基于时空混合图卷积网络的机器人定位误差预测及补偿方法

工业机器人作为智能制造的重要载体，在大范围复杂任务中具有巨大潜力。但是，定位精度低且难以控制的问题阻碍了机器人在高精度任务的进一步推广。为了提升机器人作业精度，该文提出一种基于时空混合图卷积网络的机器人定位误差预测及补偿方法。首先通过设计图关系编码模块、时空混合特征解码模块，构建基于图卷积网络的机器人位姿误差预测模型；然后，针对传统迭代补偿方法中机器人逆解次数多导致效率低的问题，该文将定位误差补偿问题转化为优化问题，并利用遗传算法同时对位置和姿态进行误差补偿；最后，通过拉丁超立方抽样方法获得训练集，实现机器人定位误差预测模型的训练，并通过实验验证了定位误差预测的准确性以及补偿的效果。

Prediction and Compensation Method of Robot Positioning Error Based on Spatio-temporal Graph Convolution Neural Network

As an important carrier of intelligent manufacturing, industrial robot has great potential in large-scale and complex tasks. However, the problem of low positioning accuracy and difficulty to control hinders the further popularization of robots in high-precision tasks. In order to improve the accuracy of robot operation, a robot positioning error prediction and compensation method based on spatio-temporal convolution graph network is proposed in this work. Firstly, through the design of graph relation coding module and spatio-temporal feature decoding module, the prediction model of the robot position and orientation error based on graph convolution network is constructed; Then, to solve the problem of low efficiency caused by too many times of robotic inverse kinematics solution in traditional iteration compensation methods, the problem of compensation for positioning errors is transformed into optimization problem, and the genetic algorithm is used to compensate the position and attitude errors simultaneously; Finally, the training set is obtained by Latin hypercube sampling method to realize the training of robot positioning error prediction model, and the accuracy of positioning error prediction and the effect of compensation are verified by the experiments.