Zhe‐Ming Lu
Papers
1
Total Citations
5
H-Index
1
About
Zhe-Ming Lu is a researcher at the forefront of robotics and artificial intelligence, with a primary focus on intelligent path planning and autonomous navigation for service robots. His most notable contribution is the development of Re-DQN, a deep reinforcement learning-based algorithm for complete coverage path planning, specifically designed for lawn mowing robots. This work, published in 2025 and already garnering 5 citations, addresses a critical challenge in smart home and agricultural automation: enabling robots to efficiently and comprehensively cover complex, unstructured environments without human intervention. By integrating deep Q-networks with a novel reward structure, Lu’s algorithm significantly improves coverage completeness and energy efficiency compared to traditional methods. His research bridges the gap between theoretical reinforcement learning and practical robotic applications, offering scalable solutions for autonomous lawn care and precision agriculture. Lu’s work is particularly impactful for students and engineers seeking to understand how modern AI can transform mundane tasks into fully automated, intelligent systems, reducing manual labor while enhancing operational reliability.
Research Focus
Key Achievements
Top Papers
- 1