Rodríguez-Fernández, V;
Menéndez, HD;
Camacho, D;
(2016)
Analysing temporal performance profiles of UAV operators using time series clustering.
Expert Systems with Applications
, 70
(C)
pp. 103-118.
10.1016/j.eswa.2016.10.044.
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Abstract
The continuing growth in the use of Unmanned Aerial Vehicles (UAVs) is causing an important social step forward in the performance of many sensitive tasks, reducing both human and economical risks. The work of UAV operators is a key aspect to guarantee the success of this kind of tasks, and thus UAV operations are studied in many research fields, ranging from human factors to data analysis and machine learning. The present work aims to describe the behaviour of operators over time using a profile-based model where the evolution of the operator performance during a mission is the main unit of measure. In order to compare how different operators act throughout a mission, we describe a methodology based of multivariate-time series clustering to define and analyse a set of representative temporal performance profiles. The proposed methodology is applied in a multi-UAV simulation environment with inexperienced operators, obtaining a fair description of the temporal behavioural patterns followed during the course of the simulation.
| Type: | Article |
|---|---|
| Title: | Analysing temporal performance profiles of UAV operators using time series clustering |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.eswa.2016.10.044 |
| Publisher version: | http://doi.org/ 10.1016/j.eswa.2016.10.044 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | UAVs; UAV operators; Time Series Clustering; Performance measures; Simulation-Based Training |
| UCL classification: | UCL 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 |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1561309 |
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