Zhenkun Zhai

Guangdong University of Technology, Guangdong Polytechnic Normal University

Papers

Total Citations

H-Index

About

Zhenkun Zhai’s research bridges artificial intelligence and robotics, with a primary focus on multi-agent systems, machine learning, and the design of novel robotic mechanisms. His early work on RoboCup 2D soccer simulation introduced a Q-learning-based passing strategy that enhanced cooperative decision-making among autonomous agents, demonstrating how reinforcement learning can optimize team coordination in dynamic environments. This foundational contribution, cited 6 times, remains relevant for researchers studying multi-agent collaboration. Zhai further advanced the field by modeling robotic soccer teams using Unified Modeling Language (UML), providing a structured framework for designing intelligent agent behaviors and inter-agent communication. In his more recent work, Zhai has shifted toward medical robotics, developing a concentric tube robot driven by a double-threaded helical gear mechanism—a design innovation that promises greater precision and flexibility for minimally invasive surgeries. Though still early in its citation impact, this work signals his expanding influence from simulation-based AI to tangible robotic systems. Zhai’s career trajectory reflects a deep commitment to integrating machine learning with mechanical design, making his contributions valuable for students and researchers exploring the intersection of AI, robotics, and real-world applications.