Operator Choice Modeling for UAV Visual Search Tasks
Unmanned aerial vehicles (UAVs) provide unprecedented access to imagery of possible ground targets of interest in real time. The availability of this imagery is expected to increase with envisaged future missions of one operator controlling multiple UAVs. This research investigates decision models t...
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| Format: | Article |
| Language: | en_US |
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IEEE Transactions
2014
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| Online Access: | http://hdl.handle.net/1721.1/87020 |
| _version_ | 1826201838152581120 |
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| author | Bertuccelli, L.F. Cummings, M.L. |
| author_facet | Bertuccelli, L.F. Cummings, M.L. |
| author_sort | Bertuccelli, L.F. |
| collection | MIT |
| description | Unmanned aerial vehicles (UAVs) provide unprecedented access to imagery of possible ground targets of interest in real time. The availability of this imagery is expected to increase with envisaged future missions of one operator controlling multiple UAVs. This research investigates decision models that can be used to develop assistive decision support for UAV
operators involved in these complex search missions. Previous human-in-the-loop experiments have shown that operator detection probabilities may decay with increased search time. Providing the operators with the ability to requeue difficult images with the option of relooking at targets later was hypothesized to help operators improve their search accuracy. However, it was not well understood how mission performance could be impacted by
operators performing requeues with multiple UAVs. This work extends a queuing model of the human operator by developing a retrial queue model (ReQM) that mathematically describes the use of relooks. We use ReQM to generate performance predictions through discrete event simulation. We validate these predictions through a human-in-the-loop experiment that evaluates the impact of requeuing on a simulated multiple-UAV mission. Our results suggest that, while requeuing can improve detection accuracy
and decrease mean search times, operators may need additional decision support to use relooks effectively. |
| first_indexed | 2024-09-23T11:57:59Z |
| format | Article |
| id | mit-1721.1/87020 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:57:59Z |
| publishDate | 2014 |
| publisher | IEEE Transactions |
| record_format | dspace |
| spelling | mit-1721.1/870202019-04-10T12:31:42Z Operator Choice Modeling for UAV Visual Search Tasks Bertuccelli, L.F. Cummings, M.L. Decision theory man machine systems unmanned aerial vehicles Unmanned aerial vehicles (UAVs) provide unprecedented access to imagery of possible ground targets of interest in real time. The availability of this imagery is expected to increase with envisaged future missions of one operator controlling multiple UAVs. This research investigates decision models that can be used to develop assistive decision support for UAV operators involved in these complex search missions. Previous human-in-the-loop experiments have shown that operator detection probabilities may decay with increased search time. Providing the operators with the ability to requeue difficult images with the option of relooking at targets later was hypothesized to help operators improve their search accuracy. However, it was not well understood how mission performance could be impacted by operators performing requeues with multiple UAVs. This work extends a queuing model of the human operator by developing a retrial queue model (ReQM) that mathematically describes the use of relooks. We use ReQM to generate performance predictions through discrete event simulation. We validate these predictions through a human-in-the-loop experiment that evaluates the impact of requeuing on a simulated multiple-UAV mission. Our results suggest that, while requeuing can improve detection accuracy and decrease mean search times, operators may need additional decision support to use relooks effectively. 2014-05-15T21:06:29Z 2014-05-15T21:06:29Z 2012-09 Article 1083-4427 http://hdl.handle.net/1721.1/87020 Bertuccelli, L. F., and M. L. Cummings, Operator Choice Modeling for UAV Visual Search Tasks ,IEEE Transaction on Systems, Man, and Cybernetics, Part A: Systems and Humans vol :42 , Issue 5, pp. 1088-1099, Sept. 2012. en_US application/pdf IEEE Transactions |
| spellingShingle | Decision theory man machine systems unmanned aerial vehicles Bertuccelli, L.F. Cummings, M.L. Operator Choice Modeling for UAV Visual Search Tasks |
| title | Operator Choice Modeling for UAV Visual Search Tasks |
| title_full | Operator Choice Modeling for UAV Visual Search Tasks |
| title_fullStr | Operator Choice Modeling for UAV Visual Search Tasks |
| title_full_unstemmed | Operator Choice Modeling for UAV Visual Search Tasks |
| title_short | Operator Choice Modeling for UAV Visual Search Tasks |
| title_sort | operator choice modeling for uav visual search tasks |
| topic | Decision theory man machine systems unmanned aerial vehicles |
| url | http://hdl.handle.net/1721.1/87020 |
| work_keys_str_mv | AT bertuccellilf operatorchoicemodelingforuavvisualsearchtasks AT cummingsml operatorchoicemodelingforuavvisualsearchtasks |