【摘要】 为进一步提高工业机器人的定位精度，提出一种分级补偿的方法以降低几何和非几何因素引起的定位误差。使用遗传算法优化最小二乘法(GA-LS)进行几何参数误差辨识并补偿到机器人运动学模型中，再通过梯度提升树(GBDT)算法对残余非几何参数误差进行预测，并对残余误差进行补偿，最后以UR10机器人为研究对象进行了实验，验证该方法的准确性。实验结果表明：此分级补偿方法能有效提高机器人的绝对定位精度，补偿后机器人的平均定位误差由2.381 mm降低至0.156 mm,定位精度提升了93.4%;均方根定位误差由2.417 mm降低至0.163 mm,定位精度提升了93.2%。实验结果验证了此分级补偿方法的有效性。更多还原

【Abstract】 In order to further improve the positioning accuracy of industrial robots, a graded compensation method was proposed to reduce the positioning error caused by geometric and non-geometric factors.The genetic algorithm optimized least squares method(GA-LS) was used to identify the geometric parameter errors, and then the geometric parameter errors were compensated it into the robot kinematics model.Then the gradient boosting decision tree(GBDT) model was used to predict and compensate the residual non-geometric parameter errors, and finally the UR10 robot was used as the research object for experiments to verify the accuracy of the method.The experimental results show that the graded compensation method can effectively improve the absolute positioning accuracy of the robot, and the average positioning error of the robot is reduced from 2.381 mm to 0.156 mm after compensation, the positioning accuracy is increased by 93.4%,the root mean square positioning error is reduced from 2.417 mm to 0.163 mm, and the positioning accuracy is improved by 93.2%.The effectiveness of the graded compensation method is verified by the experimental results.更多还原