IET-CSR Workshop on Advances in CPS & Robotics

IET-CSR Workshop on Advances in CPS & Robotics
Nov. 28, 2019, Hangzhou, Zhejiang University
Gongkong New Building (工控新楼) Room 105 Yuquan Campus, Zhejiang University
Contact: Chao Xu/许超, 13706711953

10:10 a.m. – 10:15 a.m.
Welcome address
10:15 a.m. – 10:30 a.m.
Group photo

10:30 a.m. – 11:10 a.m., Chaired by Chao Xu
Prof. Petros G. Voulgaris, UIUC
1. The input-output approach for social optimization problems: Uncovering decentralized and selfish optimality
11:10 a.m. – 11:50 a.m., Chaired by Chao Xu
Prof. Nick Freris, USTC
2. Learning in Cyberphysical Systems
11:50 a.m. – 1:30 p.m.

1:30 p.m. – 2:10 p.m., Chaired by Petros G. Voulgaris
Prof. Mark Butala, ZJUI
3. High-Dimensional State Estimation for Space Science
2:10 p.m. – 2:50 p.m., Chaired by Petros G. Voulgaris
Prof. Jufeng Wu, ZJU
4. Boolean Gossip Networks
2:50 p.m. – 3:10 p.m.
Tea break

3:10 p.m. – 3:50 p.m., Chaired by Nick Freris
Prof. Liangjing Yang, ZJUI
5. Robotics and Computer Vision for Smart Surgical and Biomedical Applications
3:50 p.m. – 4:30 p.m., Chaired by Nick Freris
Prof. Shiyu Zhao, Westlake University
6. Bearing Rigidity Theory and its Applications for Control and Estimation of Network Systems

4:30 p.m. – 5:10 p.m., Chaired by Liangjing Yang
Prof. Fei Gao, ZJU
7. Robust Perception and Autonomous Navigation for Aerial Robots in Complex Environments

5:10 p.m. –
Tea break and free discussion, dinner

1) Prof. Petros G. Voulgaris, UIUC
The input-output approach for social optimization problems: Uncovering decentralized and selfish optimality

Abstract: We present an input-output approach to enhance the study of cooperative multiagent optimization problems that admit decentralized and selfish solutions, hence eliminating the need for an interagent communication network. The framework under investigation is the one used in standard Mean Field games: a set of N independent agents coupled only through an overall cost that penalizes the divergence of each agent from the average collective behavior. This type of social optimization problems can be related to a variety of applications including stock market, production engineering, dynamic demand management, population dynamics, pricing, consensus, swarm connectivity, etc. We show that optimal decentralized and selfish solutions are possible in a variety of standard input-output cost (norm) criteria, in some cases regardless of the population size N, and in some others asymptotically as N grows large.

Bio: Professor Petros G. Voulgaris received the Diploma in Mechanical Engineering from the National Technical University, Athens, Greece, in 1986, and the S.M. and Ph.D. degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 1988 and 1991, respectively. Since 1991, he has been with the Department of Aerospace Engineering, University of Illinois where he is currently a Professor (also appointments with the Coordinated Science Laboratory, and the department of Electrical and Computer Engineering.) His research interests include optimal, robust and distributed control and estimation; networked control; applications of advanced control methods to engineering practice including, power systems, air-vehicle, nano-scale, robotic, and structural control systems. Dr. Voulgaris is a recipient of several awards including the NSF Research Initiation Award, the ONR Young Investigator Award and the UIUC Xerox Award for research. He has also been a Visiting ADGAS Chair Professor, Mechanical Engineering, Petroleum Institute, Abu Dhabi, UAE (2008-10). His research has been supported by several agencies including NSF, ONR, AFOSR, NASA, and Boeing. He is also a Fellow of IEEE.

