With the increase in automation to serve the growing needs and challenges of aviation, air traffic controllers (ATC) are now faced with 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 is evaluated in a live air traffic environment of Australian airspace 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.

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