基于过滤事件数据的原始制造任务分类
Classification of Primitive Manufacturing Tasks from Filtered Event Data
摘要：
协作机器人越来越多地出现在工业中，以支持人类活动。然而，要使人机协作过程更有效，还有几个挑战需要解决。协作机器人系统需要了解人类活动，以（1）预测协作/辅助动作，（2）通过演示学习，以及（3）激活共享工作空间中的安全程序。本研究提出一个动作分类系统，从动态主动像素视觉传感器（DAVIS）捕捉的人体运动事件数据中识别原始装配任务。比较并组合几种滤波器以去除事件数据噪声。使用高级深度学习和递归网络对空间和时间特征进行分类，从连续的事件数据流中对任务模式进行分类。实验进行了一个新的数据集，制造任务的数据集（DMT 22），具有5类代表性的制造原语（拾取，放置，螺丝，保持，空闲）从5个参与者。实验结果表明，该滤波器能有效去除65%的噪声，对训练系统的受试者和新受试者的分类准确率分别达到99.37%和97.08%。仅使用右手训练数据成功地对来自左手受试者的数据进行了分类。这些结果与对象无关。
英文摘要：
Collaborative robots are increasingly present in industry to support human activities. However, to make the human-robot collaborative process more effective, there are several challenges to be addressed. Collaborative robotic systems need to be aware of the human activities to (1) anticipate collaborative/assistive actions, (2) learn by demonstration, and (3) activate safety procedures in shared workspace. This study proposes an action classification system to recognize primitive assembly tasks from human motion events data captured by a Dynamic and Active-pixel Vision Sensor (DAVIS). Several filters are compared and combined to remove event data noise. Task patterns are classified from a continuous stream of event data using advanced deep learning and recurrent networks to classify spatial and temporal features. Experiments were conducted on a novel dataset, the dataset of manufacturing tasks (DMT22), featuring 5 classes of representative manufacturing primitives (PickUp, Place, Screw, Hold, Idle) from 5 participants. Results show that the proposed filters remove about 65\% of all events (noise) per recording, conducting to a classification accuracy up to 99,37\% for subjects that trained the system and 97.08\% for new subjects. Data from a left-handed subject were successfully classified using only right-handed training data. These results are object independent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
