针对六自由度串联机器人误差模型的综合辨识，智能选择和优化测量位姿

2016 23rd International Conference on Mechatronics and Machine Vision in Practice (M2VIP) Pub Date : 2016-11-01 DOI:10.1109/M2VIP.2016.7827279

Xiaoyan Chen, Qiuju Zhang, Yilin Sun

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引用次数: 3

摘要

对主要由关节柔度引起的非几何误差和几何误差进行识别和补偿，以提高精度。提出了一种包含几何参数和柔度参数的综合误差模型。提出了一种基于干扰检测和线性降权粒子群算法的测量姿态智能选择与优化方法。在六自由度串联工业机器人上的仿真结果表明，采用最优测量位姿可以显著提高标定精度和测量效率。

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Intelligent selection and optimization of measurement poses for a comprehensive error model identification of 6-DOF serial robot

Non-geometric errors mainly caused by the joint compliance should be identified and compensated as well as geometric errors to improve the accuracy. This paper presents a new comprehensive error model consisting of both geometric and compliance parameters. A new approach is proposed for intelligent selection and optimization of measurement poses based on interference detection method and linearly decreasing weight particle swarm optimization (LinWPSO) algorithm. Simulation results on a 6-DOF serial industrial robot demonstrate that using the optimal measurement poses can significantly improve the calibration accuracy and measurement efficiency.

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2016 23rd International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

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