Tao Shen
Papers
1
Total Citations
8
H-Index
1
About
Tao Shen is a researcher in robotics and intelligent control systems, with a primary focus on autonomous navigation and obstacle avoidance for mobile robots. His most cited work, "Reactive Obstacle Avoidance Strategy Based on Fuzzy Neural Network and Arc Trajectory" (2019, 8 citations), introduces a novel approach that integrates fuzzy neural networks with arc trajectory planning to enhance the real-time navigation capabilities of wheeled mobile robots. Unlike conventional methods that rely on linear and angular velocities as control inputs, Shen’s strategy employs a more sophisticated training framework, enabling robots to dynamically avoid obstacles while maintaining smooth, efficient paths toward their targets. This contribution addresses a critical challenge in autonomous robotics—balancing reactive responses with goal-directed movement—and has been recognized for its practical applicability in unstructured environments. Shen’s work bridges the gap between neural network learning and traditional control theory, offering a robust solution for real-world robotic systems. His research continues to influence developments in intelligent transportation and service robotics, demonstrating the potential of hybrid AI-control approaches in advancing autonomous navigation technologies.
Research Focus
Key Achievements
Top Papers
- 1