Control of Underactuated Systems from Theory to Practice
Krzysztof Kozlowski, Institute of Automation and Robotics, Poznan University of Technology, Poland
Collective and Individual Decision-Making in Swarm Robotics
Sanaz Mostaghim, Computer Science, Otto-von-Guericke-Universität Magdeburg, Germany
Multi-sensorial Environment Perception in Urban Environment
Csaba Benedek, SZTAKI, Institute for Computer Science and Control, Hungary
How to Address Uncertainty in Smaller, Faster, More Agile, Yet Safer Drones?
Erdal Kayacan, Aarhus University, Denmark
Control of Underactuated Systems from Theory to Practice
Krzysztof Kozlowski
Institute of Automation and Robotics, Poznan University of Technology
Poland
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Brief Bio
Professor K. Kozlowski received the M.S. degree in electrical engineering from Poznan University of Technology (PUT), Poland; and the Ph.D. degree in control engineering from PUT in 1979, where he is currently a full professor of robotics and automation. He joined the IEEE in 1983 and has been a Senior Member since 1988. He is the author of a book titled Modelling and Identification in Robotics (Springer-Verlag, 1998). He was and is on the editorial boards of several Polish and international journals (e.g. IEEE Robotics and Automation Magazine, IEEE Transactions on Control Systems Technology, Journal of Applied Mathematics and Computer Science, Journal of Intelligent Robotic Systems, Control Society Conference Editorial Board). He has served every year since 1995 on the ICRA Program Committee. He has worked as a program chair of the International Conference on Advanced Robotics (ICAR), 2001, International Workshop on Robot Motion and Control (RoMoCo) since 2001, and vice chairman of the IEEE Conference on Models in Automation and Robotics (MMAR), since 2001. In 2001 he was elected as a Chairman of the IEEE Robotics and Automation Chapter in Poland. The IEEE Robotics and Automation Society, Poland Section Chapter, was granted 2001 Chapter of the Year Award for the first time of history of the IEEE R$A. In 2014 he was reelected as chair of the IEEE Robotics and Automation Chapter in Poland. He is a member of the Control Systems Society Chapter in Poland. He has served as an RAS Administrative Committee member from 2000 till 2002 and 2004 till 2005. He served IEEE Control Systems Society as a member of Board of Governors in 2002 and 2003. He is also a member of EUROMECH, European Mechanics Society, since 1994 and GAMM, Geselschaft fur Angewandte Mathematik und Mechanik, since 1992. His control interest is in control of nonholonomic systems, multi-agent systems and application of robotic systems in particular in medicine and rehabilitation.
Abstract
In this paper we consider a class of mechanical systems that consists of two or three links acting on a vertical plane as planar manipulators with deficiency of control signals equal to one. In case of two link planar manipulators one can consider two classical systems such as Acrobot and Pendubot in the vertical position. In case of three link robot there are three possible single joints that are not actuated and two of them actuated. Controllability and stabilization in upright position of such systems is a main goal considered in this presentation. Using tools rooted in differential geometry maximal linearizing part is analyzed. It will be proved that zero dynamics is stable. Extensive simulation tests are run and comparative analysis will be carried out. Some experimental work will be discussed. Another important aim of this presentation is to show limitations and constraints that are present in implementation of formal differential geometry tools to linearize mechanical systems widely used in practical applications.
Collective and Individual Decision-Making in Swarm Robotics
Sanaz Mostaghim
Computer Science, Otto-von-Guericke-Universität Magdeburg
Germany
Brief Bio
Sanaz Mostaghim is a full professor of computer science at the chair of Computational Intelligence and the founder and head of SwarmLab at the Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany. Sanaz holds a PhD degree (2004) in electrical engineering from the University of Paderborn, Germany, has worked as a postdoctoral fellow at ETH Zurich in Switzerland and as a lecturer at Karlsruhe Institute of Technology (KIT), Germany, where she received her habilitation degree in applied computer science. Her research interests are in the area of multi-criteria optimization and decision-making, evolutionary computation, collective learning and decision-making, and their applications in robotics and science. Sanaz is a member of Saxon Academy of Sciences, the vice president of the IEEE Computational Intelligence Society (CIS), IEEE CIS distinguished lecturer, deputy chair of German Informatics and member of several advisory boards. She is an associate editor of IEEE Transaction on Evolutionary Computation as well as member of the editorial board of several international journals on AI. Sanaz has been appointed as a member of advisory board at the Ministry of Infrastructure and Digitalization, State Saxony-Anhalt, Germany.
Abstract
Autonomous systems are becoming more and more ubiquitous and their influence on our lives grows every day. In the last years, computational intelligence methods have – more than ever - extensively contributed to the latest scientific breakthrough in developing such intelligent systems. Nevertheless, one major challenge concerns the real-time reactions of autonomous systems to the unknown dynamics in their environments which is considered to be among the grand challenges in this area. This talk is about multi-objective decision making algorithms in Swarm Robotics. It will give an overview about the design issues and the challenges in real-time applications in robotics and computer games. In most of such applications, the decision makers (robots or agents) must find and select one possible optimal solution in a very limited time frame. This is very challenging, when the environment dynamically changes as the decision maker needs to re-optimize and decide on the fly. Multi-objective decision making algorithms in dynamically changing environments will be addressed and applications in Swarm Robotics will be presented. The results on individual and collective decision making are represented.
