Probabilistic approach to recognize local navigation plans by fusing past driving information with a personalized user model
Probabilistic approach to recognize local navigation plans by fusing past driving information with a personalized user model
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通过将过去的驾驶信息与个性化用户模型融合来识别本地导航计划的概率方法
DOI：
10.1109/icra.2013.6631197
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
J. Schutter
中科院分区：
文献类型：
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作者：
Alexander Hüntemann;E. Demeester;E. V. Poorten;H. Brussel;J. Schutter
由于电动轮椅体积大，机动性有限，驾驶电动轮椅可能非常具有挑战性。此外，目标用户经常患有认知或身体残疾，这干扰了安全导航。因此，帮助驾驶的机器人轮椅可以被证明是无价的。这样的轮椅与它的人类操作员共享控制。通常情况下，机器人擅长精细运动控制，而用户希望保持掌控权。因此，机器人应该将其帮助集中在本地，并让用户决定全局行为。此外，一个有效的机器人应该了解其用户的导航计划。它需要考虑用户的能力，以避免错误的帮助让用户感到沮丧。为了解决这些需求，我们提出了一种概率框架，以特定于用户的方式识别本地导航计划。该框架在线推断导航计划，并提供了一种根据实际驾驶数据校准所有模型参数的方法。它将过去的本地信息与用户特定的模型融合在一起，以推断用户打算如何导航以及导航到哪里。我们通过识别日常驾驶环境中痉挛用户的本地导航计划来说明我们方法的有效性。
Navigating an electrical wheelchair can be very challenging due to its large size and limited maneuverability. Additionally, target users often suffer from cognitive or physical disabilities, which interfere with safe navigation. Therefore, a robotic wheelchair that helps to drive can prove invaluable. Such a wheelchair shares the control with its human operator. Typically, robots excel in fine-motion control whereas users want to remain in charge. Hence, the robot should focus its help locally and let the user decide about global behavior. Further, an effective robot should understand the navigation plans of its user. It needs to consider the user's abilities to avoid frustrating the user with wrong assistance. In order to address these requirements, we propose a probabilistic framework to recognize local navigation plans in a user-specific way. The framework infers navigation plans online and provides a method to calibrate all model parameters from real driving data. It fuses past local information with a user-specific model to reason about how and where the user intends to navigate. We illustrate the validity of our approach by recognizing the local navigation plans of a spastic user driving in a daily environment.