Zilong Cheng
Papers
3
Total Citations
75
H-Index
3
About
Zilong Cheng is a leading researcher in robotics and autonomous systems, specializing in trajectory planning, control theory, and multi-agent decision-making. His most impactful work, "Local Learning Enabled Iterative Linear Quadratic Regulator for Constrained Trajectory Planning" (2022, 46 citations), introduces a novel approach that integrates local learning with optimal control to efficiently handle nonlinear dynamics and constraints—a critical advancement for autonomous navigation. Cheng also addresses the challenge of motion planning in complex 3D environments in his paper "Integrated Planning and Control for Collision-free Trajectory Generation in 3D Environment with Obstacles" (2019, 20 citations), extending beyond traditional 2D solutions. Additionally, his research on "Data-Driven Predictive Control for Multi-Agent Decision Making With Chance Constraints" (2020, 9 citations) advances model predictive control for multi-robot systems under uncertainty, with applications in mobile robots and unmanned vehicles. With over 75 total citations, Cheng’s work bridges theoretical rigor and practical implementation, offering scalable solutions for constrained trajectory generation and cooperative autonomy. His contributions are essential for students and researchers seeking to understand modern approaches to safe, efficient robot motion in dynamic environments.
Research Focus
Key Achievements
Top Papers
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