Multi-sensorial Environment Perception in Urban Environment
Csaba Benedek
SZTAKI, Institute for Computer Science and Control
Hungary
www.sztaki.hu
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Brief Bio
Dr. Csaba Benedek a scientific advisor with the Machine Perception Research Laboratory of the Institute for Computer Science and Control (SZTAKI) in Budapest, where he is the head of the Research Group on Geo-Information Computing. He also works as a full professor with the Faculty of Information Technology and Bionics of the Péter Pázmány Catholic University. Dr. Benedek is the past president of the Hungarian Image Processing and Pattern Recognition Society (Képaf), and the Hungarian Governing Board Member of the International Association for Pattern Recognition (IAPR). He is a Senior Member of IEEE, an Associate Editor of Digital Signal Processing (Elsevier) and a Guest Editor of the Remote Sensing (MDPI) journals. He received various awards including the Bolyai plaquette from the Hungarian Academy of Sciences (HAS) 2019, Researcher Acknowledgement from the HAS Secretary-General (2018), Imreh Csanád plaquette (2019), and the Michelberger Master Award from the Hungarian Academy of Engineering (2020). He has been the manager of various national and international research projects in the recent years. His research interests include Bayesian image and point cloud segmentation, object extraction, change detection, machine learning applications and GIS data analysis.
Abstract
In the past decade we have witnessed an explosion of new technologies for acquisition and understanding of environmental information. 3D vision systems of self-driving vehicles (SDV) can be used for -apart from safe navigation- real time mapping of the environment, detecting and analyzing static (traffic signs, power lines, vegetation, street furniture), and dynamic (traffic flow, crowd gathering, unusual events) scene elements. On the other hand, new generation geo-information systems (GIS) store extremely detailed 3D maps about the cities, consisting of dense 3D point clouds, registered camera images and semantic metadata.
In this talk, I present new techniques to facilitate the joint exploitation of the measurements of car mounted online sensing platforms, and offline 3D environmental data obtained by Mobile Laser Scanning (MLS) technology in urban environment. First I introduce a 3D convolutional neural network (CNN) based method to segment dense MLS point clouds into nine different semantic classes, which can be used for high definition city map generation. In the next part, a Lidar based real time and accurate self-localization approach is presented for SDVs in high resolution 3D point cloud maps of the environment obtained through Mobile Laser Scanning (MLS). Finally, we propose an end-to-end, automatic, online camera- Lidar calibration approach, for application in self driving vehicle navigation
How to Address Uncertainty in Smaller, Faster, More Agile, Yet Safer Drones?
Erdal Kayacan
Aarhus University
Denmark
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Brief Bio
Erdal Kayacan received a Ph.D. degree in electrical and electronic engineering at Bogazici University, Istanbul, Turkey in 2011. After finishing his post-doctoral research in KU Leuven at the division of mechatronics, biostatistics and sensors (MeBioS) in 2014, he worked in Nanyang Technological University, Singapore at the School of Mechanical and Aerospace Engineering as an assistant professor for four years. Currently, he is pursuing his research at Aarhus University at the Department of Engineering as an associate professor. He has since published more than 110 peer-refereed book chapters, journal and conference papers in model-based and model-free control, parameter and state estimation, and their robotics applications. He has completed a number of research projects which have focused on the design and development of ground and aerial robotic systems, vision-based control techniques and artificial intelligence. Dr. Kayacan is co-writer of a course book “Fuzzy Neural Networks for Real Time Control Applications, 1st Edition Concepts, Modeling and Algorithms for Fast Learning”. He is a Senior Member of Institute of Electrical and Electronics Engineers (IEEE). Since 1st Jan 2017, he is an Associate Editor of IEEE Transactions on Fuzzy Systems and IEEE Transactions on Mechatronics.
Abstract
Request for increased, almost perfect, accuracy and efficiency of aerial robots pushes the operation to the boundaries of the performance envelope and, thus, induces a need for reliable operation at the very limits of attainable performance. The use of advanced learning algorithms, which can learn the operational dynamics online and adjust the operational parameters accordingly, might be a candidate solution to all the aforementioned problems. This talk will focus both model-based and model-free learning methods to handle various real-time aerial robot control problems. Furthermore, due to the cost associated with data collection and training, the topics related to approaches such as transfer learning will also be mentioned to transfer knowledge between aerial robots and thereby increase the efficiency of their control. Not but not the least, some state-of-the-art drone applications, e.g. autonomous drone racing and fully autonomous cinematography system for aerial drones with the aim of letting the onboard artificial intelligence completely take over the film directing, will also be elaborated.