人机协同搬运中人体运动的长短期预测

Sisi Liu, Kaihang Ye, Mengda Geng, Xinbo Yu, Yi Xing, Guoliang Li, Wei He
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引用次数: 0

摘要

提出了一种基于长短期记忆(LSTM)的人机协同搬运任务中长时间人体运动预测方法。利用长期人体运动预测来预测任务中的人体运动目标。如果机器人意识到目标,机器人将引导任务,以减少人类的控制努力。如果机器人不知道人类的运动目标,人类就会把任务引向目标。混合视觉力控制器在动态层面上对人体的短期运动进行预测,以减少运动过程中的运动延迟。最后,通过人机协同搬运任务的实验验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Long Short-Term Human Motion Prediction in Human-Robot Co-Carrying
In this paper, a long short-term human motion prediction method based on long short-term memory (LSTM) is proposed in human-robot co-carrying task. Long-term human motion prediction is utilized to predict the human motion target in the task. If robot is aware of the target, robot will lead the task to reduce human's control efforts. If robot does not know the human motion target, human will lead the task towards the target. Short-term human motion prediction is involved in hybrid visual-force controller in dynamical level to reduce the motion delay in the process. Finally, the method is verified by experiment of human-robot co-carrying task.
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