Papers
15
Total Citations
529
H-Index
9
About
Gexiang Zhang is a leading researcher at the intersection of membrane computing (P systems) and intelligent robotics, pioneering bio-inspired control and planning algorithms. His major contributions include the design and implementation of membrane controllers for nonholonomic wheeled mobile robots, achieving precise trajectory tracking through the integration of feed-forward and feedback controls. He has also developed modified membrane-inspired algorithms, such as mMPSO, which combine P systems with particle swarm optimization for multi-objective robot path planning in dynamic environments. Zhang’s work on membrane parallel rapidly-exploring random tree (RRT) algorithms has advanced robotic motion planning in high-dimensional spaces. His research on enzymatic numerical P systems and spiking neural P systems has led to novel multi-behavior coordination controllers and joint controllers for walking biped robots. With his most-cited paper, *Real-life Applications with Membrane Computing* (177 citations), Zhang demonstrates the practical impact of his work. His recent innovations include entropy-weighted numerical gradient optimization spiking neural systems for biped robot control and constrained deep deterministic policy gradient for gait optimization. Overall, Zhang’s research bridges theoretical membrane computing with real-world robotic systems, offering efficient, parallel, and distributed solutions for autonomous navigation and motion control.
Research Focus
Key Achievements
Top Papers
- 1Real-life Applications with Membrane Computing177 citations · 2017
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