使用2DOF机器人手臂进行视觉控制的自主物体跟踪
Umesh Kumar Sahu1, Mebin K S1, Abhinav K1
1Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Scientific reports
|April 18, 2025
概括
本研究介绍了一种基于深度学习的系统,用于使用2DOF机器人手臂精确的实时对象跟踪. 基于视觉的控制增强了各种应用的自主功能,提高了准确性和响应时间.
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
- 人工智能的人工智能
背景情况:
- 使用机器人操纵器实时跟踪对象对于制造业和医疗保健等应用至关重要.
- 传统系统在传感器监控,稳定性和复杂性方面面临挑战.
- 现有的视觉伺服方法有局限性,这项研究旨在克服这些局限性.
研究的目的:
- 为机器人手臂设计一个精确和响应的物体跟踪系统.
- 消除与传统追踪机制相关的复杂性.
- 开发一种基于视觉的自主控制系统,用于移动物体跟踪.
主要方法:
- 基于图像的视觉伺服 (IBVS) 方法用于2度自由度 (DOF) 的机器人手臂.
- 基于深度学习的对象检测框架被用于实时对象识别和定位.
- 设计了一种基于视觉的控制技术,整合了对象检测系统的实时响应.
主要成果:
- 拟议的深度学习控制器在视觉伺服任务中表现出高精度和快速响应时间.
- 使用CoppeliaSim的模拟和使用2DOF机器人臂的实验验证证证了该策略的有效性.
- 该系统成功地自主跟踪移动的物体.
结论:
- 开发的基于深度学习的视觉控制策略为机器人手臂对象跟踪提供了强大的解决方案.
- 该方法通过减少对复杂机制和多个传感器的依赖来简化传统方法.
- 对数据驱动学习技术的进一步探索可以提高控制方案的适应性和稳定性.
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