用高斯路径模型库实现人教机器人运动的直观编程
Gaussian path model library for intuitive robot motion programming by demonstration
- 从示范轨迹生成多种形状的高斯路径模型
- 支持通过几何分析修改已有路径模型
- 适合需要快速编程机器人的工业场景
本文提出一种从表示路径形态的教学数据中生成高斯路径模型的系统，并介绍利用这些路径模型对人类示范路径进行分类的方法。通过构建包含多种形状的高斯路径模型库，可实现基于人类示范的直观机器人运动编程。此外，还提出一种通过几何分析，基于示范对现有高斯路径模型进行修改的方法。
原文摘要 · Abstract (English)
This paper presents a system for generating Gaussian path models from teaching data representing the path shape. In addition, methods for using these path models to classify human demonstrations of paths are introduced. By generating a library of multiple Gaussian path models of various shapes, human demonstrations can be used for intuitive robot motion programming. A method for modifying existing Gaussian path models by demonstration through geometric analysis is also presented.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。