在应用机器人对车身点焊质量检测过程中，其焊点的定位精度受到点焊作业质量以及车身制造误差等因素的影响，导致实际焊点与设计值并不重合。针对传统示教无法对焊点定位进行实时补偿的问题，提出基于双目视觉引导机器人的焊点定位策略，并构建基于改进粒子群算法优化的支持向量机回归误差补偿模型，对定位结果进行补偿。在机器人末端安装双目传感器，利用双目定位原理对焊点进行初步定位，并将焊点位置的测量数据与实际数据作为学习样本，利用训练好的误差补偿模型预测系统定位误差，将补偿结果作为纠偏值引导机器人定位焊点。实验结果表明，补偿后的定位精度得到较大提升，验证了该方法的有效性。

In the process of applying robots to car body spot welding quality inspection, the positioning accuracy of its welding spots was affected by factors such as quality of spot welding operations and car body manufacturing errors, which resulted in actual welding spots did not coincide with designed values. Aiming at the problem that traditional teaching could not perform real-time compensation for welding spot positioning, a welding spots positioning strategy based on binocular vision guided robots was proposed, and a support vector machine regression error compensation model optimized based on improved particle swarm algorithm was constructed to compensate the positioning results. The binocular sensors were installed at the end of the robot, the binocular positioning principle was used to initially locate welding spots, and the measured data and actual data of welding spots position was used as learning samples. The trained error compensation model was used to predict positioning error of the system, and the compensation results were used as the correction value guiding the robot to locate welding spots. The experimental results show that the positioning accuracy after compensation is greatly improved, which verifies the effectiveness of the method.