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虚拟环境的用户意图捕获

程成, 赵东坡, 卢保安(北京理工大学计算机学院智能信息技术北京市重点实验室, 北京 100081)

摘 要
目的 虚拟制造环境中需要复杂精确的3D人机交互.目前的虚拟环境(VE)的主要问题是人在交互过程中的认知和操作负荷太重,交互效率亟需提高.解决此问题的重要途径是提高机器的认知能力.方法 本文研究了用户意图的分析和抽取,并建立多通道用户意图理解的算法,以此来提高交互效率.结果 结合虚拟装配应用给出了典型意图的实验结果并给予分析.通过实验对多通道意图的可用性和可靠性,以及基于意图系统的实时性进行了评估.实验是虚拟装配空间中用户拾取对象意图的实验.当3维鼠标和对象距离为5 000 mm时,传统系统中操作平均耗时5.344 7 s,而基于意图的系统中平均耗时2.326 6 s.基于意图的系统极大地降低了操作的时间和复杂度.结论 采用意图驱动的系统情景转换能在虚拟环境工作中有效地降低人的认知负荷,并能很好地帮助系统开发者进行混成系统的建模和分析,降低开发的复杂度.实践结果表明用户意图理解的多通道模型和算法能极大地提高交互式系统的交互自然性和交互效率.该方法不仅适用于本文所用的虚拟装配系统,对于所有的虚拟环境应用场景都有同样的有效性.
关键词
Intent understanding for virtual environments

Cheng Cheng, Zhao Dongpo, Lu Baoan(Department of Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China)

Abstract
Objective Virtual manufacturing environments require complex and accurate three-dimensional (3D) human-computer interaction (HCI). The main problem of current virtual environments (VEs) is the heavy user burden associated with the cognitive and motor operation aspects, as well as the improvement in HCI efficiency. This problem is solved by promoting the cognitive capability of the machine. This study investigates how user intents are analyzes and abstracted, as well as constructs multimodal intent understanding algorithms. Method Intent-based VE construction is practiced in a virtual assembly system. Experiments on typical intents are conducted and analyzed. A comprehensive evaluation of the usability and reliability of multimodal intent understanding is presented, and the intent-based VE system is demonstrated to be a real-time system. The experiment focuses on the intent of object picking in VE. Result When the distance between the 3D cursor and object is 5 000 mm, the operation costs 5.344 7 s on average in traditional systems, whereas it costs 2.326 6 s on average in intent-based systems. The intent-based system significantly reduces operation time and manipulation complexity. Conclusion Intent-driven scenario transition can significantly enhances the naturalness and efficiency of HCI, as well as effectively reduce the complexity of human-centered VE system analysis and development. Application of intent understanding demonstrates that multimodal intent models and algorithms can efficiently promote the naturalness and efficiency of HCI. This system construction method can be used in any VE system.
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