Xueheng Hu

About

Xueheng Hu is a leading researcher in robotics and autonomous systems, with a primary focus on intelligent control, path planning, and navigation for humanoid and parallel robots. Hu’s major contributions lie in developing advanced algorithms that enable robots to operate effectively in complex, unstructured environments. Notably, Hu pioneered the use of Deep Deterministic Policy Gradient for obstacle-avoiding path planning of humanoid arms, a work that has garnered 30 citations. Further innovations include the application of the Retinex algorithm for autonomous robot navigation in low-light conditions (18 citations) and the integration of fuzzy Q-learning with Takagi–Sugeno structures for improved trajectory planning and obstacle avoidance in humanoid manipulators like the NAO robot. These combined works, totaling over 78 citations, demonstrate a sustained impact on the field of robotic control. Hu’s research is distinguished by its practical, algorithm-driven approach to solving real-world challenges in robot motion and manipulation, making significant strides toward more autonomous and adaptive robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Path Planning of Humanoid Arm Based on Deep Deterministic Policy Gradient

   30 citations · 2018
2. 2
   Autonomous robot navigation using Retinex algorithm for multiscale image adaptability in low-light environment

   18 citations · 2019
3. 3
   Q-learning trajectory planning based on Takagi–Sugeno fuzzy parallel distributed compensation structure of humanoid manipulator

   12 citations · 2019
4. 4
   NAO robot obstacle avoidance based on fuzzy Q-learning

   11 citations · 2019
5. 5
   Fractional-order internal model control algorithm based on the force/position control structure of redundant actuation parallel robot

   7 citations · 2020

Key Collaborators

Contact & Links