使用机器对机器技术开发用于并行操纵器的人工视觉
Arailym Nussibaliyeva1, Gani Sergazin2,3, Gulzhamal Tursunbayeva3
1Department of Electronics and Robotics, Almaty University of Power Engineering and Telecommunications named Gumarbek Daukeyev, Almaty 050013, Kazakhstan.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
这项研究将人工视觉系统与机器对机器 (M2M) 通信集成为灵活的三角洲机器人. 这增强了实时目标识别,并加快了组装和分类等制造任务.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 机器对机器的通信
背景情况:
- 灵活的三角洲机器人操纵器需要先进的视觉系统来实现精确的实时操作.
- 整合机器对机器 (M2M) 通信可以增强设备交互和系统性能.
- 优化实时设备交互对于组装和分类等制造过程至关重要.
研究的目的:
- 开发和整合人工视觉系统与M2M通信,用于灵活的三角洲机器人操纵器.
- 通过增强实时交互来提高机器人系统的速度和整体性能.
- 为了实现精确的实时检测,识别和定位目标在有限的空间内制造任务.
主要方法:
- 使用RGB图像作为MASK-R-CNN算法的输入,用于对象检测和识别.
- 适应MASK-R-CNN输出数据用于三角洲机器人控制系统,考虑到其特定特征.
- 集成的M2M通信,以促进机器人手臂对环境变化的快速反应.
主要成果:
- 综合系统在实时目标检测和识别方面表现出高精度.
- 通过M2M通信,机器人臂能够迅速对物体位置的动态变化做出反应.
- 该系统在分类和包装等制造过程中被证明是有效的,即使是移动物体.
结论:
- 人工视觉系统和M2M通信的结合显著提高了灵活的三角洲机器人的能力.
- 这种集成优化了实时设备交互,从而提高了制造业的速度和性能.
- 开发的系统显示出高可靠性和现实世界的应用在自动装配和分拣的潜力.
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