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This article describes an algorithm for the online extrapolation of hand-motion during remote welding. The aim is to overcome the spatial limitations of the human welder’s arms in order to cover a larger workspace with a continuous weld seam and to substantially relieve the welder from strain and fatigue. Depending on the sampled hand-motion data, an extrapolation of the given motion patterns is achieved by decomposing the input signals in a linear direction and a periodic motion component. An approach to efficiently determine the periodicity using a sampled autocorrelation function and the subsequent application of parameter identification using a spline function are presented in this paper. The proposed approach is able to resemble all practically relevant motion patterns and has been validated successfully on a remote welding system with limited input space and audio-visual feedback by an experienced welder.
Lucas Ebel; Jochen Maaß; Patrick Zuther; Shahram Sheikhi. Trajectory Extrapolation for Manual Robot Remote Welding. Robotics 2021, 10, 77 .
AMA StyleLucas Ebel, Jochen Maaß, Patrick Zuther, Shahram Sheikhi. Trajectory Extrapolation for Manual Robot Remote Welding. Robotics. 2021; 10 (2):77.
Chicago/Turabian StyleLucas Ebel; Jochen Maaß; Patrick Zuther; Shahram Sheikhi. 2021. "Trajectory Extrapolation for Manual Robot Remote Welding." Robotics 10, no. 2: 77.
This article covers the signal processing for a human–robot remote controlled welding application. For this purpose, a test and evaluation system is under development. It allows a skilled worker to weld in real time without being exposed to the associated physical stress and hazards. The torch movement of the welder in typical welding tasks is recorded by a stereoscopic sensor system. Due to a mismatch between the speed of the acquisition and the query rate for data by the robot control system, a prediction has to be developed. It should generate a suitable tool trajectory from the acquired data, which has to be a C 2 -continuous function. For this purpose, based on a frequency analysis, a Kalman-Filter in combination with a disturbance observer is applied. It reproduces the hand movement with sufficient accuracy and lag-free. The required algorithm is put under test on a real-time operating system based on Linux and Preempt_RT in connection to a KRC4 robot controller. By using this setup, the welding results in a plane are of good quality and the robot movement coincides with the manual movement sufficiently.
Lucas Christoph Ebel; Patrick Zuther; Jochen Maass; Shahram Sheikhi. Motion Signal Processing for a Remote Gas Metal Arc Welding Application. Robotics 2020, 9, 30 .
AMA StyleLucas Christoph Ebel, Patrick Zuther, Jochen Maass, Shahram Sheikhi. Motion Signal Processing for a Remote Gas Metal Arc Welding Application. Robotics. 2020; 9 (2):30.
Chicago/Turabian StyleLucas Christoph Ebel; Patrick Zuther; Jochen Maass; Shahram Sheikhi. 2020. "Motion Signal Processing for a Remote Gas Metal Arc Welding Application." Robotics 9, no. 2: 30.
Implementing digitalization in the field of production represents a major hurdle for some small- and medium-sized enterprises (SMEs) due to the ensuing demands on employees and, in some cases, the significant financial investment required. The RobReLas research project has developed a system whose purpose is to enable an economical solution to this dilemma for SMEs in the field of automated, robot-based reconditioning of components. A laser scanner was integrated in the robot’s control. The data generated by the scanner are used to mathematically describe the virtual area of the surface to be laser-treated. The scanner recognizes the relevant area within the robot’s predefined work space by defining the maximum length and width of the relevant component. The system then automatically applies predefined and qualified repair strategies in the virtual area. Tests on nickel-based blades demonstrated the system’s economic potential, showing a reduction in reconditioning time of about 70% compared to the conventional reconditioning method. The main advantage of the system is the fact that a basic knowledge of operating robots is sufficient for the attainment of repeatable results. Further, no additional CAD/CAM workstations are required for implementation.
Shahram Sheikhi; Eduard Mayer; Jochen Maaß; Florian Wagner. Automated Reconditioning of Thin Wall Structures Using Robot-Based Laser Powder Coating. Sustainability 2020, 12, 1477 .
AMA StyleShahram Sheikhi, Eduard Mayer, Jochen Maaß, Florian Wagner. Automated Reconditioning of Thin Wall Structures Using Robot-Based Laser Powder Coating. Sustainability. 2020; 12 (4):1477.
Chicago/Turabian StyleShahram Sheikhi; Eduard Mayer; Jochen Maaß; Florian Wagner. 2020. "Automated Reconditioning of Thin Wall Structures Using Robot-Based Laser Powder Coating." Sustainability 12, no. 4: 1477.