High accurate absolute robot positioning is a requirement, and still a challenge, in many applications, such as drilling in the aerospace industry. The accuracy is affected due to many sources of errors from robot model, tool calibration, sensor and product uncertainties. While model-based error compensation cannot reach the desired accuracy, sensor-based compensation appears as the practical solution to increase the robot positioning accuracy. A structured analysis of the error sources in robotic manufacturing processes can facilitate error identification and further compensation. This paper describes an error source breaking down approach for analyzing robotic manufacturing processes. Moreover, an external sensor-based compensation is proposed for error reduction and error identification. Comparison with a compliance model-based compensation is performed. The proposed approach is applied to a robotic drilling process for aircraft manufacturing, considered a general and real industrial application. Further validation through experimentation is performed. The validation revealed a clear improvement in robot positioning accuracy and the benefits of the proposed error source structure for analysis.

In many robotic processes such as milling and drilling, there are multiple solutions for robot poses, as the rotation around the tool axis remains as a degree of freedom (DoF) in positioning. Yet until now, this DoF causes additional efforts in CAM programming as it requires manual intervention. Instead, this DoF can be used to optimize the robot pose according to different criteria of the robot such as stiffness or avoidance of backlash effects. This paper presents different criteria for optimization of the robot pose in machining and describes the optimization of robot stiffness based on a novel method for its identification, which is in detail described on this paper. Furthermore, the potential of the automatic resolution of the DoF is outlined enabling staff without robot knowledge to define reasonable robot paths.