Autonomy Challenges in Unmanned Aviation

Dear Colleagues,

Unmanned Aviation has seen unprecedented growth during the past two decades. Applications span military, civilian, and public domain areas. The underlying technology, along with the exponential advances in AI, learning-based frameworks, and computational power, has made it possible to consider autonomy attributes and autonomous functionality of complex systems in general, and UAVs/UAS in particular. There is a plethora of autonomy-related challenges that need to be considered and overcome before decision-making delegation gradually shifts from the human operator to the machine itself! When looking at published literature, there is an essential need to define specific attributes of autonomy and autonomy metrics that will contribute to both enhancing UAV/UAS autonomous functionality under uncertainties pertaining to model variations and also environment, as well as adaptability to completely unknown situations, which tackle the resiliency component of the UAV/UAS – siad differently, it refers to UAV/UAS design for resilience.

The Special Issue centers around “Autonomy Challenges in Unmanned Aviation” with the aim to register the state-of-the-art and to discuss and/or propose specific autonomy attributes, measures of autonomy, autonomy metrics, levels of autonomy, etc., for unmanned aviation systems, and how they may be implemented and tested to evaluate performance under nominal and detrimental conditions. The goal is to establish the foundations of an autonomous framework that is verified and validated, and is applicable in real-time. As such, submitted papers should focus on both the underlying theoretical methodology for measuring and evaluating autonomy, as well as on its implementation using a real UAV/UAS.

Articles to be submitted should focus on all aspects of unmanned aviation systems, autonomous functionality coupled with performance evaluation. However, of specific interest are articles that emphasize:

1. Foundations of Autonomy: Attributes, quantitative metrics and measures of autonomy, levels of autonomy;
2. UAV/UAS design for resilience;
3. Self-organizing and reconfigurable UAV/UAS controller designs for autonomous functionality;
4. Decision-making;
5. Real-time applicable and implementable learning based navigation and control techniques for UAV/UAS autonomous functionality;
6. Controller design for nonlinear systems with time-varying and unstructured uncertainties.

Prof. Dr. Kimon P. Valavanis
Prof. Dr. Anthony Tzes
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• unmanned aviation
• autonomy
• learning-based navigation and control
• UAS design for robustness and resilience
• NextGen UAS