Nong Cheng
Papers
3
Total Citations
25
H-Index
3
About
Nong Cheng is a robotics researcher whose work spans path planning, locomotion, and state estimation for autonomous systems. His most influential contribution, "Extended RRT-based path planning for flying robots in complex 3D environments with narrow passages" (2012, 18 citations), advances sampling-based motion planning by enhancing the Rapidly Exploring Random Tree (RRT) algorithm to efficiently navigate constrained, high-dimensional spaces—a critical challenge for aerial robots operating in cluttered settings. Cheng also addresses legged locomotion in "A Static Gait Generation for Quadruped Robots with Optimized Walking Speed" (2020, 4 citations), where he develops a method to maximize traversal speed while preserving stability across varied terrains. In the domain of sensor fusion, his work "A Visual-Inertial Navigation System Based on Multi-State Constraint Kalman Filter" (2017, 3 citations) implements a tightly coupled visual-inertial framework that improves accuracy and robustness over loosely coupled alternatives, a key enabler for reliable state estimation in GPS-denied environments. Together, these contributions demonstrate Cheng’s focus on practical, computationally efficient solutions for autonomous robots operating in real-world, unstructured environments.
Research Focus
Key Achievements
Top Papers
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