This study focused on the improvement of an adaptive orbital welding system, applying a laser-based vision sensor, with the objective of increasing its reliability and robustness by proposing a calibration for the manipulator’s rail deformations, and also developing novel methods and algorithms based on neural network techniques for the adaptive parameters setup. The main problem with mobile-based robotic welding systems that use computer vision for trajectory control arises from the offset between the sensing point and the welding torch, in conjunction with the geometric deformations of the mounted rail. This configuration in moving base manipulators generates an error on the online trajectory correction. In this context, methods of verification and calibration of this systematic error intrinsic to the geometric composition are necessary. Welding tests were conducted in order to validate the proposed methods, for both rail form calibration and adaptive welding parameters setup. The enhanced system was capable of overcoming significant geometrical variations of the gap and pipe mismatch. A previous scan was used to calibrate the rail deformation, allowing for single laser line projection sensors to be utilized for this purpose. The proposed methods are restricted to not only welding operations of large-diameter pipes, but also for general welding applications and possible inspection operations as well.

本研究的重点是改进自适应轨道焊接系统，应用基于激光的视觉传感器，目的是通过提出对机械手轨道变形的校准来提高其可靠性和鲁棒性，并开发基于神经网络的新方法和算法。自适应参数设置的网络技术。使用计算机视觉进行轨迹控制的基于移动的机器人焊接系统的主要问题来自传感点和焊枪之间的偏移，以及安装导轨的几何变形。移动基座机械手中的这种配置会在在线轨迹校正上产生误差。在这种情况下，需要验证和校准几何组成固有的系统误差的方法。进行了焊接测试以验证所提出的方法，用于轨道形状校准和自适应焊接参数设置。增强型系统能够克服间隙和管道不匹配的显着几何变化。之前的扫描用于校准轨道变形，允许使用单个激光线投影传感器用于此目的。所提出的方法不仅限于大直径管道的焊接操作，而且也适用于一般焊接应用和可能的检查操作。之前的扫描用于校准轨道变形，允许使用单个激光线投影传感器用于此目的。所提出的方法不仅限于大直径管道的焊接操作，而且也适用于一般焊接应用和可能的检查操作。之前的扫描用于校准轨道变形，允许使用单个激光线投影传感器用于此目的。所提出的方法不仅限于大直径管道的焊接操作，而且也适用于一般焊接应用和可能的检查操作。