Jian S Dai
Professor and Chair of Mechanisms and Robotics
School of Natural and Mathematical Sciences
King’s College London, University of London, UK
Professor Jian S. Dai, CEng, FIMechE, ASME Fellow, is Chair Professor of Mechanisms and Robotics at King’s College London and a pioneer in reconfigurable mechanisms and robots, in origami robots, in ankle rehabilitation robots and in metamorphic robots. He is currently a leading expert in kinematics, mechanisms theory, evolutionary mechanisms and robots and their applications to health, home and manufacture.
Professor Dai received a BEng in 1982 and an MSc in 1984 from Shanghai Jiao Tong University, and received a PhD in Kinematics and Robotics from the University of Salford in the UK in 1993.
Professor Dai is the recipient of 2015 ASME Mechanisms and Robotics Award that is an honor to engineers and scientists who have made a lifelong contribution to the fundamental theory, design and applications of mechanisms and robotic systems. He is the 27th recipient since the award was established in 1974. Professor Dai received many other awards including 2010 Overall Supervisory Excellence Award by King’s College London, 2012 ASME Outstanding Service Award and 2012 Mechanisms Innovation Award, together with several conference and journal Best Paper awards.
Professor Dai has published three books and over 500 peer-reviewed journal and conference papers, with a large number of citations. He has awarded a large number of Research Council grants including EPSRC, EU, NSFC, and industrial grants. He has educated over 25 PhD students with 10 former PhD students working in faculties in ten leading universities in the UK, Mexico, Australia, Italy, UAE and China.
Kinematics Entails Reconfigurable Mechanisms
As reconfigurable mechanisms and robotics become a global trend in developing a bridge between versatile but expensive robots, and efficient but non-flexible machines, kinematics become the essence in this research direction where all developments can be made from the environments and from the requirements by variation of the mechanisms and by reconfiguration of robots and where all reconfigurability can be pre-designed and predicted from an early stage that mimic nature and evoluation.
This plenary speech will present the study of kinematics and screw theories and their relations to Lie groups and Lie algebra through finite screws that lead to development of reconfigurable mechanisms and robots that are entailed by the theoretical study. The intrinsic theory in the kinematics study provides a foundation of development of reconfigurable mechanisms in their various forms, leading to a two-decade innovation in metamorphic mechanisms, robotic devices and parallel robots. With change of the order of a screw system, the mechanism changes its mobility and presents its different topologies.
The plenary speech will further give the vast applications of the reconfigurable mechanisms and robots in assembly, packaging, food industry, domestic robots, rehabilitation and manufacture, and present the current state of the art of reconfigurable mechanisms and robots.
University of Southern Denmark
Maersk Mc-Kinney Moller Institute – SDU-Robotics
Technical Faculty at the University of Southern Denmark
Norbert Krüger has been employed at the University of Southern Denmark since 2006 (first as an Associate Professor and then as a full Professor (MSO) since 2008). Norbert Krüger’s research focuses on Cognitive Vision and Robotics, in particular vision based manipulation, learning and welfare robotics. He has published more than 60 papers in journals (15 of them as first author, most others as last author indicating supervisor involvement) and more than 100 papers at conferences covering the topics computer vision, robotics, neuroscience as well as cognitive systems. His H-index is 31 (google scholar). He is currently involved in one European projects in the area of industrial robotics as well as four Danish projects in industrial and welfare robotics. Norbert Krüger has generated external funding of more than 6.000.000 Euros.
Optimization Robot Equipment in Simulation and 3D Printing to reduce Set-Up times in Industrial Robotics
In my talk, I will present work addressing the problem of efficiently setting-up automated assembly processes with robots. The large set-up times for robot assembly processes that are still required today are the reason for the dominance of manual work in production: only 15% of production is automated today. This then often leads to a move of production into countries with low salaries. The use of robots however could make production more cost-efficient and competitive even if high salaries are paid.
Set-up times of robot systems are dominated by a number of sub-issues: First, often specialized grippers are used for grasping and manipulation that allow for good force control. These grippers often need to be manually designed or refined for particular objects occurring in the assembly process. Second, it is often required to assure that the position and orientation of objects is predetermined with a high degree of precision. This usually requires specific and often rather expensive machinery for precise positioning. Third, robot grasps and trajectories (including appropriate forces) need to be taught in or programmed which often is done through menu-oriented control devices, a quite tedious procedure. Finally, there does not exist yet stable mechanisms for the re-use of action experience. Usually, such experience stays in the brain of the engineer who sets-up the robot solutions.
In my talk I will give an overview about the above mentioned four issues and then in particular focus on the first two problems: The tedious manual design of equipment for robot solutions such as gripper fingers and traps for bowl feeder are a serious obstacle for a cost efficient application of robots for low batch size production. The establishment of robot solutions often requires multiple cycles of generating such pieces of equipment based on prior experience of engineers and long sets of trial and error experiments. This hinders the application of robots in particular in Small and Medium Enterprises, which often do not have such robotic expertise in-house. A cost efficient alternative for such a tedious optimization process is the learning of such equipment in simulation and followed by 3D printing of the learned equipment.
In this talk, I will give two examples for the learning of robot equipment (fingers and traps for bowl feeders) in simulation as well as comparisons between simulation and real world experiments which show sufficient similarity to justify our approach.
At the end of the talk, the relevance of our work in the context of current developments in industrial robotics will be discussed.