Building a CableBot and Learning the Dynamics Model and the Controller

Controlling cable driven master slave robots is a challenging task. Fast and precise motion planning requires stabilizing struts which are disruptive elements in robot-assisted surgeries. In this work, we study parallel kinematics with an active deceleration mechanism that does not require any hindering struts for stabilization. 

Reinforcement learning is used to learn control gains and model parameters which allow for fast and precise robot motions without overshooting. The developed mechanical design as well as the controller optimization framework through learning can improve the motion and tracking performance of many widely used cable-driven master slave robots in surgical robotics.

Project Consortium

• Montanuniversität Leoben

Related Work

H Yuan, E Courteille, D Deblaise (2015). Static and dynamic stiffness analyses of cable-driven parallel robots with non-negligible cable mass and elasticity, Mechanism and Machine Theory, 2015 – Elsevier, link.

MA Khosravi, HD Taghirad (2011). Dynamic analysis and control of cable driven robots with elastic cables, Transactions of the Canadian Society for Mechanical Engineering 35.4 (2011): 543-557, link.

Publications

2019

Rueckert, Elmar; Jauer, Philipp; Derksen, Alexander; Schweikard, Achim Dynamic Control Strategies for Cable-Driven Master Slave Robots Proceedings Article In: Keck, Tobias (Ed.): Proceedings on Minimally Invasive Surgery, Luebeck, Germany, 2019, (January 24-25, 2019). Links | BibTeX @inproceedings{Rueckert2019c, title = {Dynamic Control Strategies for Cable-Driven Master Slave Robots}, author = {Elmar Rueckert and Philipp Jauer and Alexander Derksen and Achim Schweikard}, editor = {Tobias Keck}, doi = {10.18416/MIC.2019.1901007}, year = {2019}, date = {2019-01-24}, booktitle = {Proceedings on Minimally Invasive Surgery, Luebeck, Germany}, note = {January 24-25, 2019}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close doi:10.18416/MIC.2019.1901007 Close

Neuartige Robotertechnologien und künstliche Lernmethoden können Schlüsseltechnologien sein, um  unsere Umwelt zu schützen. Prof. Dr. Elmar Rückert und Herr Ole Pein haben diese Seite ins Leben gerufen, um diese Thematik gemeinsam mit Schülerinnen und Schülern des Carl-Jacob-Burckhardt-Gymnasium in Lübeck zu untersuchen.

Das Projekt wird innerhalb des Wahlpflichtunterrichts am Carl-Jacob-Burckhardt-Gymnasium  in der 8. und 9. Klassenstufe verwirklicht. In der 8. Klasse lernen die Schülerinnen und Schüler Lego-Mindstorms-EV3-Roboter zu konstruieren und zu programmieren. 

Das Projekt basiert auf unserer frei verfügbaren Python Software für LEGO EV3s. Es wird kontinuierlich von einem Team der Universität Lübeck weiterentwickelt und an die Bedürfnisse und Fragestellungen der Schülerinnen und Schüler angepasst.

Das Projekt mit dem Titel „Autonome Elektrofahrzeuge als urbane Lieferanten“ wird im Rahmen des Programms „Our Common Future“ von der Robert Bosch Stiftung gefördert.

Link: https://future.ai-lab.science

This project aims to develop a new paradigm to build open-ended learning robots called `Goal-based Open ended Autonomous Learning’ (GOAL). GOAL rests upon two key insights. First, to exhibit an autonomous open-ended learning process, robots should be able to self-generate goals, and hence tasks to practice. Second, new learning algorithms can leverage self-generated goals to dramatically accelerate skill learning. The new paradigm will allow robots to acquire a large repertoire of flexible skills in conditions unforeseeable at design time with little human intervention, and then to exploit these skills to efficiently solve new user-defined tasks with no/little additional learning.

Link: http://www.goal-robots.eu