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Estimating and reshaping human intention via human--robot interaction

机译:通过人机交互评估和重塑人的意图

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Human--robot interaction (HRI) is studied in two important research areas, intention estimation and intention reshaping. Although there are many studies in the literature that define human intention, new research examines the reshaping of human intentions by using robots in HRI. In this paper, 2 different robot movements are tested in a real environment in order to reshape current human intention. The hidden Markov model (HMM) is used to estimate human intention in our intelligent robotic system. The algorithmic design of the system comprises 2 parts: the first part tracks the moving objects in the environment, and the second part estimates human intention and reshapes the estimated current human intention by using intelligent robots. In the first part, a feature vector consisting of the headings of the human posture and the locations of the humans and robots is created by using video processing techniques. The second part is related to estimating the current intention of a human participant via HMM models and to reshaping the current intention into another intention. The system is tested in a real experimental environment including humans and robots, and the results in the recorded videos are given at the end of the paper.
机译:人机交互(HRI)在两个重要的研究领域中进行了研究:意图估计和意图重塑。尽管文献中有许多定义人类意图的研究,但新的研究通过使用HRI中的机器人来研究人类意图的重塑。在本文中,为了重塑当前的人类意图,在实际环境中测试了2种不同的机器人动作。隐马尔可夫模型(HMM)用于估计我们智能机器人系统中的人类意图。该系统的算法设计包括两部分:第一部分跟踪环境中的移动物体,第二部分通过使用智能机器人估计人类意图并重塑估计的当前人类意图。在第一部分中,使用视频处理技术创建了一个由人的姿势的头部以及人和机器人的位置组成的特征向量。第二部分与通过HMM模型估计人类参与者的当前意图以及将当前意图重塑为另一个意图有关。该系统已在包括人类和机器人在内的真实实验环境中进行了测试,所录制视频的结果在本文末尾给出。

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