Papers
7
Total Citations
88
H-Index
4
About
Song Chen is a leading researcher in robotics and autonomous systems, with a primary focus on Simultaneous Localization and Mapping (SLAM), multi-robot coordination, and intelligent path planning. Their most influential work, "SaD-SLAM: A Visual SLAM Based on Semantic and Depth Information" (2020), has garnered 53 citations and addresses a critical challenge in mobile robotics: enabling robust navigation in dynamic environments with moving objects, moving beyond traditional static-scene SLAM systems. Chen’s foundational research on formation and obstacle avoidance for multi-robot systems (2009, 14 citations) introduced leader-following algorithms combined with artificial potential fields, enabling robots to maintain formations and navigate unknown terrains—a key contribution to cooperative robotics. They have also advanced real-time face detection for mobile robots (2010, 10 citations) and developed efficient clustering methods for person-specific image retrieval (2009, 4 citations). More recently, Chen optimized industrial robot path planning with an improved Dijkstra algorithm (2022, 3 citations) and explored fine-grained asynchronous crossbar switches for neuromorphic computing (2023, 2 citations). Notably, Chen led "Team Water" to victory in the 2013 RoboCup Middle Size League, demonstrating their work’s real-world impact in competitive robotics.
Research Focus
Key Achievements
Top Papers
- 1SaD-SLAM: A Visual SLAM Based on Semantic and Depth Information53 citations · 2020
- 2
- 3
- 4
- 5
- 6Fine-Grained Transistor-Level QDI Asynchronous Crossbar Switch2 citations · 2023
- 7