For internal members only
Workshop on Aerial Physical Interaction - 15 February
Safe and efficient path planning for multiple robots Jin KIM
Abstract: A team of multiple cooperative autonomous robots can better exploit the benefits of robotic deployments by overcoming the physical limits of individual robots. Such potential has driven considerable research on multi-robot systems, and this talk presents developments related with generation of physically feasible trajectories for cooperative operation of multiple agents. Experimental demonstrations using ground and flying robots including aerial exploration and autonomous transportation will also be presented.
Bio: H. Jin Kim is a Professor and Department Chair in Aerospace Engineering at Seoul National University. received MSc and PhD degrees in Mechanical Engineering from the University of California, Berkeley and BS in Mechanical Engineering from Korea Advanced Institute of Science and Technology (KAIST), Korea. Her research is on navigation, control and path planning of autonomous robotic systems, such as ground robots, autonomous vehicles, and flying robots. She has served as an (Associate) Editor for several journals and conferences including IEEE Transactions on Robotics, Mechatronics, an International Journal of IFAC, and IEEE Conference on Robotics and Automation, and International Journal of Control, Automation, and Systems. She has been selected as one of the leading researchers for 100 future technologies in Korea and a member of National Academy of Engineering of Korea.
Frugality in robotics: a 10-year journey - Jacques GANGLOFF
Abstract: Doing better with less is a major source of inspiration in robotics. At ICube Strasbourg, we started working in this direction in 2013 with cable-driven parallel robotics (CDPR). More recently, following this path has seamlessly led us to aerial robotics. Our approach to aerial manipulation retains an important feature inherited from CDPR: elastic suspension. With the dextAIR robot, we managed to transform a major drawback of this class of robot, the elasticity of the cables, into a benefit. After this brief history, the presentation will focus on more recent developments in aerial robotics, more specifically developments of the ANR STRAD and TIR4STREET ongoing research programs.
Bio: Jacques Gangloff graduated from the Ecole Normale Supérieure de Cachan in 1995. He received the M.S. and Ph.D. degrees in robotics from the University Louis Pasteur, Strasbourg, France, in 1996 and 1999, respectively. Between 1999 and 2005, he was an associate professor at the University of Strasbourg. He is currently a full professor at the same University and a member of the Robotics, Data Science and Healthcare Technologies team at the ICube laboratory. His research interests include visual servoing, predictive control, medical robotics, cable driven parallel robotics and, more recently, aerial manipulation. He has authored or coauthored over 130 publications and received numerous awards. More on his personal webpage: https://rdh.icube.unistra.fr/index.php/Page_personnelle_de_Jacques_Gang…
Tradeoff maneuverability and power consumption with a novel morphing omnidirectional multirotor: the OmniMorph Antonio FRANCHI
Abstract: In this seminar I will introduce the design, modeling, and control of a novel morphing multi-rotor Unmanned Aerial Vehicle (UAV) that we call the OmniMorph. The morphing ability allows the selection of the configuration that optimizes energy consumption while ensuring the needed maneuverability for the required task. The most energy-efficient uni-directional thrust (UDT) configuration can be used, e.g., during standard point-to-point displacements. Fully-actuated (FA) and omnidirectional (OD) configurations can be instead used for full pose tracking, such as, e.g., constant attitude horizontal motions and full rotations on the spot, and for full wrench 6D interaction control and 6D disturbance rejection. Morphing is obtained using a single servomotor, allowing possible minimization of weight, costs, and maintenance complexity. The actuation properties are studied, and an optimal controller that compromises between performance and control effort is proposed and validated in realistic simulations. Preliminary tests on the prototype are presented to assess the propellers’ mutual aerodynamic interference.
Bio: Antonio Franchi holds a joint appointment as Full Professor in Aerial Robotics Control at the University of Twente (EEMCS Faculty, RAM department), Enschede, The Netherlands, and Full Professor at the Sapienza University of Rome (DIAG department), Rome, Italy. He is an IEEE Fellow. In the past he has been a Permanent Researcher at CNRS and a Senior Research Scientist at the Max Planck Institute for Biological Cybernetics in Germany. He received the master degree (summa cum laude) in Electronic Engineering and the Ph.D. degree in System Engineering from Sapienza University of Rome, Italy. He received the French HDR degree (Accreditation to Supervise Research) from the National Polytechnic Institute of Toulouse. He has been a visiting student at the University of California at Santa Barbara. His main research interests lie in the robotics area, with a special regard to control and estimation problems and applications ranging across motion and physical interaction control, decentralized control/estimation/coordination, haptics, and hardware/software architectures. His main areas of expertise are aerial robotics and multiple-robot systems. He published more than 160 papers in international journals, books, and conferences and gave more than 90 invited talks in international venues. He was awarded with the IEEE RAS ICYA Best Paper Award for one of his works on Multi-robot Exploration. In 2018 he was a recipient of the 2018 IEEE RAS Most Active Technical Committee Award. One the PhD theses he supervised has been the recipient of the award for the Best Robotics PhD Thesis in France in 2019. He is currently Senior Editor of the International Journal of Robotics Research.
