31 May 2018
Bayes methods, autonomous aerial vehicles, learning (artificial intelligence), air traffic, aerospace computing, control engineering computing, image segmentation, aerodrome situational awareness, unmanned aircraft, integrated self-learning approach, Bayesian network semantic segmentation, unmanned aerial vehicles, manned civil aircraft, national airspace systems, civil traffic, UAV autonomous taxiing, real outdoor experimental data collection, Walney Island Airport, UK, outdoor aerodrome environment
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.