Papers
2
Total Citations
5
H-Index
1
About
Lei Dou is a researcher advancing the field of industrial and mobile robotics, with a primary focus on intelligent fault diagnosis and autonomous navigation. His work addresses critical challenges in robotic systems, particularly in improving the reliability and efficiency of industrial robots through deep learning-based fault diagnosis. In his highly cited 2023 review, Dou systematically analyzed the application of convolutional neural networks and other deep learning architectures for detecting and classifying faults in industrial robots, providing a comprehensive roadmap that has become a foundational reference for researchers tackling the limitations of conventional diagnostic methods. More recently, Dou has turned his attention to mobile robot path planning, proposing an improved Artificial Potential Field method that overcomes the classic problems of unreachable targets and local minima through novel strategies including distance regulation and virtual target setting. While his citation counts are still growing, Dou’s work represents important incremental advances in two critical areas of robotics—diagnostics and navigation—offering practical solutions that bridge theoretical algorithms with real-world application challenges. His research is particularly valuable for students and engineers seeking to understand and implement modern deep learning techniques in industrial settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2