论文标题
最小化机器人导航图表，以实现人类基于位置的可预测性
Minimizing Robot Navigation-Graph For Position-Based Predictability By Humans
论文作者
论文摘要
在人类和机器人在执行自己的任务同时移动的情况下，移动机器人采取的可预测路径不仅可以使环境变得更安全，而且人类还可以通过避免路径冲突或不阻止路线来帮助解决空间的导航。因此，可预测的路径变得至关重要。随着机器人数量的增加，人类预测机器人路径的认知努力变得站不住脚。随着人类数量的增加，这也使机器人在考虑多个人的运动时更难移动。此外，如果新人们进入该空间（例如在餐馆，银行和医院），他们对机器人通常采用的轨迹的熟悉程度较小。这进一步增加了沿路径的可预测机器人运动的需求。 考虑到这一点，我们建议最大程度地减少机器人的导航环，以实现基于位置的可预测性，这是从机器人的当前位置提供的可预测性。这很重要，因为除了执行自己的任务外，不能期望人类继续跟踪机器人的目标和先前行动。在本文中，我们定义了基于位置的可预测性的措施，然后呈现并评估一种爬山算法，以最大程度地减少机器人运动的导航图（有向图）。接下来是我们的人类受试者实验的结果，这些实验支持我们提出的方法。
In situations where humans and robots are moving in the same space whilst performing their own tasks, predictable paths taken by mobile robots can not only make the environment feel safer, but humans can also help with the navigation in the space by avoiding path conflicts or not blocking the way. So predictable paths become vital. The cognitive effort for the human to predict the robot's path becomes untenable as the number of robots increases. As the number of humans increase, it also makes it harder for the robots to move while considering the motion of multiple humans. Additionally, if new people are entering the space -- like in restaurants, banks, and hospitals -- they would have less familiarity with the trajectories typically taken by the robots; this further increases the needs for predictable robot motion along paths. With this in mind, we propose to minimize the navigation-graph of the robot for position-based predictability, which is predictability from just the current position of the robot. This is important since the human cannot be expected to keep track of the goals and prior actions of the robot in addition to doing their own tasks. In this paper, we define measures for position-based predictability, then present and evaluate a hill-climbing algorithm to minimize the navigation-graph (directed graph) of robot motion. This is followed by the results of our human-subject experiments which support our proposed methodology.
