CPS Events
Some Thoughts and Ideas on the Quest for Safe Autonomy of Aerospace Vehicles
Abstract
Autonomous aerospace vehicles have applications in diverse fields like space exploration, disaster monitoring, infrastructure inspection, and transportation. However, in spite of several years of research on autonomy of aerospace vehicles, substantial challenges remain in achieving safe and reliable autonomy. The biggest challenges in integrating autonomous aerospace vehicles into human society have to do with dynamic uncertainties due to natural (environmental) effects and safety and reliability in interactions with humans. Recent advances in learning-based and data-enabled control and navigation have made it possible to deal with these challenges to some extent. However, most of these schemes lack nonlinear stability and robustness to external inputs or internal unknowns, which are needed to meet the stringent requirements of safety and reliability for autonomous aerospace vehicles. This talk presents some of my thoughts and ideas on safe autonomous operations of aerospace vehicles, based on how my research has handled these challenges. A key feature of this research is the stress on nonlinear stability and robustness of GNC algorithms for autonomous aerospace vehicles in the presence of actuator constraints, sensor and onboard processor capabilities, and dynamic (time-varying) uncertain inputs or disturbances. Future directions that include learning-based control for autonomous aerospace vehicles are also touched upon.
Speaker Bio
Amit Sanyal obtained the B.Tech. degree in Aerospace Engineering from the Indian Institute of Technology, Kanpur, in 1999. He completed his MS in Aerospace Engineering from Texas A&M University in 2001, where he received the Distinguished Graduate Student Master's Research Award. He obtained his Ph.D. in Aerospace Engineering and MS in Mathematics from the University of Michigan in 2004 and 2005, respectively, and was the recipient of an Engineering Academic Scholar Certificate. After his post-doctoral research at Arizona State University in 2005–2006, he joined the faculty in Mechanical Engineering at the University of Hawaii in 2007. He has been a faculty member in Mechanical and Aerospace Engineering at New Mexico State University (2010–2015), and is currently a faculty member in Mechanical and Aerospace Engineering at Syracuse University. He develops and applies techniques from geometric mechanics, nonlinear and geometric control, and continuous and discrete-time Lagrangian/Hamiltonian systems, to dynamics modeling, guidance, navigation, and control of unmanned and autonomous systems. He is an associate fellow of AIAA, a senior member of IEEE, and a member of ASME and SIAM. His research has been supported by NSF, NASA, and AFOSR, and he co-founded Akrobotix LLC, a robotics start-up company.
Cyber-Physical Systems in Agriculture: Integrating Soft Robotics and Human-in-the-Loop Mechanisms
Abstract
Tree fruit growers worldwide are grappling with labor shortages in crucial operations such as harvesting and pruning. The development of robotic solutions for these labor-intensive tasks has generated significant interest. However, existing efforts have proven to be excessively expensive, slow, or necessitate orchard reconfiguration for functionality.
In this presentation, I introduce three alternative approaches to agriculture robot development: 1) Utilizing a cyber-physical system that facilitates human participation and machine learning for collaborative robotic apple harvesting. 2) Introducing a novel soft-growing multi-robot platform to reduce costs and enhance overall responsiveness. 3) Learning human-robot collaboration behaviors to optimize efficiency.
Leveraging human-robot interaction to address existing individual shortcomings of humans and robots will enable mutual learning of behaviors to enhance overall efficiency.
Speaker Bio
Dr. Ming Luo is currently a Flaherty Assistant Professor in the School of Mechanical and Materials Engineering at Washington State University. He is the director of the Mechanically-Intelligent Autonomous Robotics (MIAR) Laboratory. From 2018 to 2020, he was a postdoctoral scholar in the Department of Mechanical Engineering at Stanford University. He received his Ph.D. in Robotics Engineering from Worcester Polytechnic Institute in 2017. His academic interests include agricultural robotics, soft robotics, snake robots, origami robots, and haptics. His research has been funded by the NSF, USDA, NIFA, and the Washington Tree Fruit Research Commission. His work has also been featured in The Wall Street Journal, IEEE Xplore, Reuters, and The Verge.
