LAUSR

The primary goal of this project is to develop a general identification method via software that can be applied to collaborative robots. To achieve this, collaborative ultralight robots Kinova Gen2 and Kuka LWR4+ with seven degrees of freedom (M-DOF) were used. Specifically, the "recursive Newton-Euler" formulation was used to provide a set of parameters that could describe the body structure and to create a general symbolic representation for collaborative robots. For parameter estimation "Least-Squares" method was used. Besides, trajectories generated with random numbers cannot give very consistent results; hence, verified trajectories were used. To verify the trajectories, real robots were simulated with V-Rep before being executed. Because when untested trajectories are first tested on robots, undesirable results may occur. Therefore, the created trajectories were first simulated with V-Rep, and the chance of damaging the robot was eliminated. After being satisfied with the simulation results and being sure that any trajectories may be harmful to the robots, the trajectories were executed on the real robot. This method not only provides great convenience in terms of parameter estimation, robot health, and saving time, but also increased the consistency of our results. During the execution of real robots, data were collected by ROS in realtime. The algorithms have been coded via Matlab, and ROS packages via Python. Matlab, ROS, V-Rep were work together in harmony under Ubuntu operating system. The identification methods were modeled, implemented, tested, and validated successfully. The results for both robots are given in this article.