He has been Associate Editor of the IEEE Transactions on Robotics and Senior Editor for IEEE ICRA, associate editor of the IEEE Robotics & Automation Magazine, IEEE ICRA, IEEE/RSJ IROS and the IEEE Aerospace and Electric Systems Magazine. He is the co-founder and emeritus co-chair of the IEEE RAS Technical Committee on Multiple Robot Systems, http://multirobotsystems.org/.
He has been PI in several projects focused on aerial robots and multi-robots, such as, e.g., the EU Horizon AutoAssess project, the EU H2020 Aerial-CORE project, the EU H2020 AEROARMS project, the ANR PRC ‘The Flying Co-worker’ and the JCJC ANR MuRoPhen projet.
Sustainability Robotics - Mirko KOVAC
Abstract: Environmental sciences rely heavily on accurate, timely and complete data sets which are often collected manually at significant risks and costs. Robotics and mobile sensor networks can collect data more effectively and with higher spatial-temporal resolution compared to manual methods while benefiting from expanded operational envelopes and added data collection capabilities. In future, robotics and AI will be an indispensable tool for data collection in complex environments, enabling the digitalisation of forests, lakes, off-shore energy systems, cities and the polar environment. However, such future robot solutions will need to operate more flexibly, robustly and efficiently than they do today. This talk will present how animal-inspired robot design methods can integrate adaptive morphologies, functional materials and energy-efficient locomotion principles to enable this new class of environmental robotics. The talk will also include application examples, such as flying robots that can place sensors in forests, aerial-aquatic drones for autonomous water sampling, drones for aerial construction and repair, and impact-resilient drones for safe operations in underground and tunnel systems.
Bio: Prof. Mirko Kovac is director of the Aerial Robotics Laboratory and full professor at Imperial College London. He is also heading the Laboratory of Sustainability Robotics at the Swiss Federal Laboratories for Materials Science and Technology (Empa) in Zürich. His research group focusses on the development of novel mobile robots for distributed sensing and autonomous manufacturing in complex natural environments. Prof. Kovac's particular specialisation is in robot design, hardware development and multi-modal sensor mobility. Before his appointment in London, he was post-doctoral researcher at Harvard University and he obtained his PhD at the Swiss Federal Institute of Technology in Lausanne (EPFL). He received his undergraduate degree in Mechanical Engineering from the Swiss Federal Institute of Technology in Zurich (ETHZ) in 2005. Since 2006, he has presented his work in more than 100 peer reviewed publications in leading conferences and journals, has won several best paper awards and has delivered over 100 keynote and invited lectures. He also regularly acts as advisor to government, investment funds and industry on robotics opportunities.
Gaussian Process Regression for modeling and control of physical systems - Ruggero CARLI
Abstract: In the last decades, Machine Learning (ML) and Deep Learning (DL) algorithms proved promising solutions to solve complex problems, ranging from modeling, classification, regression, and control. These algorithms are particularly effective in settings where a large amount of data is available, such as virtual environments. As an example, DL algorithms reach super-human performance in playing Chess, Shogi, and Go. On the contrary,when dealing with physical systems, the number of samples available is limited, possibly compromising the effectiveness of these technologies. This motivates the interest in data-efficient ML and DL algorithms for physical systems, also known as physics-informed models. These algorithms aim at limiting interaction time on the actual system by exploiting prior knowledge on the underlying dynamics. In this talk, we will present a class of physics-informed solutions based on Gaussian Process Regression (GPR). The first part of the talk will discuss the application of GPR to inverse dynamics identification. Instead, in the last part, we will present MC-PILCO, a data-efficient Reinforcement Learning algorithm based on GRP.
Bio: Ruggero Carli (Member, IEEE) received the Laurea degree in computer engineering and the Ph.D. degree in information engineering from the University of Padova, Padua, Italy, in 2004 and 2007, respectively. From 2008 to 2010, he was a Postdoctoral Fellow with the Department of Mechanical Engineering, University of California, Santa Barbara. He is currently an Associate Professor with the Department of Information Engineering, University of Padova. His research interests include distributed optimisation, estimation and control, nonparametric estimation, and learning-based control for robotic systems.