Aerial Robotics Research Facility

Research Interests

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• Aerial Robot (Drone) Applications

  • Counter-UAS

  • Infrastructure Inspection and Maintenance

  • Wildfire Detection and Monitoring

  • Search and Rescue; Surveillance and Reconnaissance

  • Delivery; Precision Agriculture

  • Urban Air Mobility (UAM); and much more!

• Space Robotics​​​​​​

  • Terrain Relative Navigation; Map-based Localization

  • Low-texture Terrain Navigation

  • Safe Landing Site Detection; Slope Estimation

  • Mars Science Helicopter

  • Spacecraft Docking

• Robot/Computer Vision

  • Visual-Inertial Odometry (VIO); Vision-Aided Inertial Navigation Systems (V-INS)

  • Simultaneous Localization and Mapping (SLAM)

  • Visual Language Model(VLM)-based Navigation

  • Sensor Fusion

• Guidance, Navigation, and Control (GNC)​​​​

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Research Topics

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Computer Vision & Robot Perception for Extreme Environments

Reliable perception is critical for robots operating in extreme environments, where conditions such as low light, high dynamics, and texture-poor scenes challenge traditional vision systems. To address these challenges, we develop computationally efficient, robustness-aware vision algorithms that perceive and interact with the real world. Through extensive practical experiments, we advance the image-processing front-end of high-precision VIO or VI-SLAM systems, improving estimation consistency, robustness, and accuracy under challenging conditions.

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Autonomous GNC Solutions for Unmanned Aircraft, Aerospace Mobility, and Robotics

Safe and seamless navigation is essential for aerospace and robotics platforms operating in GPS-denied or high-risk environments. To address this, we develop state-of-the-art intelligent GNC solutions. By leveraging high-fidelity, tightly coupled multi-sensor fusion tailored to system dynamics, our solutions deliver robust, consistent, and resilient navigation and guidance for safety-critical, real-world operations, validated through extensive field testing.

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Autonomy for Multi-Robot Systems

Autonomy is a key enabler of robotic collaboration and coordination under limited communication and line-of-sight constraints. To support this, we investigate distributed, autonomous decision-making that allows multiple heterogeneous robots to share information and make optimized, real-time decisions together, overcoming operational constraints. Field deployments ensure scalable and adaptable collaborative autonomy.