基于立体视觉和深度学习的运动的双模机器人接轨迹生成方案
Xinlei Li1, Jiawei Ma1, Shida Yao1
1State Key Laboratory of Precision Welding & Joining of Materials and Structures, Harbin Institute of Technology, Harbin 150001, China.
Materials (Basel, Switzerland)
|June 13, 2025
概括
本研究引入了一种双模态感知框架,用于强大的机器人轨迹规划,在复杂的条件下提高准确性,使用立体视觉和深度学习进行精确的接识别和路径生成.
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
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 现有的无教学方法在复杂环境中难以处理冗余的点云处理和稳定性.
- 挑战包括精确的接细分和功能点提取用于机器人路径规划.
研究的目的:
- 提出一个新的双模态感知框架,集成2D图像识别和3D点云规划.
- 为了提高机器人轨迹规划的自主性和稳定性,用于接应用.
主要方法:
- 开发了一种改进的U-Net深度学习模型,使用VGG16骨干和双通道注意模块 (DAM) 来进行接细分.
- 利用张苏恩算法进行接中线提取和多项式拟合用于特征点识别.
- 实施了改进的RANSAC算法和同面积珠填充策略,用于槽特征提取和轨迹规划.
主要成果:
- 实现了高接细分性能,mIoU为0.887和F1-Score为0.940.
- 获得精确的特征点定位,平均绝对误差 (MAE) 为0.238mm.
- 在复杂的条件下,系统准确度为0.208mm,接宽度波动在±0.15mm以内.
结论:
- 拟议的双模式框架显著提高了机器人轨迹规划的自主性和稳定性.
- 该方法即使在噪声干扰和槽变形的情况下也保持高精度,这对于工业应用至关重要.
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