The e-Bike motor assembly: Towards advanced robotic manipulation for flexible manufacturing

• Published in: Robotics and computer-integrated manufacturing Vol. 85; p. 102637
• Main Authors: Rozo, Leonel, Kupcsik, Andras G., Schillinger, Philipp, Guo, Meng, Krug, Robert, van Duijkeren, Niels, Spies, Markus, Kesper, Patrick, Hoppe, Sabrina, Ziesche, Hanna, Bürger, Mathias, Arras, Kai O.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2024
• Subjects: Flexible manufacturing; Learning from demonstration; Optimal control; Robotic manipulation; Task planning; Learning from demonstration; Robotic manipulation; Optimal control; Task planning; Flexible manufacturing
• ISSN: 0736-5845; 1879-2537
• DOI: 10.1016/j.rcim.2023.102637

Robotic manipulation is currently undergoing a profound paradigm shift due to the increasing needs for flexible manufacturing systems, and at the same time, because of the advances in enabling technologies such as sensing, learning, optimization, and hardware. This demands for robots that can observe and reason about their workspace, and that are skillfull enough to complete various assembly processes in weakly-structured settings. Moreover, it remains a great challenge to enable operators for teaching robots on-site, while managing the inherent complexity of perception, control, motion planning and reaction to unexpected situations. Motivated by real-world industrial applications, this paper demonstrates the potential of such a paradigm shift in robotics on the industrial case of an e-Bike motor assembly. The paper presents a concept for teaching and programming adaptive robots on-site and demonstrates their potential for the named applications. The framework includes: (i) a method to teach perception systems onsite in a self-supervised manner, (ii) a general representation of object-centric motion skills and force-sensitive assembly skills, both learned from demonstration, (iii) a sequencing approach that exploits a human-designed plan to perform complex tasks, and (iv) a system solution for adapting and optimizing skills online. The aforementioned components are interfaced through a four-layer software architecture that makes our framework a functional industrial technology. To demonstrate the generality of the proposed framework, we provide, in addition to the motivating e-Bike motor assembly, a further case study on dense box packing for logistics automation. •We show the potential of advanced learning methods on the e-Bike motor assembly.•Our framework includes a self-supervised method to teach perception systems onsite.•We exploit general representations of object-centric motion and force-aware skills.•We provide a sequencing method that exploits a human-designed plan for complex tasks.•We test our framework in the e-Bike motor assembly and the box packing for logistics.