Trusted Autonomy for Rapidly Prototyped Uncrewed Ground Vehicles
Sep 1, 2024
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1 min read
Project Lead (Sep 2024 — Present)
- Engineered a rapidly deployable autonomous ground robot capable of exploring, mapping, and navigating previously unseen terrain with minimal setup and low hardware cost for expeditionary missions.
- Developed an interpretable autonomy stack that converts multi-modal sensor data into human-readable terrain and uncertainty maps to support both autonomous decision-making and operator trust.
- Implemented online mapping and real-time replanning that adapts navigation as new obstacles and terrain features are discovered during execution.
- Developed a Python data access layer over ROS2 bag files (SQLite), enabling SQL-style queries for efficient extraction and analysis of logged autonomy data.
- Deployed and maintained the full autonomy stack on embedded onboard compute under real-world constraints, and collaborated with NIWC Pacific human-factors researchers through regular technical reviews.
Tech: PyTorch, ROS 2, SQL, SLAM, YOLO, Semantic Mapping, CasADi, Jetson Orin
Authors
Shengfan Cao
(he/him)
PhD Researcher in Autonomous Driving & Robotics
PhD researcher with 3+ years of hands-on experience in autonomous driving and robotic systems, spanning safe learning, control, and end-to-end autonomy deployment. I am transitioning into industry to work where large-scale data and real-world constraints continuously shape and validate learning-based autonomous systems.