Evaluation of ML-Based Grasping Approaches in the Field of Automated Assembly

Current trends in the manufacturing industry lead to high competitive pressure and requirements regarding process autonomy and flexibility in the production environment. Especially in assembly, automation systems are confronted with a high number of variants. Robot-based processes are a powerful tool for addressing these challenges. For this purpose, robots must be made capable of grasping a variety of diverse components, which are often provided in unknown poses. In addition to existing analytical algorithms, empirical ML-based approaches have been developed, which offer great potentials in increasing flexibility. In this paper, the functionalities and potentials of these approaches will be presented and then compared to the requirements from production processes in order to analyze the status quo of ML-based grasping. Functional gaps are identified that still need to be overcome in order to enable the technology for the use in industrial assembly.

A practical implementation of robotic riveting cell for wedge parts of airframe

As there are increasing demands for efficiency and quality in airframe assembly, automation integration is required to replace manual operation. This paper presents a design of a robotic riveting cell for wedge part riveting, which is used on the trailing edge of airfoil. To complete the continuous riveting in a row of rivets at the trailing edge, a robotic riveting cell is designed to realize a set of process actions including rivet feeding/injection/cutting and press riveting. Based on the visual compensation of the hole position, a forceless feedback spiral track rivet injection compensation method is proposed. The stress feedback-based riveting process control method is introduced after rivet injection and cutting. The proposed approach is applied to the robotic riveting cell and the rivet injector works stably. No indentation has been detected using the force-controlled riveting method. The prototype can achieve the riveting task well on the wedge part. The proposed design and approach can be applied on both wedge parts and flat parts. This will give a new way to solve the automation integration problem in airframe assembly. A robotic riveting cell is designed to realize a set of process actions. A forceless feedback spiral track rivet injection compensation method is proposed. The stress feedback-based riveting process control method is employed after rivet injection and cutting.

Development of SCARA Robots

The presentation on SIGMA robot for assembly by A. d’Auria at the 7th International Symposium on Industrial Robots (ISIR) held in Tokyo in October 1977 made an immense impact on engineers studying assembly automation in Japan. The 1970s witnessed the shift from the mass production of a few types to limited production of a wide variety of products in Japan, and research started for a production system with a quick response to a given type of products and change in a quantity of production. Professor Hiroshi Makino of Yamanashi University was stimulated by SIGMA and got an idea for a robot with Selective Compliance Assembly Robot Arms (SCARA) and started working on the design for prototype 1 two months after the presentation. Further, he organized the SCARA Robot Consortium with Yamanashi University and thirteen domestic companies for three years, from April 1978 to March 1981, and had success in the development and spread of the SCARA robot in the assembly work. After the 1980s, the SCARA robot became one of the de facto standards of industrial robots in the world. In 2019, it is estimated that the SCARA robots will compromise 30% or more of industrial robots working all over the world. The author was one member of a research group as an associate professor, in Yamanashi University, and believes that it is extremely effective to discuss the needs for research and development of the SCARA robot and technological solutions thirty years after the establishment of JRM.