This article aims at flying target detection and localization of a fixed-wing unmanned aerial vehicle (UAV) autonomous take-off and landing within Global Navigation Satellite System (GNSS)-denied environments. A Chan-Vese model–based approach is proposed and developed for ground stereo vision detection. Extended Kalman Filter (EKF) is fused into state estimation to reduce the localization inaccuracy caused by measurement errors of object detection and Pan-Tilt unit (PTU) attitudes. Furthermore, the region-of-interest (ROI) setting up is conducted to improve the real-time capability. The present work contributes to real-time, accurate and robust features, compared with our previous works. Both offline and online experimental results validate the effectiveness and better performances of the proposed method against the traditional triangulation-based localization algorithm.

Collective navigation is inspired by natural migratory species traveling long distances and conserving their group behavior in the whole journey. With the technological development of autonomous intelligent systems, collective navigation has been an important application in the field of intelligent vehicles. However, many of the existing collective behavior approaches rely on communication exchanges between individuals and focus on the emergence of collective motion, which limits the application in specific mission scenarios and lacks robustness against interference. To address these challenges, this paper proposes a novel flocking-inspired approach of collective navigation for aerial vehicle swarms flying synchronously to the desired region. Firstly, flocking without communication is achieved based on bionic visual projection field (VPF), ensuring the normal operation of the swarm in communication-denied unknown environments. Secondly, each aerial entity is assigned a hidden identity as informed or uninformed, but only the informed agents are allocated information about the migration destination. Under such an architecture, the trade-off between informed and uninformed individuals in the group enables few-to-many control and fast navigation. Finally, simulation and experiments are both conducted within multiple scenarios, in terms of adaptability to different environments, swarm scalability, and robustness to vehicle failures. Results validate the effectiveness of the developed bio-inspired collective navigation algorithm.

[-5]One of the greatest challenges for fixed-wing unmanned aircraft vehicles (UAVs) is safe landing. Hereafter, an on-ground deployed visual approach is developed in this paper. This approach is definitely suitable for landing within the global navigation satellite system (GNSS)-denied environments. As for applications, the deployed guidance system makes full use of the ground computing resource and feedbacks the aircraft’s real-time localization to its on-board autopilot. Under such circumstances, a separate long baseline stereo architecture is proposed to possess an extendable baseline and wide-angle field of view (FOV) against the traditional fixed baseline schemes. Furthermore, accuracy evaluation of the new type of architecture is conducted by theoretical modeling and computational analysis. Dataset-driven experimental results demonstrate the feasibility and effectiveness of the developed approach.