, if the background in the UAV vision is similar to the color of the flying object, detection becomes more difficult. Thus, this paper presents a new UAV system to effectively detect and chase randomly flying objects. First, the detecting performance of YOLOv8 was improved by introducing a search region (SR) technique, which can reduce the probability of detection failure and increase the inference speed. Second, the UAV is controlled to visually capture its target from a different field of view to improve target visibility. If the target object is less noticeable within the image background, the UAV moves to change its position with respect to the target considering the covariance value of the target and the background pixels. The performance of the proposed control system was validated with outdoor flight experiments, where the UAV successfully chased an object that was flying randomly at a speed of 5 m/s with an average distance error of 0.66 m.

Tracking a micro aerial vehicle (MAV) is challenging because of its small size and swift motion. A new model was developed by combining compact and adaptive search region (SR). The model can accurately and robustly track MAVs with a fast computation speed. A compact SR, which is slightly larger than a target MAV, is less likely to include a distracting background than a large SR; thus, it can accurately track the MAV. Moreover, the compact SR reduces the computation time because tracking can be conducted with a relatively shallow network. An optimal SR to MAV size ratio was obtained in this study. However, this optimal compact SR causes frequent tracking failures in the presence of the dynamic MAV motion. An adaptive SR is proposed to address this problem; it adaptively changes the location and size of the SR based on the size, location, and velocity of the MAV in the SR. The compact SR without adaptive strategy tracks the MAV with an accuracy of 0.613 and a robustness of 0.086, whereas the compact and adaptive SR has an accuracy of 0.811 and a robustness of 1.0. Moreover, online tracking is accomplished within approximately 400 frames per second, which is significantly faster than the real-time speed.