协作式人机交互工具现场标定的无监督优化方法

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI:10.1109/MFI49285.2020.9235229

Bruno Maric, Marsela Polic, Tomislav Tabak, M. Orsag

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引用次数: 8

摘要

在这项工作中，我们提出了一个基于动作捕捉系统的直观工具，用于机器人操作中的演示任务编程。针对任意外部测量系统的工作环境，提出了一种基于无监督学习和单纯形优化的在线标定方法。利用Nelder-Mead单纯形法根据运动捕捉系统记录标定机器人工具和环境的刚性变换。通过使用迭代聚类和离群值检测过程的数据集子采样，实现快速优化过程。在线校准可以实时定制和执行演示任务的编程。

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Unsupervised optimization approach to in situ calibration of collaborative human-robot interaction tools

In this work we are proposing an intuitive tool based on motion capture system for programming by demonstration tasks in robot manipulation. For a robot manipulator set in a working environment equipped with any external measurement sys-tem, we propose an online calibration method based on unsupervised learning and simplex optimization. Without loos of generality the Nelder-Mead simplex method is used to calibrate the rigid transforms of the robot tools and environment based on motion capture system recordings. Fast optimization procedure is enabled through dataset subsampling using iterative clustering and outlier detection procedure. The online calibration enables customization and execution of programming by demonstration tasks in real time.

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来源期刊

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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