It is expected that soon there will be a significant number of unmanned aerial vehicles (UAVs) operating side‐by‐side with manned civil aircraft in national airspace systems. To be able to integrate UAVs safely with civil traffic, a number of challenges must be overcome first. This study investigates situational awareness of UAVs’ autonomous taxiing in an aerodrome environment. The research work is based on a real outdoor experimental data collected at the Walney Island Airport, the UK. It aims to further develop and test UAVs’ autonomous taxiing in a challenging outdoor environment. To address various practical issues arising from the outdoor aerodrome such as camera vibration, taxiway feature extraction, and unknown obstacles, the authors develop an integrated approach that combines the Bayesian‐network based semantic segmentation with a self‐learning method to enhance situational awareness of UAVs. Detailed analysis of the outdoor experimental data shows that the integrated method developed in this study improves the robustness of situational awareness for autonomous taxiing.
Aerodrome situational awareness of unmanned aircraft: an integrated self‐learning approach with Bayesian network semantic segmentation
IET Intelligent Transport Systems ; 12 , 8 ; 868-874
2018-10-01
7 pages
Article (Journal)
Electronic Resource
English
image segmentation , Walney Island Airport , outdoor aerodrome environment , aerodrome situational awareness , civil traffic , UAV autonomous taxiing , Bayes methods , integrated self‐learning approach , manned civil aircraft , real outdoor experimental data collection , UK , unmanned aerial vehicles , unmanned aircraft , autonomous aerial vehicles , learning (artificial intelligence) , Bayesian network semantic segmentation , control engineering computing , aerospace computing , national airspace systems , air traffic