Papers
5
Total Citations
35
H-Index
3
About
Qiang Gu is a robotics researcher whose work spans multi-robot coordination, real-time motion planning, and medical robotics. He introduced a novel ROS-based hybrid architecture for heterogeneous multi-robot systems (19 citations), integrating personal computers with embedded systems to balance complex computation and real-time control. His FPGA-based collision detection accelerator (6 citations) addresses the critical challenge of real-time motion planning for robotic arms in dynamic environments, enabling faster and safer operation. Gu has also pioneered clinical applications of robotics, demonstrating in a 2022 study (4 citations) that robot-assisted percutaneous balloon compression significantly improves one-time puncture success rates and safety in treating primary trigeminal neuralgia. His reinforcement learning approach to robotic grasping (3 citations) advances model-free methods for six-degree-of-freedom manipulation using RGB-D sensing. Additionally, his foundational work on car-like mobile robots (3 citations) provides a complete framework for trajectory-tracking from modeling through embedded implementation. Gu’s research uniquely bridges industrial robotics, embedded systems, and medical applications, with his clinical work representing a notable translation of robotic precision into improved patient outcomes.
Research Focus
Key Achievements
Top Papers
- 1A new ROS-based hybrid architecture for heterogeneous multi-robot systems19 citations · 2015
- 2FPGA-based Design and Implementation of Real-time Robot Motion Planning6 citations · 2019
- 3
- 4A Method of Robot Grasping Based on Reinforcement Learning3 citations · 2022
- 5