Title:    Resilient Navigation and Perception to Increase Autonomy in Space Systems
Date:   Wednesday, October 28th, 2026

Time:  2PM - 3PM ET

Virtual Link: https://teams.microsoft.com/meet/2205202324560830?p=ujaza5yWTRsOGWnxsv

Marilyn Braojos

Robotics PhD Student

School of Aerospace Engineering

Georgia Institute of Technology

Committee members

• Dr. E. Glenn Lightsey (advisor), School of Aerospace Engineering, Georgia Institute of Technology
• Dr. Yashwanth Nakka, School of Aerospace Engineering, Georgia Institute of Technology
• Dr. Sean Wilson, School of Electrical and Computer Engineering, Georgia Tech Research Institute
• Dr. Julie Wertz, Jet Propulsion Laboratory
• Dr. Kar-Ming Cheung, Jet Propulsion Laboratory

Summary:

Spacecraft rely on state estimation robust to dynamic environments for increased autonomy. This proposal presents predictive modeling frameworks that increase spacecraft awareness and resilience. The first aim of this work addresses navigation challenges from extended periods of radio communication outages. Global LSTM-based architectures were benchmarked with ARIMA architectures to model GPS satellites’ clock bias for extended 6-hour horizons at < 2 ns accuracy. The second aim of this proposal transitions to mobile vehicle uncertainty awareness from proprioceptive sensor information. To mitigate the limitations of exteroceptive sensors, this aim demonstrates an uncertainty-aware terrain classification mechanism based on an evidential deep learning architecture. Extracting accurate uncertainty from out-of-distribution sensor data provides further insight into system faults and terrain hazards. The final aim of this proposed work integrates the uncertainty-aware mechanism into an adaptive extended Kalman filter to achieve real-time state estimation in the Husky A200. This capability will be demonstrated by dynamically scaling the EKF’s measurement noise covariance matrix in ROS Gazebo as the mobile robot traverses pre-defined trajectories with varying friction characteristics to represent different terrain types. Together, these aims deliver a comprehensive framework capable of safe and reliable state estimation resilient to dynamic environments and unexpected sensor faults.