2) Prof. Nick Freris, USTC
Learning in Cyberphysical Systems

Abstract: Cyberphysical Systems (CPS) are very large networks of “smart” devices (possessing sensing, communication, and computation capabilities), that control physical entities. Notable examples enlist Smart Cities, Smart Grids, Intelligent Transportation, Sensor Networks, and Swarm Robotics. This keynote talk will comprise two parts: In the first part, we will present two methods for real-time learning in CPS: a) Sparse Kernel Density Estimation (S-KDE), with application in online estimation of travel time densities in transportation systems, and b) Sparse Matrix Decomposition (S-MD), applied to online detection and localization of forced oscillations in smart grids. The second part will highlight the fundamental balance between data transformation and data utility in machine learning. In Big Data applications, a key challenge lies in the fact that the data are hardly ever available in their original form, e.g., due to compression, anonymization, encryption, or right protection. We will showcase methods for exact learning from inexact data, with provable fidelity guarantees, in specific: a) Optimal distance estimation of compressed data series b) Nearest Neighbor preserving watermarking c) Cluster preserving compression, and d) Distributed consensus on encrypted data.

Bio: Nick Freris is Professor in the School of Computer Science and Technology at USTC, and Vice Dean of the International College. He received the Diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece, in 2005, and the M.S. degree in Electrical and Computer Engineering, the M.S. degree in Mathematics, and the Ph.D. degree in Electrical and Computer Engineering all from the University of Illinois at Urbana-Champaign (UIUC) in 2007, 2008, and 2010, respectively. His research lies in Cyberphysical Systems: machine learning, distributed optimization, data mining, wireless networks, control, and signal processing, with applications in sensor networks, transportation, robotics, cyber security, and power systems. Dr. Freris has published 35 papers in high-profile journals and conferences held by IEEE, ACM and SIAM. He holds two patents and three patent applications. His research was recognized with the 1000-Talents award, the USTC Alumni Foundation Innovation Scholar award, and the IBM High Value Patent award. Previously, he was assistant professor at New York University Abu Dhabi (NYUAD) and global network assistant professor at NYU Tandon School of Engineering (NYU-Tandon), from 2014 to 2018. From 2012 to 2014, he was senior researcher at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and from 2010 to 2012, he was postdoctoral researcher at IBM Research – Zurich, Switzerland. Dr. Freris is a Senior Member of IEEE, and a member of ACM and SIAM.

3) Prof. Mark Butala, ZJUI
High-Dimensional State Estimation for Space Science

Abstract: The accelerating volume of information generated from ever less expensive sensing and processing technology is transforming all disciplines concerned with empirical determination. The space science community, with its ever expanding fleet of spacecraft and ground instrumentation, is now positioned to take advantage of the “big data” revolution. In this seminar, I describe how data assimilation, the systematic Bayesian inference of a high-dimensional state given a huge data volume and physical constraints on system dynamics, yields a deeper scientific characterization for the potentially destructive space weather phenomena facing our increasingly technological society. Attention is given to Kalman filtering and system identification methodological foundations and computational challenges faced when confronted with enormous data volumes.

Bio: Dr. Butala earned his Ph.D. degree in 2010 from the University of Illinois at Urbana-Champaign from the Department of Electrical and Computer Engineering. He then joined the technical staff at the NASA Jet Propulsion Laboratory, California Institute of Technology. His contributions have been recognized with NASA Group Achievement Awards including “for outstanding achievement in the operation and successful execution of the Curiosity rover’s mission of exploration to the surface of Gale Crater on Mars.” In September 2017, he joined the faculty of the Zhejiang University / University of Illinois at Urbana-Champaign Institute at the Zhejiang University International Campus in Haining.

4) Prof. Jufeng Wu, ZJU
Boolean Gossip Networks

Abstract: We propose and investigate a Boolean gossip model as a simplified but non-trivial probabilistic Boolean network. With positive node interactions, in view of standard theories from Markov chains, we prove that the node states asymptotically converge to an agreement at a binary random variable, whose distribution is characterized for large-scale networks by mean-field approximation. Using combinatorial analysis, we also successfully count the number of communication classes of the positive Boolean network explicitly in terms of the topology of the underlying interaction graph, where remarkably minor variation in local structures can drastically change the number of network communication classes. With general Boolean interaction rules, emergence of absorbing network Boolean dynamics is shown to be determined by the network structure with necessary and sufficient conditions established regarding when the Boolean gossip process defines absorbing Markov chains. Particularly, it is shown that for the majority of the Boolean interaction rules, except for nine possible nonempty sets of binary Boolean functions, whether the induced chain is absorbing has nothing to do with the topology of the underlying interaction graph, as long as connectivity is assumed.

