通过在线滑落估计纯追踪算法的性能改进,用于越野追踪车辆
1Graduate School of Science and Engineering, Hacettepe University, Beytepe, 06800 Ankara, Türkiye.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究通过整合扩展的卡尔曼波器 (EKF) 来提高追踪机器人的自主导航,以估计即时旋转中心 (ICR). 与传统方法相比,这种增强的运动控制显著提高了44%的路径跟踪精度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 自主导航自主导航自主导航自主导航自主导航自主导航
背景情况:
- 运动路径跟踪算法对于移动机器人导航至关重要.
- 目前的方法在实时准确性方面面临挑战,特别是在重型车辆中.
- 改进运动控制可以提高自动驾驶能力.
研究的目的:
- 为了提高跟踪移动机器人的路径跟踪性能.
- 将扩展卡尔曼波器 (EKF) 与动力控制器集成在一起.
- 为了提高自主导航的运动控制的准确性.
主要方法:
- 使用扩展卡尔曼波器 (EKF) 估计使用GPS和IMU数据即时旋转中心 (ICR).
- 将基于EKF的ICR估计集成到动力运动控制算法中.
- 采用了一辆越野轨道车辆的高保真模拟模型,通过实地测试进行验证.
主要成果:
- 提出的基于EKF的方法显著提高了路径跟踪精度约44%.
- 现实世界的实地测试验证了在稳定的道路上增强的性能.
- 与经典纯追求算法相比,集成显示出更高的准确性.
结论:
- 经EKF增强的动力控制器为追踪移动机器人提供了可观的路径跟踪精度改进.
- 这种方法对于控制重型越野车来说是有效的,考虑到滑动值.
- 该方法为精确的自主导航提供了强大的解决方案.
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