工业机器人全域误差场精细化建模方法及其误差补偿策略

doi:10.3901/JME.2024.13.316

摘要/Abstract

摘要： 工业机器人在其全寿命周期内受多源不确定性因素影响，导致末端执行器实际位置与期望位置存在偏差，直接影响所加工产品质量。为此，开展工业机器人误差补偿研究，提出了基于非运动学标定原理的全域精细化误差场建模方法与误差补偿策略，并进行了实验验证，旨在为提升国产工业机器人精度性能提供一种有效工具。以工业机器人末端执行器名义位置为输入节点，相应的定位误差为输出节点训练径向基函数神经网络，结合交叉验证和粒子群优化算法提高训练效率及模型精度，使用较少样本准确构建工业机器人定位误差场。根据误差场预测工业机器人任意点定位误差，通过编辑控制器设定值以增加预偏置的方式实现误差补偿。针对三款额定负载分别为3 kg、12 kg和50 kg的国产工业机器人开展误差补偿实验，相同样本点下所建立误差场与基于误差相似性原理的误差模型相比精度更高，补偿后三款工业机器人绝对定位误差范围分别减小了44.14%、77.48%和80.65%，最大绝对定位误差分别减小了42.55%、76.07%和82.24%，验证了所提方法的工程适用性。

关键词: 工业机器人, 误差补偿, 误差场模型, 径向基函数神经网络, 精度保持性

Abstract: Industrial robots are affected by multiple sources of uncertainty during their whole life cycle, which leads to deviations between the actual and desired end-effector positions, affecting the quality of the products being processed. For this reason, a study on error compensation in industrial robots has been carried out, in which a full-domain refined error field modelling method and error compensation strategy based on the principle of non-kinematic calibration are proposed and experimentally verified. It aims to provide an effective tool for improving the accuracy performance of domestic industrial robots. The radial basis function network is trained with the nominal position of end-effector as input node and the corresponding position error as the output node. Then, the cross-validation and particle swarm optimization algorithms are used to improve the training efficiency and model accuracy. Thus, a position error field of industrial robot in full domain is accurately constructed with few samples. According to the well-developed error field model, the error for any position point of industrial robot can be predicted. After that, error compensation is realized by editing the controller setpoints to increase the pre-bias. The error compensation experiments are conducted for three kinds of domestic industrial robots with rated loads of 3 kg, 12 kg and 50 kg, respectively. The proposed error field model has higher accuracy compared with the error model based on error similarity principle using the same sample points. After compensation, the absolute position error ranges of the experimental industrial robots are reduced by 44.14%, 77.48% and 80.65%, the maximal absolute position error are reduced by 42.55%, 76.07% and 82.24%, verifying the engineering applicability of the proposed method.

Key words: industrial robot, error compensation, error field model, radial basis function neural network, accuracy durability

中图分类号:

TP242

张德权, 李星奥, 张宁, 宁国松, 韩旭. 工业机器人全域误差场精细化建模方法及其误差补偿策略[J]. 机械工程学报, 2024, 60(13): 316-329.

ZHANG Dequan, LI Xingao, ZHANG Ning, NING Guosong, HAN Xu. Refinement Modeling Method of Error Field in Full Domain for Industrial Robot and Its Corresponding Error Compensation Strategy[J]. Journal of Mechanical Engineering, 2024, 60(13): 316-329.

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