Yuyang Zhou

Papers

Total Citations

H-Index

About

Yuyang Zhou is a researcher focused on advancing autonomous navigation for mobile robots, with a particular emphasis on intelligent path planning in complex environments. His work addresses the critical challenge of enabling robots to navigate efficiently and safely through unpredictable surroundings. Zhou’s major contributions include innovative improvements to reinforcement learning and bio-inspired algorithms. His most cited paper (2023, 8 citations) enhances the Q-learning algorithm by integrating it with the Rapidly-exploring Random Tree (RRT) method, significantly reducing the blind exploration typical of standard Q-learning and boosting goal-oriented performance in intricate settings. Another key study (2022, 7 citations) refines Ant Colony Optimization for path planning, demonstrating how swarm intelligence can be adapted for real-world robotic navigation. These contributions are foundational for applications in logistics, search-and-rescue, and industrial automation. Zhou’s work stands out for its practical approach to merging classical algorithms with modern learning techniques, offering scalable solutions that improve both the speed and reliability of robot movement. His research continues to influence the development of more adaptive and intelligent autonomous systems.