Bio: Junfeng Wu received the B.Eng. degree from the Department of Automatic Control, Zhejiang University, Hangzhou, China, and the Ph.D. degree in electrical and computer engineering from Hong Kong University of Science and Technology, Hong Kong, in 2009, and 2013, respectively. From September to December 2013, he was a Research Associate in the Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology. From January 2014 to June 2017, he was a Postdoctoral Researcher in the ACCESS (Autonomic Complex Communication nEtworks, Signals and Systems) Linnaeus Center, School of Electrical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden. He is currently with the College of Control Science and Engineering, Zhejiang University, Hangzhou, China. His research interests include networked control systems, state estimation, and wireless sensor networks, multiagent systems. He received the Guan Zhao-Zhi Best Paper Award at the 34th Chinese Control Conference in 2015.

吴均峰,2017年入选国家青年高层次人才类项目,浙江大学控制科学与工程学院“百人计划”特聘研究员,博士生导师。研究领域包括网络控制系统、信息物理融合系统、卡尔曼滤波、状态估计、多智能体系统、分布式优化算法设计与性能分析等。2005年-2017年,吴均峰博士曾先后在浙江大学控制科学与工程系、香港科技大学电子及计算机学系、瑞典皇家工学院(KTH)学习和工作。在网络控制领域围绕状态估计、卡尔曼滤波、多智能体系统等方面取得了一定研究成果。近五年来,先后在IEEE Trans. Automatic Control、Automatica、IEEE Trans. Signal Processing等国际期刊发表和录用论文30余篇。2015年获第34届中国控制会议关肇直奖,2016年获澳大利亚政府Endeavour Research Fellowship,分别获2014-2015年度Automatica与IEEE Trans. Control of Network Systems期刊杰出审稿人称号。曾担任IET Control Theory & Applications期刊专刊客座编辑(Leading Guest Editor)。

5) Prof. Liangjing Yang, ZJUI
Robotics and Computer Vision for Smart Surgical and Biomedical Applications

Abstract: Robotics and Computer Vision have profound impact on the advancement of medicine by equipping modern surgical procedures with unprecedented dexterous, visual and informational augmentations. Minimally invasive surgeries are examples that benefit from innovative imaging and robot-assisted technologies. In the talk, I will present my research that incorporates Robotics and Computer Vision to improve treatment procedures. This research encompasses the registration of pre-operative surgical plans with robotic systems as well as the enhancement of intra-operative images. A self-contained image mapping framework for 3D ultrasound image-guided endoscopic procedures developed to equip surgeons with an intuitive visualization of the vascular network will be presented. This example demonstrates the application of Computer Vision in overcoming the limitations of conventional intra-operative imaging methods. The talk will end with a look at a potential research on interactive robotic systems for surgical simulation and training.

Bio: Liangjing Yang is an assistant professor in Zhejiang University/ University of Illinois at Urbana-Champaign (ZJU-UIUC) Institute. He received the B.Eng. and M.Eng. degrees in mechanical engineering from the National University of Singapore. He obtained the D.Eng. degree from the University of Tokyo before receiving a joint postdoctoral fellowship to work at the Singapore University of Technology and Design, and Massachusetts Institute of Technology. Liangjing’s research interests are in Robotics and Computer Vision primarily focusing on medical applications. His work on image mapping for 3D ultrasound-guided endoscopic procedures is featured in both engineering and medical journals. He also developed a robotic system for overlapping ablation of large liver tumor, which is published in a special issue on “Surgical and Interventional Medical Devices” of ASME/IEEE Transactions on Mechatronics. He holds a US patent on a Robotic Surgical Training System. This development was named “Best Innovation in Biomedical Application” in a challenge organized by National Instrument in 2011. Homepage:

