Keynote Speakers

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry

Wednesday, August 20th, 2025

Abstract of Keynote

The evolution of Industry 4.0 and the emergence of Industry 5.0 are reshaping human-robot collaboration (HRC) in industrial settings. Ensuring operator safety, ergonomic well-being, and production efficiency remains a critical challenge for modern manufacturing and logistics systems. This talk provides an overview of advanced control and optimization techniques designed to enhance safety, ergonomics, and operational performance in collaborative robotics and drone-assisted automation. As a key application, an optimization-based methodology for time-optimal and safety-compliant trajectory planning of robot manipulators is introduced, with a focus on ergonomic considerations and adherence to safety standards. Additionally, novel control frameworks for human-drone interaction in industrial warehouses are discussed, where drones assist in pick-and-delivery tasks while dynamically adjusting their trajectories to the movements of human operators. The effectiveness of these approaches is demonstrated through realistic scenarios, providing a path toward more efficient and safer HRC in future industrial environments.

Short Bio

Mariagrazia Dotoli is a Full Professor in Automation at Politecnico di Bari, Italy, where she is also the Founder and Coordinator of the Italian National PhD Program on Autonomous Systems. She is the founder and director (2012-) of the Decision&Control Laboratory of Politecnico di Bari and the founder (2012) of Politecnico di Bari spin-off company Innolab S.r.l. She authored 300+ international publications in automation. Her h-index in Google Scholar equals 48, with 8000+ citations. Prof. Dotoli is listed in the world top 2% scientists list for career-long impact and single-year categories in the “Industrial Engineering & Automation” and “Artificial Intelligence & Image Processing” fields in the author database by Ioannidis et al. released by Stanford University and Elsevier BV. Prof. Dotoli is an IEEE Fellow and serves as VP for Membership&Student Activities of IEEE SMCS and AdCom member of IEEE RAS. She is a Senior Editor of the IEEE Transactions on Automation Science and Engineering and an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics: Systems. She was the General chair of the 2024 IEEE Conference on Automation Science and Engineering (CASE2024) and is the General co-Chair of 2025 IFAC workshop. on Smart Energy Systems for Efficient and Sustainable Smart Grids and Smart Cities (SENSYS2025). More information about Mariagrazia Dotoli can be found at http://dclab.poliba.it/people/mariagrazia-dotoli/

Mariagrazia Dotoli

Full Professor in Systems and Control Engineering at Politecnico di Bari, Department of Electrical and Information Engineering


Advances in Autonomy for Robotic Exploration of Mars

Monday, August 18th, 2025

Abstract of Keynote

Spacecraft on the surface of Mars do most of their communication with Earth through relay orbiters, which only affords a few communication windows per day. All communication windows are affected by the speed of light latency between Earth and Mars that can be up to about 40 minutes each way. These factors require that robotic vehicles on Mars operate with a high degree of autonomy. Autonomous navigation behaviors began 30 years ago with simple obstacle avoidance and odometry; this has gradually progressed to include more sophisticated obstacle perception, path planning, and rover localization techniques. Rovers execute a large number of activities during each day, which historically were carefully planned on Earth to respect all applicable constraints, then transmitted to the rovers for execution. Various contingencies during execution could lead to premature termination of the sequence or other suboptimal time allocation. Recently, some sequence planning capability has been moved onboard to achieve more efficient operations. This talk will give an overview of the evolution of Mars rover autonomy and give a glimpse at our ambitions for the next two decades of Mars exploration. This may include significant increases in onboard computing capability, which would enable far more capable onboard autonomy.

