Welcome to the X-Bai Research Group at Rutgers University
The X-Bai Research Group advances intelligent, uncertainty-aware aerospace systems by integrating physics-based modeling, machine learning, uncertainty quantification, and experimental validation. Our research develops theories, algorithms, and testbeds that support space situational awareness, autonomous space operations, thermospheric density and space-weather prediction, and space robotics.
Supporting Our Vision
Our work is fueled by the trust and support from distinguished organizations such as NSF, AFOSR, NASA, ONR, DOD SBIR, and several Rutgers strategic funds. Their backing allows us to push the boundaries of what's possible in our field.
Research Interests: Pioneering New Avenues in Space Autonomy
At the X-Bai Research Group, we are advancing intelligent and autonomous capabilities for future space systems.
Space Situational Awareness and Astrodynamics: We improve how space objects are tracked, characterized, and predicted through advanced orbit propagation, uncertainty quantification, and physics-informed machine learning.
Uncertainty-Aware Machine Learning for Space Systems: We develop physics-ML methods that enable reliable prediction, estimation, and decision-making under uncertain sensing, dynamics, and space-environment conditions.
Thermospheric Density and Space Weather Prediction: We build physics-informed machine-learning models for thermospheric neutral density forecasting and uncertainty quantification, supporting improved satellite drag modeling, orbit prediction, and conjunction assessment.
Space Robotics and Proximity Operations: We create autonomy algorithms and robotic testbeds for proximity operations, including pose and motion estimation for unknown objects, perception-aware planning, space manipulator trajectory optimization, and hardware-in-the-loop validation.
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