6) Prof. Shiyu Zhao, Westlake University
Bearing Rigidity Theory and its Applications for Control and Estimation of Network Systems

Abstract: This talk will introduce my recent research results on distributed control and estimation over robotic networks. In particular, I will introduce a new bearing-based approach to solve some problems that used to be difficult to solve by conventional approaches. This bearing-based approach, which was originally motivated by vision-based multi-robot swarming, fully explores the critical role of bearing (or called direction) information in multi-robot control and estimation. With this approach, distributed formation control of multiple robots can be achieved merely based on inter-neighbor bearing measurements while distance information is not required. The bearing-based approach can also be applied to sensor network self-localization with bearing-only measurements and provide a simple solution to control the scale of multi-robot formations to avoid obstacles.

Bio: Dr Shiyu Zhao received the BEng and MEng degrees from Beijing University of Aeronautics and Astronautics, China, in 2006 and 2009, respectively. He got the PhD degree in Electrical Engineering from National University of Singapore in 2014. Thereafter, he served as post-doctoral researchers at the Technion – Israel Institute of Technology and University of California at Riverside from 2014 to 2016. He then became a Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield, UK, in 2016. He has joined Westlake University since January 2019 and independently created the Intelligent Unmanned Systems Laboratory.
Dr Zhao has extensive theoretical and practical research experience on unmanned aerial systems. He has published more than 40 research articles in the area of automatic control and robotics. He serves as associate editors for a few international conferences including IEEE IROS, IEEE ICCA, and ICUAS, etc. He was a Regional Chair of IEEE ICCA 2018 and a Program Chair of IEEE ICCA 2019. He is an Associate Editor of the international journal, Unmanned Systems. He is a corecipient of the Best Paper Award (Guan Zhao-Zhi Award) in the 33rd Chinese Control Conference.
赵世钰分别于2006年和2009年在北京航空航天大学获得本科和硕士学位,于2014年在新加坡国立大学获得博士学位。随后他在以色列理工学院和美国加州大学河滨分校各从事了一年的博士后研究,之后在2016年成为英国谢菲尔德大学自动控制与系统工程系的讲师和博士生导师。他于2019年初正式加入西湖大学工学院,并独立创建了西湖大学“智能无人系统实验室”。其主要代表性研究成果为多机器人协同控制与估计,特别是方位刚性理论及其在网络系统中的应用。他目前是多个国际顶级期刊和会议的审稿人,也是一系列国际重要会议的编委(包括IROS2018、ICARCV2018、IROS2019、ICUAS2019),并担任ICCA2018的英国区域主席和ICCA2019的程序委员会主席。此外,他是国际期刊Unmanned Systems的编委。他曾获得领跑者5000——中国精品科技期刊顶尖论文、以及第33届中国控制会议关肇直奖。

7) Prof. Fei Gao, ZJU
Robust Perception and Autonomous Navigation for Aerial Robots in Complex Environments

Abstract: Aerial robots have drawn greatly intentions due to their mobility, agility, and flexibility. In complex environments, the capability to navigate with full autonomy is essential to deploy aerial robots in field and industrial applications. In this talk, I focus on key techniques in robotics autonomous navigation. I will firstly introduce my past research on robotics perception and sensor fusion, which builds the foundation for autonomous robots. Then online motion planning algorithms and system integration will be introduced. Cutting-edge robotics applications and methodologies will be covered in this talk.

Bio: Dr. Fei Gao received his Ph.D. degree from Robotics Institute, Hong Kong University of Science and Technology in 2019. He is currently an assistant professor in Department of Control Science and Engineering, Zhejiang University. Dr. Gao’s research interests cover autonomous navigation, motion planning, perception, SLAM, sensor fusion, aerial robots, field robots, etc.

高飞,博士, 2019年于香港科技大学机器人所获博士学位,随后加入控制学院任特聘副研究员。他的研究兴趣包括机器人、无人机、自主导航、运动规划、环境感知、视觉定位等。博士期间在机器人领域顶级会议、期刊上共发表论文20篇,并获得IEEE SSRR 2016最佳论文,香港科技大学大学工学院2018最佳博士生等荣誉。