基于粒子群优化神经网络的机器人精度补偿方法研究

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针对工业机器人应用于飞机柔性化自动装配时绝对定位精度不能满足装配精度的问题,在机器人空间网格精度补偿方法的基础上,综合考虑环境温度的变化对机器人的绝对定位精度的影响,提出了基于神经网络的机器人综合精度补偿方法。为了防止神经网络在训练中陷入局部极值,采用粒子群优化方法对它的初始权值和阈值进行了优化。实验结果表明，当温度在20~30℃范围内变化时,机器人的绝对定位误差由补偿前的1~3mm,提高到补偿后的绝对定位误差最大值为0.32mm,平均值为0.194mm,精度较未补偿前有了大幅提高,可以满足飞机自动化装配的高精度的要求。
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Method of Industrial Robot Accuracy Compensation Based on Particle Swarm Optimization Neural Network

For the absolute positioning accuracy of industrial robots used in aircraft flexible automated assembly cannot meet assembly precision,a neural network-based integrated accuracy compensation approach taking into account ambient temperature change factor was proposed based on robot spatial grid accuracy compensation method.Neural network's initial weights and thresholds were optimized by using particle swarm optimization method in order to prevent from falling into local minima in training.The experimental  results show that the maximum value of the robot positioning error is as  0.32mm, and the mean value is as0.19mm with the temperature in the range of 20℃ to 30℃,which are much more better than the previous values 1~3mm,the absolute positioning accuracy can satisfy the requirements of aircraft automatic assembly.