Papers
4
Total Citations
37
H-Index
4
About
Riya Zeng is a robotics researcher whose work sits at the intersection of motion planning, terrain intelligence, and adaptive control for mobile robotic systems. With a focus on both wheeled and tracked platforms operating in unstructured, real-world environments, Zeng has made meaningful contributions to how autonomous robots navigate challenging terrain reliably and efficiently. Her most cited work, "Smooth-RRT*" (2021, 12 citations), advances the state of sampling-based motion planning by introducing a novel reconnection method that generates smooth, curved trajectories while respecting kinodynamic constraints — a significant practical improvement over classical RRT* approaches. Complementing this, her self-adaptive path tracking framework employing RBF neural networks (2021, 7 citations) addresses the persistent challenge of wheel slippage in field environments, combining kinematic and dynamic modeling for robust performance. Zeng's parallel research stream tackles terrain identification with equal rigor. Her integrated terrain identification framework (2020, 10 citations) fuses inertial and driving current signals to deliver stable, multi-source parameter estimation, while her learning-based counterpart (2020, 8 citations) achieves precise torque prediction under varied maneuver conditions. Together, these contributions position Zeng as a rising voice in intelligent, field-ready mobile robotics research.
Research Focus
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