Bio-inspired robotics for air traffic weather information management

• Published in: Transactions of the Institute of Measurement and Control Vol. 34; no. 2-3; pp. 291 - 317
• Main Authors: Bui, Vinh, Pham, Viet V., Iorio, Antony W., Tang, Jiangjun, Alam, Sameer, Abbass, Hussein A.
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.04.2012; Sage Publications Ltd
• Subjects: Air traffic; Air traffic control; Airspace; Automation; Autonomous; Climatology; Icing; Image processing systems; Information management; Robotics; Robots; Weather; image processing; path planning; weather information management; air traffic management; bio-inspired robotics
• ISSN: 0142-3312; 1477-0369
• DOI: 10.1177/0142331210366688

With the increase in automation to serve the growing needs and challenges of aviation, air traffic controllers (ATCs) are now faced with an information overload from a myriad of sources, both in graphical and textual format. One such source is weather information, which is typically comprised of wind speed, wind direction, thunderstorms, cloud cover, icing, temperature and pressure at various altitudes. This information requires domain expertise to interpret and communicate to ATCs, who then employ this information to manage air traffic efficiently and safely. Unfortunately, ATCs are not trained meteorologists, so there are significant challenges associated with the correct interpretation and utilization of this information by ATCs. In this paper, we propose a bio-inspired weather robot, which interacts with the air traffic environment and provides targeted weather-related information to ATCs by identifying which airspace sectors they are working on. It uses bio-inspired techniques for processing weather information and path planning in the air traffic environment and is fully autonomous in the sense that it only interacts with the air traffic environment passively and has an onboard weather information processing system. The weather robot system was evaluated in an experimental environment with live Australian air traffic, where it successfully navigated the environment, processed weather information, identified airspace sectors and delivered weather-related information for the relevant sector using a synthetic voice.