基于物理运动模型和YOLOv3的桌球训练机器人捕捉偏差的优化方法
1College of Physical Education and Health, Shaanxi University of Chinese Medicine, Xianyang, 712046, China. wangxudong131452@163.com.
Scientific reports
|January 6, 2026
概括
这项研究通过优化感知,预测和控制系统来增强乒乓球机器人. 协作优化显著减少了捕捉偏差和系统延迟,以提高机器人的性能.
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
- 控制系统 控制系统
背景情况:
- 乒乓球训练机器人因检测不准确,轨迹预测错误和系统延迟而面临接受偏差的挑战.
- 现有的系统经常与乒乓球的动态和快节奏性质作斗争.
- 提高这些机器人的精度和响应能力对于有效的培训至关重要.
研究的目的:
- 通过解决接收偏差和系统延迟等关键问题,提高乒乓球训练机器人的性能.
- 实施一个"感知-预测-控制"全链协作优化战略.
- 为桌球训练机器人的智能升级提供技术支持.
主要方法:
- 改进了你只看一次版本3 (YOLOv3) 检测网络,包括卷积注意力模块,自适应空间特征融合,以及用于小物体检测的CIoU损失.
- 构建了一个包含旋转和空气动力学的高阶物理运动模型,与扩展的卡尔曼波器合并,以获得最佳的轨迹估计.
- 设计了一个复合控制策略,将前轨迹规划和反补偿相结合,以减轻系统延迟.
主要成果:
- 增强的检测模型实现了98.8%±0.5%的准确性和97.5%±0.6%的回忆.
- 扩展的卡尔曼波器和高阶模型将平均轨迹预测误差降低到8.7 mm ± 1.2 mm (75.6%的减少).
- 控制策略将中位撞击偏差降低到12.4毫米,将捕获成功率提高到95.2%,并将系统延迟从42ms降低到32ms.
结论:
- 多模块协作优化显著减少了乒乓球训练机器人的捕捉偏差.
- 拟议的"感知-预测-控制"框架为增强动态环境中的机器人性能提供了可行的解决方案.
- 这项研究为运动目标跟踪和拦截设备提供了有价值的见解.
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