Papers
5
Total Citations
191
H-Index
5
About
Rui Gao is a robotics researcher whose work spans autonomous navigation, collision avoidance, and robotic perception, with particularly influential contributions to multi-robot systems and reinforcement learning-based motion planning. His most celebrated work, "Reinforcement Learned Distributed Multi-Robot Navigation With Reciprocal Velocity Obstacle Shaped Rewards" (2022), has garnered 144 citations and stands as a landmark contribution to the field, introducing a distributed framework that intelligently combines reciprocal velocity obstacle concepts with reinforcement learning to enable robots to navigate complex, dynamic environments with remarkable adaptability. Building on this foundation, his AEMCARL framework further advanced collision avoidance by tackling the intertwined challenges of environment modeling, rapid perception, and reliable motion planning in crowded scenes. Gao's research interests extend beyond navigation — his earlier work on fruit recognition and object extraction for agricultural harvesting robots demonstrates a longstanding commitment to practical robotic vision systems. More recently, his Phase-SLAM research pushes boundaries in 3D spatial mapping using structured light illumination. Collectively, Gao's portfolio reflects a researcher who bridges theoretical innovation with real-world robotic applications, making meaningful contributions across nearly two decades of work.
Research Focus
Key Achievements
Top Papers
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- 3Recognition and location of fruits for apple harvesting robot.12 citations · 2010
- 4Object extraction for the vision system of fruit picking robot12 citations · 2004
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