Human-aware navigation is an essential requirement for autonomous robots in human-coexisting environments. The goal of conventional navigation is to find a path for a robot to pass through safely and efficiently without colliding with human. Note that if such a path cannot be found, the robot stops until a path is clear. Thus, such collision-avoidance based passive navigation does not work in a congested or narrow space. To avoid this freezing problem, the robot should induce humans to make a space for passing by an adequate inducement method, such as body movement, speech, and touch, depending on the situation. A robot that deliberately clears a path with such actions may make humans uncomfortable, so the robot should also utilize inducements to avoid causing negative feelings. In this study, we propose a fundamental framework of interactive navigation with situation-adaptive multimodal inducement. For a preliminary study, we target a passing scenario in a narrow corridor where two humans are standing and adopt a model-based approach focusing on common parameters. The suitable inducement basically varies depending on the largest space through which a robot can pass, distance between the robot and a human, and human behavior such as conversing. We thus develop a situation-adaptive inducement selector on the basis of the relationship between human–robot proximity and allowable inducement strength, considering robot efficiency and human psychology. The proposed interactive navigation system was tested across some contextual scenarios and compared with a fundamental path planner. The experimental results indicated that the proposed system solved freezing problems, provided a safe and efficient trajectory, and improved humans’ psychological reaction although the evidence was limited to robot planner and hardware design we used as well as certain scenes, contexts, and participants.

中文翻译：

---

情景自适应多模式诱导的交互式导航框架的初步研究：通过场景

感知人类的导航是人类共处环境中自主机器人的基本要求。常规导航的目的是为机器人找到一条安全有效地通过而不会与人碰撞的路径。请注意，如果找不到这样的路径，则机器人会停止直到清除路径。因此，这种基于避免碰撞的被动导航在拥挤或狭窄的空间中不起作用。为了避免这种冻结问题，机器人应根据情况通过适当的诱导方法（例如，身体移动，语音和触摸）来诱使人类腾出空间。机器人故意通过此类动作清除路径可能会使人不舒服，因此，机器人还应利用诱因来避免产生负面感觉。在这个研究中，我们提出了一种基于情境自适应多模式诱导的交互式导航的基本框架。对于初步研究，我们针对两个人站立的狭窄走廊中的通过场景，并采用基于模型的方法来关注共同参数。合适的诱因基本上取决于机器人可以通过的最大空间，机器人与人之间的距离以及诸如对话之类的人类行为而变化。因此，我们在考虑机器人效率和人类心理的基础上，根据人与机器人的接近度与允许的诱导强度之间的关系，开发了一种情境自适应诱导器。所提出的交互式导航系统已在某些上下文场景中进行了测试，并与基本路径规划器进行了比较。