S3PM:无结构环境的密集3D点云中自主移动机器人的透规范路径规划
Artem Sazonov1, Oleksii Kuchkin1, Irina Cherepanska2
1Automation Hardware and Software Department, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 37, Prospect Beresteiskyi, 03056 Kyiv, Ukraine.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了S3PM,这是一个在复杂的工业环境中移动机器人导航的新框架. 通过将动态对象信息集成到其映射和路径规划中,S3PM提高了安全性和可靠性,大大减少了碰撞.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 计算机视觉 计算机视觉
- 自主系统 自主系统
背景情况:
- 在混乱的工业环境中,自主导航由于传感器限制和动态元素而具有挑战性.
- 现有的方法在部分可观测性,传感器噪声和移动剂方面存在困难,这影响了工业4.0的可靠性.
- 这就需要先进的解决方案,在非结构化的环境中进行强大的映射和路径规划.
研究的目的:
- 引入S3PM,一个轻量级的调节框架,用于使用3D点云同时绘制和路径规划.
- 开发一个动态意识的场,将占用概率与风险评估的运动线索融合在一起.
- 为了在动态的工业环境中实现可靠的导航,主动避免碰撞,实时重新规划轨迹.
主要方法:
- S3PM利用3D点云上的动态感知场,将占用概率与剩余光流融合在一起.
- 每个voxel都会收到一个风险加权的得分,考虑到几何不确定性和预测的物体动态.
- 多目标成本函数平衡路径长度,流性,安全性和信息获取,通过voxel散列和增量距离转换进行优化.
主要成果:
- 在静态/动态细分中,S3PM的IOU高18-27%;在运动检测中,AUC高0.94-0.97.
- 与OctoMap + RRT*等基准方法相比,该框架的碰撞数量减少了30-45%.
- 该系统在Raspberry Pi 5上以12-15 Hz的频率运行,在NPU加速时达到25-30 Hz.
结论:
- 在具有挑战性的工业环境中,S3PM为自主导航提供了计算效率高和强大的解决方案.
- 该框架集成动态信息的能力提高了工业4.0应用程序的安全性和可靠性.
- 在资源有限的平台上S3PM的性能使其适合于现实世界的部署.
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