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Guofei Chen 
Biography
Hi! I am Guofei Chen, a Master’s student at RI, Carnegie Mellon University. I love building and iterating solid robotic systems and answering the most interesting research questions I found there. I am particularly insterested in navigation systems that actively interact with the environment and human.
Download my resumé .
Interests
- Planning & SLAM for Navigation
- Field Robotics
- Optimization
Education
MS in Robotics, 2025 (expected)
Carnegie Mellon University, Advisor - Prof. Ji Zhang
BE in Automation, 2023
Zhejiang University, Advisor - Prof. Fei Gao
Skills
Linux
ROS 1/2
Lua
C++
Unity
Research Experience
Research Assistant
Sep 2023 –
Dec 2025
Pittsburgh, Pennslyvania
- Autonomous Navigation - Field Robotics
- Mapping and Planning
- Navigation System Development: Unitree Go2, Diablo
Research Assistant
Jul 2022 –
Aug 2023
Hangzhou, Zhejiang
- Certifiable perception
- Localization in aerial swarm, Sensor Network
Member
Jun 2020 –
Nov 2022
Hangzhou, Zhejiang
- Path Planning and Motion Control
- Multi-robot Systems
Posts
A novel planning algorithm based on visibility of obstacles on the field.
Jun 12, 2022
8 min read
Projects
Publications
Full-stack navigation systems on Unitree Go2 and Diablo, aiming to provide a low-cost and ready-to-use platform for AI navigation research.
Guofei Chen, Botao He, Shibo Zhao, Yiannis Aloimonos, Wenshan Wang, Ji Zhang
A real-time 3D mapping and path planning algorithm for aerial robot that is up-to x1000 times faster than grid-map based strong benchmarks.
Botao He, Guofei Chen, Wenshan Wang, Cornelia Fermuller, Yiannis Aloimonos, Ji Zhang
An efficient navigation system that combines manipulating movable objects for shorter path, even in complex unknown environments.
Botao He, Guofei Chen, Wenshan Wang, Ji Zhang, Cornelia Fermuller, Yiannis Aloimonos
Search based planning algorithms on grid maps for solving path planning among movable obstacles problem.
Zhongqiang Ren, Bunyond Suvonov, Guofei Chen, Botao He, Yijie Liao, Cornelia Fermuller, Ji Zhang
An inference framework using deep network derived closely following optimal control principals for large-scale modeling chemical processes.
Zhichao Chen, Hao Wang, Guofei Chen, Yiran Ma, Le Yao, Zhiqiang Ge, Zhihuan Song