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An Intelligent Autopilot System that learns piloting skills from human pilots by imitation

Baomar, H; Bentley, PJ; (2016) An Intelligent Autopilot System that learns piloting skills from human pilots by imitation. In: Chen, YQ, (ed.) 2016 International Conference on Unmanned Aircraft Systems (ICUAS 2016). (pp. pp. 1023-1031). Institute of Electrical and Electronics Engineers (IEEE) Green open access

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Abstract

An Intelligent Autopilot System (IAS) that can learn piloting skills by observing and imitating expert human pilots is proposed. IAS is a potential solution to the current problem of Automatic Flight Control Systems of being unable to handle flight uncertainties, and the need to construct control models manually. A robust Learning by Imitation approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured from these demonstrations. The datasets are then used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when performing piloting tasks including handling flight uncertainties such as severe weather conditions. Experiments show that IAS performs learned take-off, climb, and slow ascent tasks with high accuracy even after being presented with limited examples, as measured by Mean Absolute Error and Mean Absolute Deviation. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to pilot an aircraft starting from the stationary position on the runway, and ending with a steady cruise.

Type: Proceedings paper
Title: An Intelligent Autopilot System that learns piloting skills from human pilots by imitation
Event: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), 7-10 June 2016, Arlington VA, USA
ISBN-13: 9781467393355
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICUAS.2016.7502578
Publisher version: http://dx.doi.org/10.1109/ICUAS.2016.7502578
Language: English
Additional information: Copyright © 2016 by the Institute of Electrical and Electronics Engineers, Inc. All Rights Reserved. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Artificial neural networks, Training, Aerospace control, Databases, Neurons, Data collection, Topology
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/1502258
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