Collaborative Human-Robot Exploration via Implicit Coordination

Yves Georgy Daoud, K. Goel, Nathan Michael, Wennie Tabib
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引用次数: 1

Abstract

This paper develops a methodology for collaborative human-robot exploration that leverages implicit coordination. Most autonomous single- and multi-robot exploration systems require a remote operator to provide explicit guidance to the robotic team. Few works consider how to embed the human partner alongside robots to provide guidance in the field. A remaining challenge for collaborative human-robot exploration is efficient communication of goals from the human to the robot. In this paper we develop a methodology that implicitly communicates a region of interest from a helmet-mounted depth camera on the human's head to the robot and an information gain-based exploration objective that biases motion planning within the viewpoint provided by the human. The result is an aerial system that safely accesses regions of interest that may not be immediately viewable or reachable by the human. The approach is evaluated in simulation and with hardware experiments in a motion capture arena. Videos of the simulation and hardware experiments are available at: https://youtu.be/7jgkBpVFIoE.
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基于隐式协调的人机协作探索
本文提出了一种利用隐式协调的人机协作探索方法。大多数自主的单机器人和多机器人勘探系统需要远程操作员为机器人团队提供明确的指导。很少有人考虑如何将人类伴侣嵌入机器人身边,以在该领域提供指导。人机协作探索的另一个挑战是人与机器人之间目标的有效沟通。在本文中,我们开发了一种方法,该方法隐含地将人类头部上的头盔深度相机感兴趣的区域传达给机器人,并基于信息增益的探索目标,该目标在人类提供的视点内偏差运动规划。其结果是一个空中系统,可以安全地进入可能无法立即被人类看到或到达的感兴趣区域。该方法在仿真和运动捕捉领域的硬件实验中进行了评估。模拟和硬件实验的视频可以在https://youtu.be/7jgkBpVFIoE上找到。
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