Hansheng Huang
Papers
1
Total Citations
4
H-Index
1
About
Hansheng Huang is a robotics researcher whose work focuses on the intersection of trajectory planning, obstacle avoidance, and learning from demonstration in dynamic environments. His most-cited paper, "Trajectory tracking and obstacle avoidance in dynamic environments using an improved artificial potential field method" (2025, 4 citations), tackles a critical challenge in robotics: enabling demonstration-learning models to simultaneously achieve precise path tracking and real-time avoidance of moving obstacles. Huang’s key contribution lies in developing a real-time trajectory planning framework that integrates an enhanced artificial potential field method, allowing robots to adaptively navigate cluttered, changing spaces without sacrificing fidelity to demonstrated paths. This work has immediate implications for autonomous navigation in applications like service robotics, manufacturing, and autonomous driving. Though early in his career, Huang’s research addresses a fundamental tension in robot learning—balancing imitation with reactive safety—and his approach offers a practical solution for deploying learning-based systems in unpredictable real-world settings. His findings are particularly relevant for researchers working on safe human-robot interaction and adaptive control.
Research Focus
Key Achievements
Top Papers
- 1