AUTHORS: Alexandre Ambiehl, Sébastien Garnier, Kévin Subrin
New method for decoupling the articular stiffness identification: Application to an industrial robot with double encoding system on its 3 first axis
In order to be able to perform complex and arduous tasks, stiffness articular identification of industrial robots is a current approach to predict the deflection under static or dynamic loading. Manufacturers propose new features to take the loading into account and a new generation of industrial robot equiped with double encoding systems are proposed. However, current methods brings some drawbacks when the ratio between the stiffness arm and the wrist one is too high. In this paper, we propose a new approach to take this aspect into account by decoupling the arm identification and the wrist one. We compare then our method regarding two current methods and applied it on this new industrial robot. The results highlight the stability and the quality of the stiffness articular estimation with and without activating the double encoding system. On our data, we are able to take into account 84% of the global deflection.
24-28 Sept. 2017Go to complete version
AUTHORS: E. Ozturk, A. Barrios, C. Sun, S. Rajabi, J. Muñoa
Robotic Assisted Milling for Increased Productivity
Robots’ role in machining is growing as they are being used as machining platforms in increasing number of applications. Moreover, robots have an important role in a new application called, robotic assisted milling, where a robot provides additional support to a workpiece when actual machining is performed by a machine tool. In the paper, alternative methods of support with the robot, i.e. fixed and mobile support are explained. Experimental results show that the robot’s support improves the static and dynamic response of the process. Hence, dimensional errors are minimized and surface quality is improved.
26 April 2018
AUTHORS: Lisa Gutzeit, Alexander Fabisch, Marc Otto, Jan Hendrik Metzen, Jonas Hansen, Frank Kirchner, Elsa Andrea Kirchner
The BesMan Learning Platform for Automated Robot Skill Learning
We describe the BesMan learning platform which allows learning robotic manipulation behavior. It is a stand-alone solution which can be combined with different robotic systems and applications. Behavior that is adaptive to task changes and different target platforms can be learned to solve unforeseen challenges and tasks, which can occur during deployment of a robot. The learning platform is composed of components that deal with preprocessing of human demonstrations, segmenting the demonstrated behavior into basic building blocks, imitation, refinement by means of reinforcement learning, and generalization to related tasks. The core components are evaluated in an empirical study with 10 participants with respect to automation level and time requirements. We show that most of the required steps for transferring skills from humans to robots can be automated and all steps can be performed in reasonable time allowing to apply the learning platform on demand.
31 May 2018Go to complete version