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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.