被动轮 - - 自动引导车辆的新定位系统
Kacper Bereszyński1, Marcin Pelic1, Wojciech Paszkowiak1
1Institute of Mechanical Technology, Poznan University of Technology, Poland.
Heliyon
|February 17, 2025
概括
与驱动轮相比,被动轮为自动驾驶汽车 (AGV) 提供了优越的死算,减少了滑动错误. 惯性传感器提高了准确性,而LiDAR性能严重取决于地图质量和移动条件.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 导航系统 导航系统
- 传感器融合式传感器
背景情况:
- 自动引导车辆 (AGV) 依赖于精确的定位,以实现高效运行.
- 现有的本地化系统包括旋转编码器,态度和方向参考系统 (AHRS) 和LiDAR扫描仪.
- 在AGV本地化方面的挑战来自轮子滑动,传感器噪音和环境因素.
研究的目的:
- 引入和评估被动轮作为AGV的新型定位系统.
- 为了比较被动轮死计算与驱动轮编码器,AHRS和LiDAR的准确性.
- 分析不同AGV移动场景中不同本地化方法的性能.
主要方法:
- 实现了卡尔曼过,用于融合死计算和惯性数据.
- 利用适应式蒙特卡洛定位 (AMCL) 或基于图形的算法用于LiDAR数据融合.
- 在五种不同的移动场景中进行了比较测试,以评估定位的准确性.
主要成果:
- 被动轮死计算显示出优于驱动轮死计算的性能,最大限度地减少了纵向和横向滑动错误.
- 整合AHRS显著提高了定位准确性,特别是在广泛的角度移动时.
- 基于LiDAR的本地化显示了适度的短期结果,有一致的错误,高度依赖于地图质量和操作条件.
结论:
- 被动轮为AGV本地化提供了一个有前途的进步,提供更高的精度和减少滑动.
- 传感器的选择和AGV路径的性质显著影响定位准确性.
- 了解每个本地化技术的优势和局限性对于最佳的AGV系统设计和部署至关重要.
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