A Competitive Analysis Approach to Perimeter Guarding
Abstract
Perimeter guarding studies how mobile defenders intercept intruders before they breach a boundary. Inspired by protecting bases from drones or missiles and shielding crops from predator swarms, the talk reviews recent work on models, limits, and motion algorithms. Using competitive analysis, performance is the competitive ratio: intruders caught by an online strategy versus an omniscient offline one. This input‑agnostic metric avoids restrictive game‑theoretic assumptions. Results begin with a single defender on a line, deriving tight bounds and algorithms with provable guarantees. Extensions cover planar settings and cooperation among multiple homogeneous or heterogeneous vehicles. Numerical experiments validate the theory. The framework offers practical guidance for designing real‑time perimeter defense across diverse threat scenarios. Open questions on sensing delays, stochastic arrivals, and resource constraints will be highlighted.
Bio
Shaunak D. Bopardikar is an Assistant Professor of ECE at Michigan State University and part of MSU’s CANVAS Center for Connected Autonomous Networked Vehicles. His research spans autonomous motion planning and control, cyber‑physical security, and scalable optimization. He has B.Tech./M.Tech. degrees in Mechanical Engineering from IIT Bombay in 2004 and a Ph.D. in Mechanical Engineering from UC Santa Barbara in 2010. His experience includes engineering at GE India Technology Center (2004–05) and a post‑doctoral appointment at UCSB (2010–11), where he developed randomized algorithms for matrix games. From 2011 to 2018, he served as a Staff Research Scientist in the Controls group at United Technologies Research Center, East Hartford, and Berkeley. Bopardikar is a senior IEEE member, author of more than 85 refereed publications, and co‑inventor on two U.S. patents. Honors include a 2021 AFRL Summer Faculty Fellowship, a 2023 NSF CAREER Award, IEEE TCSP Best Student Paper (advisor), and MSU’s 2024 Withrow Excellence in Teaching Award.
Low-Power Language Models
Abstract
This talk explores how to scale neuromorphic computing up to large-scale applications. While training large language models costs in excess of millions of dollars, the human brain does remarkably well on a power budget of 20 watts. We explore the space of neuromorphic algorithms, from spiking neural networks to lightweight language models, and learn how to span the stacks of computation to develop models that are competitive with those developed by companies that are far richer than academic labs.
Bio
Jason is an Assistant Prof with the ECE Department at UC Santa Cruz. His research is focused on brain-inspired computing. He's won a bunch of awards, failed a bunch of other stuff, and pretends like he knows things on the advisory boards of a bunch of companies.
Scalable Marine Robotics: Advancements in Perception, Navigation, and Control for Ocean Exploration
Abstract
The ocean is vital to all living beings but still needs to be explored, leaving many questions unanswered. Fortunately, underwater robotics has greatly improved ocean exploration by accurately mapping the seafloor in high resolution and tracking animals in midwater. However, these platforms are often too expensive to build and operate, limiting ocean exploration and discovery. To address this issue, we need to lower the barriers to entry, particularly for scientific research requiring high-resolution measurements on a large scale. In this talk, I will share some of the collaborative research work that our team, the CoMPAS lab at MBARI, has been involved in addressing precision control, robust navigation, and machine perception based on novel robotics learning techniques. These are just a few examples of how marine robotics research can help close the ocean exploration gap.
Bio
Giancarlo Troni is a Principal Engineer at the Monterey Bay Aquarium Research Institute (MBARI), where he leads robotics research in the CoMPAS Lab, focusing on the perception, estimation, and control of underwater robotics systems. He has a Ph.D. from Johns Hopkins University and has previously served as an Associate Professor with the Department of Mechanical Engineering, Pontificia Universidad Católica de Chile (PUC), Santiago, Chile. Currently, Giancarlo is dedicated to developing better tools and methods for small, low-cost autonomous machines that can scale ocean exploration.
