基于图像测量方法的目标托盘的方法和叉插入.
1TICO-AIST Cooperative Research Laboratory for Advanced Logistics, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8560, Japan.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究增强了叉车自主导航,以实现稳定的托盘接近和叉子插入,提高对仓库自动化环境变化的稳定性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 自动化工程自动化工程
背景情况:
- 之前的研究提出了用于托盘检测和倾斜角估计的图像处理.
- 现有的方法需要改进,以提高自动化物流中的环境稳定性.
研究的目的:
- 为了提高自主叉车基于图像导航的稳定性.
- 为了实现稳定,环境独立的托盘方法和叉子插入操作.
- 在模拟的仓库环境中展示集成系统的功能.
主要方法:
- 改进了用于托盘检测和姿势估计的图像处理算法.
- 制定了一个强大的控制策略,用于接近和叉插入阶段.
- 在自动引导叉车 (AGF) 平台上集成算法.
主要成果:
- 增强方法在各种环境条件下显示出稳定的性能.
- 实现了完整的托盘搬运顺序 (接近叉子插入) 的成功和一致执行.
- 该系统在模拟仓库环境中被证明是有效的.
结论:
- 改进的基于图像的导航系统显著提高了自主叉车操作的稳定性和稳定性.
- 这一进步使可靠的自动托盘处理成为可能,这对于仓库的效率和安全至关重要.
- 成功的演示为在复杂的物流环境中实际部署AGF铺平了道路.
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