Papers
8
Total Citations
108
H-Index
4
About
Weiguo Zhou is a leading researcher in robotics and computer vision, with a focus on developing efficient, real-time systems for robotic manipulation and perception. His most impactful work, an efficient fully convolutional neural network for generating pixel-wise robotic grasps from high-resolution RGB-D images (58 citations), has significantly advanced the field of automated grasping by enabling precise, high-speed grasp planning. Zhou has also made notable contributions to hardware acceleration, pioneering FPGA-based parallel architectures for computationally intensive tasks like the SIFT algorithm and real-time target tracking, which are critical for resource-constrained platforms such as micro aerial vehicles. His research extends to 3D object recognition with MVPointNet, a multi-view network for point cloud analysis, and practical applications like vision-based automatic express package dispatching using industrial robots. More recently, Zhou has explored medical robotics, developing a novel injection system with an instantaneous remote center of motion mechanism. With a strong track record of bridging algorithmic innovation and embedded hardware implementation, his work continues to push the boundaries of autonomous robotic systems in both industrial and healthcare settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2FPGA-based parallel hardware architecture for SIFT algorithm19 citations · 2016
- 3MVPointNet: Multi-View Network for 3D Object Based on Point Cloud17 citations · 2019
- 4Vision Based Picking System for Automatic Express Package Dispatching5 citations · 2019
- 5High-speed target tracking base on FPGA3 citations · 2016
- 6
- 7Real-time target tracking and positioning on FPGA2 citations · 2016
- 8