实时以目标为导向的抓取框架,用于资源有限的机器人.
Dongxiao Han1, Haorong Li1, Yuwen Li1
1Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 201900, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一个有效的框架,用于实时以目标为导向的抓取具有有限计算能力的机器人. 该系统在各种场景中取得了很高的成功率,从而实现了移动机器人应用.
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
- 人工智能的人工智能
背景情况:
- 面向目标的抓取对于家庭和工业机器人来说至关重要.
- 在移动机器人上部署抓取系统是由于计算限制而具有挑战性的.
研究的目的:
- 在资源有限的平台上开发一个有效的框架,用于实时以目标为导向的抓取.
- 支持基于点击的对未知对象的抓取以及基于类别的对已知对象的抓取.
主要方法:
- 压缩YOLOv8以减少模型复杂性和保持检测准确性.
- GR-ConvNetv2用于掌握姿势生成,通过MobileSAMv2口罩进行精炼.
- 一个基于几何的校正模块,以提高掌握姿势的准确性.
主要成果:
- 在康奈尔数据集上取得了98.8%的掌握成功,在杰卡德数据集上达到95.8%.
- 在现实世界杂乱和单个对象场景中证明了90%以上的成功.
- 实时性能:每67毫秒 (基于点击) 和75毫秒 (基于类别).
结论:
- 拟议的框架提供了高掌握精度和强大的性能.
- 这种高效的设计可用于移动机器人和资源有限的机器人.
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