Supervisory Control of Multiple Robots: Effects of Imperfect Automation and Individual Differences
Supervisory Control of Multiple Robots: Effects of Imperfect Automation and Individual Differences
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DOI：
10.1177/0018720811435843
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发表时间：
2012-04-01
期刊：
影响因子：
3.3
通讯作者：
Barnes, Michael J.
中科院分区：
文献类型：
--
作者：
Chen, Jessie Y. C.;Barnes, Michael J.
目的：模拟军事多任务环境，检查智能代理 RoboLeader 对机器人操作员表现的影响。背景：参与者的任务是在 RoboLeader 的帮助下管理地面机器人团队，RoboLeader 是一个能够根据战场发展情况协调机器人并改变机器人路线的智能代理。方法：在第一个实验中，RoboLeader 完全可靠；在第二个实验中，RoboLeader 的建议被操纵为容易误报或容易漏报，可靠性水平为 60% 或 90%。目标环境的视觉密度由友军士兵的存在或不存在来控制。结果：RoboLeader 在完全可靠时有助于减少总体任务时间。 RoboLeader 缺陷的类型（误报与漏报）影响了操作员执行涉及视觉扫描（目标检测、路线编辑和态势感知）的任务的性能。对于多种性能测量，视觉密度（视觉场景的混乱）具有一致的效果。参与者的注意力控制和视频游戏体验影响了他们的整体多任务表现。在这两项实验中，在需要有效视觉扫描的任务中，具有较高空间能力的参与者始终优于空间能力较低的参与者。结论：智能代理（例如 RoboLeader）可以提高人机协作的整体表现。然而，代理类型的不可靠性、任务要求和个体差异对人机交互具有复杂的影响。 应用：当前的结果将有助于机器人在军事环境中的实施，并将为多机器人控制系统的设计提供有用的数据。
Objective: A military multitasking environment was simulated to examine the effects of an intelligent agent, RoboLeader, on the performance of robotics operators.Background: The participants' task was to manage a team of ground robots with the assistance of RoboLeader, an intelligent agent capable of coordinating the robots and changing their routes on the basis of battlefield developments.Method: In the first experiment, RoboLeader was perfectly reliable; in the second experiment, RoboLeader's recommendations were manipulated to be either false-alarm prone or miss prone, with a reliability level of either 60% or 90%. The visual density of the targeting environment was manipulated by the presence or absence of friendly soldiers.Results: RoboLeader, when perfectly reliable, was helpful in reducing the overall mission times. The type of RoboLeader imperfection (false-alarm vs. miss prone) affected operators' performance of tasks involving visual scanning (target detection, route editing, and situation awareness). There was a consistent effect of visual density (clutter of the visual scene) for multiple performance measures. Participants' attentional control and video gaming experience affected their overall multitasking performance. In both experiments, participants with greater spatial ability consistently outperformed their low-spatial-ability counterparts in tasks that required effective visual scanning.Conclusion: Intelligent agents, such as RoboLeader, can benefit the overall human-robot teaming performance. However, the effects of type of agent unreliability, tasking requirements, and individual differences have complex effects on human-agent interaction.Application: The current results will facilitate the implementation of robots in military settings and will provide useful data to designs of systems for multirobot control.