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Published in Texas ScholarWorks, 2021
Near-earth space is geopolitically and commercially contested, and in need of environmental protection. To achieve space safety, security, and sustainability, we are developing ASTRIAGraph, a framework that enables monitoring, assessment, and verification of space actor behavior in the context of legal and policy instruments.
Recommended citation: http://dx.doi.org/10.26153/tsw/11754
Published in AIAA Journal of Guidance, Control, and Dynamics, 2023
ONE of the challenges for flying quadrotors in cluttered envi-ronments is to optimize their trajectories subject to collision avoidance constraints in real time. Along such a trajectory, the position of the quadrotor must stay within a set of collision-free corridors. Each corridor is a bounded convex flight space.
Recommended citation: https://doi.org/10.2514/1.G007218
Published in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
Proposed a new formulation of the robotic assembly sequencing problem as a Markov Decision Process. Then showed how a class of methods called Graph Exploration Assembly Planners (GEAPs) can be used to gather optimal assembly sequences from a graph. We then showcased a deep reinforcement learning extension for handling very complex structures, all while handling diverse constraints.
Recommended citation: https://doi.org/10.1109/IROS58592.2024.10802475
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Graduate Class, University of California, Mechanical Engineering, 2024
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