Zhuguan Liang
Papers
2
Total Citations
28
H-Index
2
About
Zhuguan Liang is a leading researcher in intelligent robotics, with a primary focus on robot path planning and optimization algorithms. His most influential work bridges deep reinforcement learning with metaheuristic optimization, notably through his 2022 paper "Robot path planner based on deep reinforcement learning and the seeker optimization algorithm," which has garnered 25 citations. In this landmark study, Liang identified critical limitations in the traditional seeker optimization algorithm (SOA)—including insufficient intelligence, slow convergence, and poor solving ability—and proposed a novel hybrid approach that integrates deep reinforcement learning to overcome these challenges. His subsequent work on a DDPG-based improved SOA further refines this methodology, demonstrating his commitment to advancing autonomous navigation systems. Liang’s contributions are particularly significant for mobile robot applications, where efficient and intelligent path planning is essential. By combining the adaptive learning capabilities of deep reinforcement learning with the search efficiency of SOA, he has developed solutions that enhance both the speed and quality of path generation. His research continues to influence the fields of robotics and artificial intelligence, offering practical frameworks for real-world autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2