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Deep learning-based human motion recognition for predictive context-aware human-robot collaboration

机译:基于深度学习的人类运动识别,用于预测背景感知人体机器人协作

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摘要

Timely context awareness is key to improving operation efficiency and safety in human-robot collaboration (HRC) for intelligent manufacturing. Visual observation of human workers' motion provides informative clues about the specific tasks to be performed, thus can be explored for establishing accurate and reliable context awareness. Towards this goal, this paper investigates deep learning as a data driven technique for continuous human motion analysis and future HRC needs prediction, leading to improved robot planning and control in accomplishing a shared task. A case study in engine assembly is carried out to validate the feasibility of the proposed method. (C) 2018 Published by Elsevier Ltd on behalf of CIRP.
机译:及时的背景知识是提高人机协作(HRC)的运营效率和安全的关键,以实现智能制造。 人工工作人员的视觉观察为有关要执行的具体任务提供信息性的线索,因此可以探索建立准确和可靠的语境意识。 在实现这一目标上,本文调查了深入学习作为持续人类运动分析和未来HRC需求预测的数据驱动技术,导致机器人规划和控制完成共享任务。 执行在发动机组件中的案例研究以验证所提出的方法的可行性。 (c)2018年由elsevier有限公司发布代表CIRP。

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