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An Objective Function to Evaluate Performance of Human-Robot Systems for Target Recognition Tasks

机译:评估目标识别任务人员系统性能的目标函数

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Robots operating in unstructured environments must cope with uncertain, unpredictable and dynamic situations, resulting in lack of information. Introducing a Human-Operator into the system can help simplify the robotic system and improve performance. In previous work we presented a methodology to determine the best collaboration level for an integrated human-robot target recognition system in unstructured environments. The methodology is based on an objective function for target recognition in human-robot systems. In this paper the objective function was expanded to include operational and time costs that are both important for evaluating and optimizing system performance. Numerical analyses of the developed objective function, combined with signal detection theory, were applied for the predefined collaboration levels. Results indicate that optimal collaboration of human and robot in target recognition tasks will always improve the optimal performance of a single human detector.
机译:在非结构化环境中运行的机器人必须应对不确定,不可预测和动态的情况,从而缺乏信息。将人力操作员引入系统可以帮助简化机器人系统并提高性能。在以前的工作中,我们提出了一种方法来确定非结构化环境中集成的人机目标识别系统的最佳协作水平。该方法基于人机系统中目标识别的目标函数。在本文中,目标函数扩大到包括对评估和优化系统性能的重要操作和时间成本。施加的具有信号检测理论的开发目标函数的数值分析应用于预定义的协作水平。结果表明,目标识别任务中的人和机器人的最佳协作将始终提高单个人类探测器的最佳性能。

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