Short Bio

TBD

Larry Matthies

Senior Research Scientist at JPL in the Mobility and Robotic Systems Section


Latent Low-Dimensional Predictor Analytics for Engineering Applications

Tuesday, August 19th, 2025

Abstract of Keynote

Modern engineering and scientific systems often utilize numerous sensors to gather high-dimensional time series data for monitoring and operations. The success of DeepSeek for large language models highlights the effectiveness of low dimensional learning, particularly when computational resources and data volume are limited. This talk introduces a latent low dimensional dynamic predictor framework that concurrently achieves dimension reduction and optimal dynamic prediction. The dynamic latent variables, termed principal predictors, form low dimensional parsimonious predictor models for high-dimensional time series data. The solution process involves iterations to extract both dynamic and static subspaces. A maximum likelihood framework is employed to develop an iterative solution. The connection between principal predictors and DeepSeek low-dimensional approximation is explored. Examples from engineering and industrial manufacturing processes will be used to demonstrate the advantages of the proposed framework. This low-dimensional dynamic modeling approach has potential applications in prediction, control, and anomaly diagnosis.

Short Bio

Joe Qin is the Wai Kee Kau Chair Professor of Data and President of Lingnan University in Hong Kong. He obtained his B.S. and M.S. degrees in Automatic Control from Tsinghua University in Beijing and his Ph.D. degree in Chemical Engineering from University of Maryland at College Park. Qin’s research interests include data science and analytics, statistical and machine learning, industrial AI, process monitoring, model predictive control, system identification, smart manufacturing, smart cities, and smart energy management. Dr. Qin is a member of the European Academy of Sciences and Arts and Fellow of the Hong Kong Academy of Engineering Sciences, the U.S. National Academy of Inventors, IFAC, AIChE, and IEEE. He is the recipient of the 2022 AIChE CAST Computing Award, 2022 IEEE CSS Transition to Practice Award, the U.S. NSF CAREER Award. His h-indices for Web of Science, SCOPUS, and Google Scholar are 69, 76, and 88, respectively.

S. Joe Qin

Wai Kee Kau Chair Professor of Data Science and President of Lingnan University in Hong Kong


Quality Feedback Control: A New Paradigm of In-Process Quality Improvement in Smart and Autonomous Manufacturing Systems

Monday, August 18th, 2025

Abstract of Keynote

This presentation will introduce a new concept of “Quality Feedback Control” for In-Process Quality Improvement (IPQI). IPQI emphasizes active defect mitigation in smart and autonomous manufacturing systems, and “Quality Feedback Control” is an enabling technology to achieve the IPQI. Different from the conventional automation that typically uses differential or difference equations with feedback control of the machine output status, the “Quality Feedback Control” paradigm directly uses the product quality measurement as the feedback information to manipulate the inputs of machine(s) that impact on the product quality. Due to the heterogeneous nature of quality data (e.g. multichannel functional curves, high resolution images, high speed videos, or 3D scanning data with millions of unstructured point clouds, etc.) and associated diverse data acquisition strategies, a set of fundamental issues need to be addressed to innovatively model the quality outputs with the control inputs, and further use this model to develop effective control strategies. This keynote talk will lay out the foundations for this important paradigm and discuss research opportunities, challenges, and advancements in “Quality Feedback Control”, with emphasis on how machine learning and quality feedback control have reshaped the landscape of IPQI. Examples of ongoing research projects will be used to illustrate and exemplify the frontiers of this research area. All examples come from real data and real industrial production systems.

Short Bio

Jianjun Shi is the Carolyn J. Stewart Chair and Professor at Georgia Institute of Technology. His research interests focus on data fusion for quality improvements, with emphasis on integration of system informatics, advanced statistics and machine learning, and control theory for the design and operational improvements in advanced manufacturing applications. He is a member of the National Academy of Engineering (NAE), an Academician of the International Academy for Quality, and a Fellow of ASME, IISE, INFORMs, and SME. He served as Editor-in-Chief of the IISE Transactions (2017-2020), the flagship journal of the Institute of Industrial and Systems Engineers. More information about Jianjun Shi can be found at https://sites.gatech.edu/jianjun-shi/

Jianjun Shi

Carolyn J. Stewart Chair and Professor in H. Milton Stewart School of Industrial and Systems Engineering, with joint appointment in the George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology.

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