在非结构化路面下使用轮式移动机器人的粘附系数识别
Hongchao Zhang1,2, Bao Song1, Junming Xu1
1School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
这项研究提出了一种使用扩展卡尔曼波器的先进方法,以准确估计非结构化路面的峰值附着系数,提高车辆在具有挑战性的路面上的安全性.
科学领域:
- 汽车工程 汽车工程
- 道路表面分析 道路表面分析
- 控制系统 控制系统
背景情况:
- 由于坡度不均,非结构化路面对准确确定道路粘附系数提出了重大挑战.
- 现有的方法难以应对变化的道路表面的复杂性,影响车辆动态和安全.
研究的目的:
- 开发一种改进的非结构化路面的峰值附着系数估计方法.
- 在具有挑战性的道路条件下提高粘附系数识别的准确性和可靠性.
主要方法:
- 扩展卡尔曼波器 (EKF) 方法用于实时估计.
- 引入了相当的悬架模型,以优化垂直轮载荷计算.
- 车辆加速和姿势数据被修改和整合,以提高准确性.
主要成果:
- 拟议的方法显著提高了道路粘附系数的识别精度.
- 模拟实验表明,估计准确度至少提高了3.6%.
- 该算法的精度和有效性通过使用Carsim.com的多条件模拟来验证.
结论:
- 开发的基于扩展卡尔曼波器的方法有效地估计了非结构化路面的峰值附着系数.
- 同等悬架模型和修改的车辆数据的整合提高了估计精度.
- 这种算法提供了一个强大的解决方案,用于改善复杂路面上的车辆控制和安全.
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