Presented By: Michigan Engineering
Aerospace Chairs Distinguished Speaker Series
Resource-centric and intelligent autonomy for aerospace systems
Sarah H.Q. Li
Ph.D. Candidate, University of Washington
Join in FXB or on Zoom at https://umich.zoom.us/j/98754209693
Abstract:
As the scale of autonomous operation grows in aerospace, an individual aircraft or spacecraft's autonomous performance will be crucially impacted by the competition for resources and the interaction uncertainty between vehicles. Combining Markov decision process, game theory, and optimization, my research aims to intelligently coordinate multi-vehicle trajectory planning. In this talk, I will introduce the Markov decision process congestion game to resolve congestion in air traffic management and ride-hail. Next, building on robust dynamic programming, I will analyze how uncoordinated vehicle interactions impact the learning dynamics of an individual vehicle, and derive Hausdorff distance convergence results for diverging value iteration. I will end by discussing some upcoming challenges in aerospace and my approaches to tackling them.
Speaker Bio:
Sarah H.Q. Li is a Ph.D. candidate in Aeronautics and Astronautics Engineering at the University of Washington and has received her B.A.Sc. in Engineering Physics from the University of British Columbia. Her research combines game theory, stochastic control, and optimization to enable large-scale autonomous interactions in disruption-prone environments such as urban air traffic and orbital spaces. She is a 2020 Zonta International Amelia Earhart Fellow and a 2022 UW Aero&Astro Condit Fellow.
Ph.D. Candidate, University of Washington
Join in FXB or on Zoom at https://umich.zoom.us/j/98754209693
Abstract:
As the scale of autonomous operation grows in aerospace, an individual aircraft or spacecraft's autonomous performance will be crucially impacted by the competition for resources and the interaction uncertainty between vehicles. Combining Markov decision process, game theory, and optimization, my research aims to intelligently coordinate multi-vehicle trajectory planning. In this talk, I will introduce the Markov decision process congestion game to resolve congestion in air traffic management and ride-hail. Next, building on robust dynamic programming, I will analyze how uncoordinated vehicle interactions impact the learning dynamics of an individual vehicle, and derive Hausdorff distance convergence results for diverging value iteration. I will end by discussing some upcoming challenges in aerospace and my approaches to tackling them.
Speaker Bio:
Sarah H.Q. Li is a Ph.D. candidate in Aeronautics and Astronautics Engineering at the University of Washington and has received her B.A.Sc. in Engineering Physics from the University of British Columbia. Her research combines game theory, stochastic control, and optimization to enable large-scale autonomous interactions in disruption-prone environments such as urban air traffic and orbital spaces. She is a 2020 Zonta International Amelia Earhart Fellow and a 2022 UW Aero&Astro Condit Fellow.
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