Sheng Yang
Papers
1
Total Citations
2
H-Index
1
About
Sheng Yang is a robotics researcher whose work centers on autonomous navigation and computer vision for mobile robots. His most-cited paper, "Autonomous Obstacle Avoidance Scheme Using Monocular Vision Applied to Mobile Robots" (2021), introduces a novel strategy that enhances robot mobility in unknown environments by integrating Canny edge detection with Otsu’s thresholding to extract obstacle features from monocular camera feeds. This approach allows robots to identify static or slow-moving barriers and determine critical pixels for real-time path planning, addressing a fundamental challenge in field robotics. While his citation count is still emerging, Yang’s contribution lies in offering a computationally efficient, vision-only solution that reduces reliance on expensive sensors, making autonomous navigation more accessible. His work is particularly relevant for researchers exploring low-cost robotic systems and sensor-limited applications. As the field moves toward greater autonomy in unstructured settings, Yang’s method provides a practical foundation for obstacle avoidance, and his ongoing research promises further advances in intelligent, vision-guided mobility.
Research Focus
Key Achievements
Top Papers
- 1