Abstract
The solution for insuring the safety of tele-operated or fully unmanned autonomous systems (UASs) in the air space requires a) that the human remain in and on the loop to the maximal extent practical and b) that the UASs, which share the air space, have an intelligent backend for the processing of their sensory data. Moreover, it is necessary that this sensory processor be capable of generalizing and learning more than it was told in order that it properly handle situations not explicitly programmed for. Given the advent of advances in nanotechnology and microsystems, several research teams continue to investigate the integration of such technologies for single UASs and small swarms of UASs for military, commercial, and civilian applications. Our proposed technology can be readily adapted for transparent learning to serve as an assistant for human piloting as well as an emergency intelligent autopilot for all manner of piloted vehicles.
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Rubin, S.H., Lee, G. (2011). Human-Machine Learning for Intelligent Aircraft Systems. In: Kamel, M., Karray, F., Gueaieb, W., Khamis, A. (eds) Autonomous and Intelligent Systems. AIS 2011. Lecture Notes in Computer Science(), vol 6752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21538-4_33
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DOI: https://doi.org/10.1007/978-3-642-21538-4_33
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