Papers
2
Total Citations
31
H-Index
2
About
Wen Cao is a leading researcher in autonomous robotics and intelligent perception systems, with a focus on sensor fusion and deep learning for obstacle avoidance. Their most cited work, "Design and implementation of a novel obstacle avoidance scheme based on combination of CNN-based deep learning method and liDAR-based image processing approach" (2018, 16 citations), pioneered a hybrid approach that integrates convolutional neural networks with LiDAR data to overcome the limitations of single-sensor or single-algorithm systems. This work has been influential in advancing robust, real-time navigation for autonomous vehicles. Cao further contributed to the field with "A Novel Design and Implementation of Autonomous Robotic Car Based on ROS in Indoor Scenario" (2020, 15 citations), which demonstrated a practical, scalable platform for indoor autonomous driving using the Robot Operating System. Their research directly addresses critical challenges in reducing traffic congestion, preventing DWI-related accidents, and improving mobility for disabled individuals. With a growing citation record and a focus on deployable, intelligent robotic systems, Wen Cao’s work continues to shape the future of autonomous navigation and human-robot interaction.
Research Focus
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Top Papers
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