Learning to Identify Users and Predict Their Destination in a Robotic Guidance Application
Learning to Identify Users and Predict Their Destination in a Robotic Guidance Application
复制标题
学习在机器人导航应用程序中识别用户并预测他们的目的地
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
R. Siegwart
中科院分区：
文献类型：
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作者：
Xavier Perrin;F. Colas;Cédric Pradalier;R. Siegwart
用户引导系统与服务机器人领域的各种应用相关，其中包括：智能GPS导航仪、博物馆或购物中心的机器人导游或残疾人的机器人轮椅。这样的系统旨在帮助用户在相当复杂的环境中到达目的地。如果我们假设系统在固定环境中由多个用户在数天或数周内用于多个导航任务，则可以利用用户例程：从最初的导航选择，可以识别用户并预测他们的目标。这些预测的结果是，引导系统可以将用户带到目的地，同时需要更少的交互。此属性对于帮助残疾人尤其重要，对于他们来说，互动是一项长期而复杂的任务。在本文中，我们使用动态贝叶斯模型和环境的拓扑表示来实现用户引导系统。该模型在涉及 4 个人类用户的场景中针对其动作预测的质量进行了评估，结果表明，除了用户身份之外，还可以准确预测用户的目标和动作。
User guidance systems are relevant to various applications of the service robotics field, among which: smart GPS navigator, robotic guides for museum or shopping malls or robotic wheel chairs for disabled persons. Such a system aims at helping its user to reach its destination in a fairly complex environment. If we assume the system is used in a fixed environment by multiple users for multiple navigation task over the course of days or weeks, then it is possible to take advantage of the user routine: from the initial navigational choice, users can be identified and their goal can be predicted. As a result of these prediction, the guidance system can bring its user to its destination while requiring less interaction. This property is particularly relevant for assisting disabled person for whom interaction is a long and complex task. In this paper, we implement a user guidance system using a dynamic Bayesian model and a topological representation of the environment. This model is evaluated with respect to the quality of its action prediction in a scenario involving 4 human users, and it is shown that in addition to the user identity, the goals and actions of the user are accurately predicted.