Active assistive systems for mobility aids are largely restricted to environments mapped a-priori, while passive assistance primarily provides collision mitigation and other hand-crafted behaviors in the platform’s immediate space. This paper presents a framework providing active short-term assistance, combining the freedom of location independence with the intelligence of active assistance. Demonstration data consisting of on-board sensor data and driving inputs is gathered from an able-bodied expert maneuvring the mobility aid around a generic interior setting, and used in constructing a probabilistic intention model built with Radial Basis Function Networks. This allows for short-term intention prediction relying only upon immediately available user input and on-board sensor data, to be coupled with real-time path generation based upon the same expert demonstration data via Dynamic Policy Programming, a stochastic optimal control method. Together these two elements provide a combined assistive mobility system, capable of operating in restrictive environments without the need for additional obstacle avoidance protocols. Experimental results in both simulation and on the University of Technology Sydney semi-autonomous wheelchair in settings not seen in training data show promise in assisting users of power mobility aids.

中文翻译：

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从示范中学习本地辅助行动辅助

用于行动辅助的主动辅助系统在很大程度上限于先验映射的环境，而被动辅助主要在平台的直接空间中提供碰撞缓解和其他手工行为。本文提出了一个提供主动短期协助的框架，将位置独立性的自由与主动协助的智能相结合。演示数据由机载传感器数据和驾驶输入组成，这些专家数据是由训练有素的专家围绕通用的内部环境操纵机动性辅助工具而来，并用于构建使用径向基函数网络构建的概率意图模型。这样一来，就可以仅根据立即可用的用户输入和机载传感器数据进行短期意图预测，通过动态策略编程（一种随机最优控制方法），基于相同的专家演示数据，结合实时路径生成。这两个要素共同提供了一个组合式辅助机动系统，能够在限制性环境中运行，而无需其他避障协议。在训练数据中未见的模拟和在悉尼科技大学半自动轮椅上的实验结果表明，有望为电动助行器的使用者提供帮助。