基于双向A*算法和YOLOv11n模型的智能汽车自动充电导航系统的优化
Shengkun Liao1, Lei Zhang1, Yunli He2
1Automotive and Transportation School, Tianjin University of Technology and Education, Tianjin 300222, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了用于电动汽车自主充电的智能导航系统. 它结合了改进的A*路径规划算法和优化的YOLOv11n模型,以实现高效和准确的充电导航.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 智能运输系统 智能运输系统
- 计算机视觉 计算机视觉
背景情况:
- 智能汽车需要强大的自主充电能力,特别是在低电池条件下.
- 有效的路径规划和准确的视觉识别对于成功的自主充电导航至关重要.
- 现有系统在动态环境和充电基础设施的精确本地化方面面临挑战.
研究的目的:
- 开发和评估智能汽车自动充电的综合导航系统.
- 通过改进的双向A*算法和优化YOLOv11n模型的视觉识别来增强路径规划.
- 为智能汽车提供高效的能源管理解决方案.
主要方法:
- 实施了改进的双向A*算法,具有动态启发函数和贪的修剪策略,用于无碰撞路径生成.
- 使用立方贝齐尔曲线平滑路径,以实现实际的车辆运动.
- 增强了YOLOv11n模型的CAFMFusion机制,以改善功能融合和充电区域和堆的检测精度.
主要成果:
- 与传统方法相比,改进的双向A*算法显著减少了计算时间,并保持了稳定性.
- 优化的YOLOv11n模型实现了最先进的精度和mAP@0.5,在小物体检测方面表现出色.
- 实验评估证明了该系统在复杂的室内环境中的有效性.
结论:
- 拟议的导航系统成功实现了智能汽车的自主充电.
- 集成先进的路径规划和视觉识别为现实世界自主充电提供了高效的解决方案.
- 这项工作有助于推进智能汽车的能源管理策略.
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