Papers
10
Total Citations
69
H-Index
4
About
Dr. Qingji Gao is a pioneering roboticist whose research spans continuum robotics, autonomous navigation, and intelligent swarm systems. His most impactful work focuses on developing specialized inspection robots for aircraft fuel tanks, where he designed cable-driven continuum robots capable of navigating cluttered, explosive environments—a contribution that has garnered 21 citations for his seminal 2014 paper on collision-free path planning using region clipping. Gao’s expertise extends to unstructured road detection for patrol-security robots, where he applied rough set theory and feature learning to overcome challenges like degraded surfaces and strong shadows (17 citations). He has also advanced reinforcement learning with an improved Q-learning algorithm that balances exploration and exploitation through an exploration region expansion strategy. His recent work on manned robot swarm scheduling, using an ant-sparrow algorithm to optimize energy consumption and passenger waiting time, demonstrates his continued innovation in real-world robotics. With over 70 total citations across his top papers, Gao’s research has practical implications for maintenance, security, and autonomous systems, making him a notable figure in applied robotics and intelligent control.
Research Focus
Key Achievements
Top Papers
- 1
- 2Rough Set based Unstructured Road Detection through Feature Learning17 citations · 2007
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- 4A Novel Design of Aircraft Fuel Tank Inspection Robot5 citations · 2013
- 5
- 6Long-term tracking method on ground moving target of UAV4 citations · 2014
- 7Breakage detection for grid images based on improved Harris corner4 citations · 2011
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- 10