Literature DB >> 27633199

A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management.

Amy Hocraffer1, Chang S Nam2.   

Abstract

A meta-analysis was conducted to systematically evaluate the current state of research on human-system interfaces for users controlling semi-autonomous swarms composed of groups of drones or unmanned aerial vehicles (UAVs). UAV swarms pose several human factors challenges, such as high cognitive demands, non-intuitive behavior, and serious consequences for errors. This article presents findings from a meta-analysis of 27 UAV swarm management papers focused on the human-system interface and human factors concerns, providing an overview of the advantages, challenges, and limitations of current UAV management interfaces, as well as information on how these interfaces are currently evaluated. In general allowing user and mission-specific customization to user interfaces and raising the swarm's level of autonomy to reduce operator cognitive workload are beneficial and improve situation awareness (SA). It is clear more research is needed in this rapidly evolving field.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Human factors; Human-robot interaction; Human-swarm interaction; Human-system interface; Multi-robot systems; Swarm; Unmanned aerial vehicle (UAV)

Mesh:

Year:  2016        PMID: 27633199     DOI: 10.1016/j.apergo.2016.05.011

Source DB:  PubMed          Journal:  Appl Ergon        ISSN: 0003-6870            Impact factor:   3.661


  3 in total

1.  Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction.

Authors:  Juan Jesús Roldán; Elena Peña-Tapia; Andrés Martín-Barrio; Miguel A Olivares-Méndez; Jaime Del Cerro; Antonio Barrientos
Journal:  Sensors (Basel)       Date:  2017-07-27       Impact factor: 3.576

Review 2.  A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints.

Authors:  Mario Coppola; Kimberly N McGuire; Christophe De Wagter; Guido C H E de Croon
Journal:  Front Robot AI       Date:  2020-02-25

3.  A Machine Learning Method for Vision-Based Unmanned Aerial Vehicle Systems to Understand Unknown Environments.

Authors:  Tianyao Zhang; Xiaoguang Hu; Jin Xiao; Guofeng Zhang
Journal:  Sensors (Basel)       Date:  2020-06-07       Impact factor: 3.576

  3 in total

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