在与机器人合作的任务中识别人类行为:通过工具识别方法扩展动作识别
Lukas Büsch1, Julian Koch1, Daniel Schoepflin1
1Hamburg University of Technology, Institute of Aircraft Production Technology, Denickestraße 17, 21073 Hamburg, Germany.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一种新的在线工具识别系统,用于手工组装,改进协作任务. 该方法使用骨架数据和图像分类来准确识别手持工具,增强人类行动识别.
科学领域:
- 机器人和自动化 机器人和自动化
- 计算机视觉 计算机视觉
- 人与计算机的交互
背景情况:
- 手动组装过程需要高效的工具识别,以跟踪进展,并在协作任务中识别人类行动 (HAR).
- 现有的进展检测和工具识别方法在实时,集成应用程序方面存在局限性.
研究的目的:
- 开发和实施一种新的在线工具识别方法,用于手持工具在手工组装.
- 将这种方法与现有的人类行动识别 (HAR) 系统集成到协作任务中.
- 评估拟议管道的通用性和性能.
主要方法:
- 开发了一个两阶段的管道: 1. 从骨数据中使用手腕位置提取感兴趣区域 (ROI). 2. 2. 2. 这是一个很棒的节目. 在裁剪的ROI中对工具进行分类.
- 利用各种对象识别算法和图像分类方法.
- 创建了一个广泛的培训数据集,用于工具识别.
主要成果:
- 该管道在不同的算法和工具类中展示了通用性.
- 离线评估包括12个工具类,显示出具有竞争力的预测准确性和稳定性.
- 在组装场景中的在线测试,具有各种实例和背景,证实了系统的功能.
结论:
- 新型在线工具识别管道是有效的,与现有方法相比具有竞争力.
- 该方法在精度,稳定性,多样性和手工组装在线能力方面具有优势.
- 这项工作推动了基于视觉的工具识别在协作机器人 HAR 系统中的整合.
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