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Human–Robot Collaborative Site Inspection Under Resource Constraints

机译：资源约束下的人机协作现场检查

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This paper is on human-robot collaborative site inspection and target classification. We consider the realistic case that human visual performance is imperfect (depending on the sensory input quality), and that the robot has constraints in communication with human (e. g., limited chances for query, poor channel quality). The robot has limited onboard motion and communication energy and operates in realistic channel environments experiencing path loss, shadowing, and multipath. We then show how to co-optimize motion, sensing, and human queries. Given a probabilistic assessment of human visual performance and a probabilistic channel prediction, we pose the co-optimization as multiple-choice multidimensional knapsack problems. We then propose a linear program-based efficient near-optimal solution, mathematically characterize the optimality gap, showing it to be very small, and mathematically characterize properties of the optimum solution. We then comprehensively validated the proposed approach with extensive real human data (from Amazon MTurk) and real channel data (from downtown San Francisco), confirming that the proposed approach significantly outperforms benchmark methodologies.
机译：本文是关于人机协作站点检查和目标分类的。我们考虑一种现实情况，即人的视觉性能是不完善的（取决于感官输入的质量），并且机器人在与人的通信中受到限制（例如，查询机会有限，信道质量差）。该机器人的车载运动和通信能量有限，并且在遇到路径损耗，阴影和多路径的现实通道环境中运行。然后，我们展示如何共同优化运动，感测和人工查询。给定人类视觉性能的概率评估和概率通道预测，我们将协同优化视为多项选择多维背包问题。然后，我们提出了一个基于线性程序的有效近最优解，用数学方法描述了最优缺口，表明它很小，并用数学方法描述了最优解的性质。然后，我们使用广泛的真实人类数据（来自Amazon MTurk）和真实渠道数据（来自旧金山市区）对提议的方法进行了全面验证，证实了提议的方法明显优于基准方法。

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