Resolving Conflicts During Human-Robot Co-Manipulation
Resolving Conflicts During Human-Robot Co-Manipulation
复制标题
解决人机协同操作期间的冲突
DOI：
10.1145/3568162.3576969
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Al-Saadi Z
中科院分区：
文献类型：
--
作者：
Al-Saadi Z
本文提出了一种机器学习（ML）的方法来检测和解决运动冲突之间发生的人和一个积极的机器人在执行一个物理协作任务。我们训练了一个随机森林分类器来区分对象协同操作过程中和谐和冲突的人机交互行为。通过团队协作产生的动觉信息被用来描述协作的交互质量。因此，我们证明了来自触觉（力/扭矩）数据的功能足以分类，如果人类和机器人和谐地操纵对象或他们面临的冲突。冲突解决策略的实施，让机器人的合作伙伴，通过在线轨迹规划的任务，只要互动的运动模式是和谐的，并遵循人类的领导时，检测到冲突。一个准入控制器调节人和机器人之间的物理交互在任务期间。这使得机器人能够在发生冲突时被动地跟随人类。人工势场是用来主动控制机器人运动时，合作伙伴的和谐工作。一个实验研究的目的是创建场景，涉及和谐和冲突的相互作用，在协同操作的对象，并创建一个数据集来训练和测试的随机森林分类器。研究结果表明，ML可以成功地检测冲突，并且与总是跟随人类伙伴的被动机器人和无法解决冲突的主动机器人相比，所提出的冲突解决机制显着减少了人力和精力。
This paper proposes a machine learning (ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish between harmonious and conflicting human-robot interaction behaviors during object co-manipulation. Kinesthetic information generated through the teamwork is used to describe the interactive quality of collaboration. As such, we demonstrate that features derived from haptic (force/torque) data are sufficient to classify if the human and the robot harmoniously manipulate the object or they face a conflict. A conflict resolution strategy is implemented to get the robotic partner to proactively contribute to the task via online trajectory planning whenever interactive motion patterns are harmonious, and to follow the human lead when a conflict is detected. An admittance controller regulates the physical interaction between the human and the robot during the task. This enables the robot to follow the human passively when there is a conflict. An artificial potential field is used to proactively control the robot motion when partners work in harmony. An experimental study is designed to create scenarios involving harmonious and conflicting interactions during collaborative manipulation of an object, and to create a dataset to train and test the random forest classifier. The results of the study show that ML can successfully detect conflicts and the proposed conflict resolution mechanism reduces human force and effort significantly compared to the case of a passive robot that always follows the human partner and a proactive robot that cannot resolve conflicts.
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DOI：
10.1016/j.automatica.2008.08.021
发表时间：
2009-02
期刊：
Autom.
影响因子：
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作者：
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影响因子：
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DOI：
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发表时间：
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期刊：
2014 IEEE-RAS International Conference on Humanoid Robots
影响因子：
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作者：
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影响因子：
3.4
作者：
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通讯作者：
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