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首页> 外文期刊>Journal of cognitive engineering and decision making >Close Target Reconnaissance: A Field Evaluation of Dismounted Soldiers Utilizing Video Feed From an Unmanned Ground Vehicle in Patrol Missions
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Close Target Reconnaissance: A Field Evaluation of Dismounted Soldiers Utilizing Video Feed From an Unmanned Ground Vehicle in Patrol Missions

机译:近距离目标侦察:在巡逻任务中利用无人地面车辆的视频馈送对下岗士兵进行现场评估

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The focus of the current study was on how the dismounted soldiers' decision cycle is affected by the use of a display device for utilizing intelligence from an unmanned ground vehicle during a patrol mission. Via a handheld monocular display, participants received a route map and sensor imagery from the vehicle that was ~20-50 m ahead. Twenty-two male participants were divided into two groups, with or without the sensor imagery. Each participant navigated for 2 km in a military urban terrain training facility, while encountering civilians, moving and stationary suspects, and improvised explosive devices. The OODA loop (observe-orient-decide-act) framework was used to examine soldiers' decisions.The experimental group was slower to respond to threats and to orient.They also reported higher workload, more difficulties in allocating their attention to the environment, and more frustration. These can be partially attributed to the novelty of the technological capability, but also to its implementation in the study. The breakdown of performance metrics into the OODA loop components enabled analysis of the major difficulties in the decision-making process. This evaluation highlights the need for new roles in combat-team setups and for additional training when unmanned vehicle sensor imagery is introduced.
机译:当前研究的重点是在巡逻任务中使用显示设备来利用无人地面车辆的情报来影响下马士兵的决策周期。通过手持式单眼显示器,参与者可以从前方20-50 m的车辆处获得路线图和传感器图像。 22名男性参与者分为两组,有或没有传感器图像。每个参与者在一个军事城市地形训练设施中航行了2公里,同时遇到了平民,动静的犯罪嫌疑人以及简易爆炸装置。 OODA循环(observe-orient-decide-act)框架用于检查士兵的决策,实验组响应威胁和定向的速度较慢,他们还报告了工作量更大,在将注意力分配到环境上更加困难,和更多的挫败感。这些可以部分归因于技术能力的新颖性,也可以归因于其在研究中的实施。将性能指标分解为OODA循环组件可以分析决策过程中的主要困难。这项评估突显了在引入无人驾驶车辆传感器图像时需要在战斗队中发挥新作用以及进行额外培训的需求。

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