Towards Efficient Human-Robot Collaboration With Robust Plan Recognition and Trajectory Prediction
Towards Efficient Human-Robot Collaboration With Robust Plan Recognition and Trajectory Prediction
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DOI：
10.1109/lra.2020.2972874
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发表时间：
2020-04-01
影响因子：
5.2
通讯作者：
Tomizuka, Masayoshi
中科院分区：
文献类型：
--
作者：
Cheng, Yujiao;Sun, Liting;Tomizuka, Masayoshi
随着制造业从大规模生产转向大规模定制，人机协作（HRC）变得越来越重要。HRC的引入可以显著提高自动化的灵活性和智能性。为了有效地完成HRC系统中的任务，机器人不仅需要预测人类的未来运动，还需要更高级别的计划，即，完成任务的行动顺序。然而，由于人类合作者的随机性和时变性，机器人要高效准确地识别这些任务计划并以安全的方式做出响应是非常具有挑战性的。为了应对这一挑战，我们提出了一个集成的人机协作框架。包括计划识别和轨迹预测模块，用于生成安全有效的机器人运动。这样的框架使机器人能够感知、预测和调整它们的行动以适应人类的工作计划，并智能地避免与人类发生碰撞。此外，通过明确地利用计划和轨迹之间的分层关系，可以实现更鲁棒的计划识别性能。在工业机器人上进行了物理实验，以验证所提出的框架。实验结果表明，该框架能够准确识别工作人员的计划，从而在运动分类噪声存在的情况下，显著提高了人力资源管理团队的时间效率。
Human-robot collaboration (HRC) is becoming increasingly important as the paradigm of manufacturing is shifting from mass production to mass customization. The introduction of HRC can significantly improve the flexibility and intelligence of automation. To efficiently finish tasks in HRC systems, the robots need to not only predict the future movements of human, but also more high-level plans, i.e., the sequence of actions to finish the tasks. However, due to the stochastic and time-varying nature of human collaborators, it is quite challenging for the robot to efficiently and accurately identify such task plans and respond in a safe manner. To address this challenge, we propose an integrated human-robot collaboration framework. Both plan recognition and trajectory prediction modules are included for the generation of safe and efficient robotic motions. Such a framework enables the robots to perceive, predict and adapt their actions to the human's work plan and intelligently avoid collisions with the human. Moreover, by explicitly leveraging the hierarchical relationship between plans and trajectories, more robust plan recognition performance can be achieved. Physical experiments were conducted on an industrial robot to verify the proposed framework. The results show that the proposed framework could accurately recognize the human workers' plans and thus significantly improve the time efficiency of the HRC team even in the presence of motion classification noises.