Plenary Lecture

Plenary Lecture

(Related to Focused Track 2: Walking Robots)
Humanoid Robotics Research in AIST and open issues in RoboticsDr. Shuuji Kajita
National Institute of Advanced Industrial Science and Technology (AIST), Japan

Oct. 14 (Wednesday) 15:10~16:10

I will present the humanoid robotics research conducted in AIST during the last two decades, and discuss open issues of robotics based on our experience. One of our key turning points was the 2015 DARPA Robotics Challenge finals (DRC finals) in the US, which was the competition for the robotics researchers from all over the world.Although the result of our team was 10th, we learned important lessons from the competition. As one of the outcomes obtained from the lessons of DRC finals, I will explain the Spatially Quantized Dynamics, which is a novel framework to realize robust knee-stretched biped walking. Finally, I would like to discuss unsolved problems of robotics with the audience.

Bio: TBA

(Related to Focused Track 1: Network and Multi-agent Systems)
Network Systems, Kuramoto Oscillators, and Synchronous Power FlowProf.혻 Francesco Bullo
University of California, Santa Barbara, USA

Oct. 15 (Thursday) 09:10~10:10

Network systems are mathematical models for the study of cooperation, propagation, synchronization and other dynamical phenomena that arise among interconnected agents. Network systems are widespread in science and technology as fundamental modeling tools.This talk will review established and emerging frameworks for modeling, analysis and design of network systems. Topics will include prototypical models, algebraic graph theory, and contractivity theory.혻 Next, I will focus on recent developments on the analysis of security and transmission capacity in power grids. I will review the Kuramoto model of coupled oscillators and present recent results on its synchronization and multi-stability behavior. Applications to synchronous power flow problems will be discussed.

Bio: Francesco Bullo is a Professor with the Mechanical Engineering Department and the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He served on the editorial boards of IEEE, SIAM, and ESAIM journals and as IEEE CSS President. His research interests focus on network systems and distributed control with application to robotic coordination, power grids and social networks. He is the coauthor of 쏥eometric Control of Mechanical Systems (Springer, 2004), 쏡istributed Control of Robotic Networks (Princeton, 2009), and 쏬ectures on Network Systems (Kindle Direct Publishing, 2020, v1.4). He received best paper awards for his work in IEEE Control Systems, Automatica, SIAM Journal on Control and Optimization, IEEE Transactions on Circuits and Systems, and IEEE Transactions on Control of Network Systems. He is a Fellow of IEEE, IFAC, and SIAM.

(Related to Focused Track 3: Automotive Control)
The optimization and estimation needs for the transition to electrified transportationProf. Anna Stefanopoulou
University of Michigan, USA

Oct. 16 (Friday) 09:10~10:10

Replacing a gasoline or diesel internal combustion engine vehicle (ICEV) with an electric vehicle (EV) will zero out the tailpipe emissions and depending on where it charges and where it was manufactured can have 50% lower green-house gas emissions. Key barrier to EV adoption is the higher up-front cost of EVs compared to ICEVs. The total cost of ownership depends on the lower operating cost due to fuel and maintenance savings that depend on the route, utilization, and charging of the vehicle. If the battery stays healthy, vehicle to building, vehicle to grid, and other 2nd life applications can provide additional value streams and considered as an optimization problem.

The battery state of health (SOH) is thus at the crux of the payback calculations and consequently can tip the EV adoption. Unfortunately, estimating the battery health with high confidence can only be done under certain discharge patterns. Identifying the physical origin of the degradation is even more difficult but very important, because it can inform the battery management system and hence prevent further degradation. We will present the range of on-board signals for tracking the intrinsic 쏿ging wrinkles in batteries. We will conclude by highlighting the estimation of gas venting and assess the intensity of thermal runaway for first-responders and post-accident management of damaged batteries.

Bio: TBA