The guiding task of a mobile robot requires not only human-aware navigation, but also appropriate yet timely interaction for active instruction. State-of-the-art tour-guide models limit their socially-aware consideration to adapting to users' motion, ignoring the interactive behavior planning to fulfill the communicative demands. We propose a multi-behavior planning framework based on Monte Carlo Tree Search to better assist users to understand confusing scene contexts, select proper paths and timely arrive at the destination. To provide proactive guidance, we construct a sampling-based probability model of human motion to consider the interrelated effects between robots and humans. We validate our method both in simulation and real-world experiments along with performance comparison with state-of-the-art models.

翻译：移动机器人的指导任务不仅需要人类认知的导航,而且需要适当而及时的互动以进行积极指导。 最先进的巡回指导模型限制其社会认知考虑以适应用户运动,忽视互动行为规划以满足交流需求。 我们提议了一个基于蒙特卡洛树搜索的多行为规划框架,以更好地帮助用户理解混乱的场景背景,选择适当的路径并及时到达目的地。 为了提供积极主动的指导,我们构建了一个基于取样的人类运动概率模型,以考虑机器人与人类之间的关联效应。我们在模拟和现实世界实验中验证了我们的方法,并与最先进的模型进行性能比较。