Papers
14
Total Citations
94
H-Index
5
About
Dayou Li is a robotics researcher whose work spans robot navigation, task planning, and autonomous manipulation. His most-cited paper (26 citations) tackles the critical “dead-end” problem in robot navigation using a genetic algorithm approach, enabling robots to escape obstacle-surrounded areas—a capability essential for applications like rescue operations. Li has also advanced service robotics by integrating semantic knowledge representation into automated task planning, allowing robots to reason and act in dynamic, unstructured domestic environments. His contributions extend to practical implementations of Simultaneous Localization and Mapping (SLAM) using the Robot Operating System (ROS) and Arduino, making sophisticated mapping accessible and cost-effective. More recently, Li has explored industrial applications, including coarse-to-fine detection of multiple weld seams for robotic welding, and learning-based grasp synergy for target-oriented grasping in occluded scenes. He has also ventured into robotic nanoassembly, surveying its challenges and potential. With a career spanning foundational navigation solutions to cutting-edge manipulation and 5G-enabled robot deployment, Dayou Li’s work demonstrates a consistent focus on bridging theoretical robotics with real-world, deployable systems.
Research Focus
Key Achievements
Top Papers
- 1Genetic algorithm based solution to dead-end problems in robot navigation26 citations · 2011
- 2
- 3K-Order Surrounding Roadmaps Path Planner for Robot Path Planning12 citations · 2013
- 4An implementation of SLAM using ROS and Arduino8 citations · 2017
- 5Robotic nanoassembly: current developments and challenges6 citations · 2011
- 6
- 7Coarse-to-Fine Detection of Multiple Seams for Robotic Welding4 citations · 2024
- 8
- 9
- 10