AEROSPACE ENGINEERING

Abstract

Many autonomous systems benefit from prolonged operational time and efficient operations in long-duration space exploration missions, such as Mars rover exploration and human missions to Mars. Due to limited propellants, dynamic operating environments, complex system behaviors, and strict mission constraints, it is challenging to realize full autonomy with capabilities of sustained power supply or fuel-efficient operations. Without human intervention, real-time decision-making, including both motion planning and logic/reasoning decisions, plays a critical role in assuring the reliability and performance of such a system toward mission success. This talk will present our work on developing sophisticated modeling approaches, scalable optimization algorithms, and machine learning based optimal control method that collectively contribute to advanced decision-making strategies for efficient and sustainable autonomous systems in space exploration missions. The discussion will highlight applications in two distinct types of autonomous systems. This first involves space vehicles engineering for Mars entry, powered descent, and landing missions where onboard propellant is limited and high precision landing is required. The second focuses on solar-powered rover that harvests energy from the environment and charges the storage batteries as a backup to realize sustainable operations. The seminar will articulate our overarching goal: to achieve a high level of autonomy for these systems, enabling them to navigate dynamic environments, complex operational scenarios, and stringent mission constraints effectively.

Speaker

Dr. Ran Dai

associate professor in the School of Aeronautics and Astronautics at Purdue University. Before joining Purdue, she was the Netjets Assistant Professor at The Ohio State University. She received her B.S. degree in Automation Science from Beihang University and her M.S. and Ph.D. degrees in Aerospace Engineering from Auburn University. After graduation, she worked as an engineer in an automotive technology company, Dynamic Research, Inc., and then joined the University of Washington as a postdoctoral fellow. Dr. Daiʼs research focuses on control of autonomous systems, numerical optimization, and networked dynamical systems. She is an associate fellow of AIAA and has received the NSF Career Award and NASA Early Faculty Career Award. Dr. Dai is serving as an associate editor of the IEEE Transactions on Aerospace and Electronic Systems and the conference editorial board of the IEEE Control Systems Society.