论文标题
机器人指南的多行为计划框架
A Multi-Behavior Planning Framework for Robot Guide
论文作者
论文摘要
移动机器人的指导任务不仅需要人为意识的导航，而且还需要适当但及时的互动以进行主动指导。最先进的旅游指南模型限制了他们对社会意识的考虑，以适应用户的动议，而忽略了满足交流需求的交互行为计划。我们提出了一个基于蒙特卡洛树搜索的多行为计划框架，以更好地帮助用户了解令人困惑的场景上下文，选择正确的路径并及时到达目的地。为了提供积极的指导，我们构建了基于抽样的人类运动的概率模型，以考虑机器人与人之间的相互关联效应。我们在模拟和现实世界实验中验证了我们的方法，以及与最新模型的性能比较。
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.
