Shenglei Shi
Papers
1
Total Citations
5
H-Index
1
About
Shenglei Shi is a robotics researcher whose work focuses on advancing motion planning and collision avoidance for autonomous systems. His primary research areas include sampling-based motion planning, safety certification, and efficient collision checking in complex configuration spaces. Shi’s major contribution is the development of hybrid safety certificates, a novel approach that significantly accelerates collision checking by combining geometric and analytical methods. This innovation addresses a critical bottleneck in robotics: the computational intractability of constructing full configuration space obstacles for complex problems. His most-cited paper, "Hybrid Safety Certificate for Fast Collision Checking in Sampling-Based Motion Planning" (2022), has garnered 5 citations and demonstrates how his method enables faster, safer path planning in high-dimensional spaces. By reducing the computational burden of collision detection, Shi’s work has practical implications for real-time robotic applications, from autonomous navigation to manipulation. His research bridges the gap between theoretical safety guarantees and practical computational efficiency, making him a promising contributor to the field of robot motion planning.
Research Focus
Key Achievements
Top Papers
- 1