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.
翻译:当人类和机器人在执行任务的同时在同一个空间移动时,如果人类和机器人在从事自己的任务时在同一个空间移动,移动机器人所走的可预见路径不仅能让环境感到更安全,而且人类也能通过避免路径冲突或不阻拦路径来帮助空间导航。因此,可预测的路径变得至关重要。人类预测机器人路径的认知努力随着机器人数量的增加而变得站不住脚。随着人类数量的增加,机器人在考虑多个人类运动的同时移动也更加困难。此外,如果新的人进入空间 -- -- 如在餐馆、银行和医院 -- -- 他们也会对机器人通常采用的轨迹不甚熟悉;这样会进一步增加对可预见机器人沿路径移动的需要。考虑到这一点,我们建议尽可能减少机器人的导航图,使其具有基于位置的可预测性,这仅仅是机器人目前的位置上的可预测性。这很重要,因为人类无法在完成自己的任务的同时跟踪机器人的目标和先前的行动。在本文中,我们界定了基于定位的可预测性的测量方法,然后通过图表来评估我们所遵循的模型的导航结果。