Xing Xiangrui
Papers
2
Total Citations
28
H-Index
2
About
Xing Xiangrui is a researcher whose work lies at the intersection of artificial intelligence, robotics, and optimization algorithms, with a particular focus on advancing robot path planning. His major contributions center on enhancing the intelligence and efficiency of pathfinding systems by integrating deep reinforcement learning with the seeker optimization algorithm (SOA). In his most-cited work, "Robot path planner based on deep reinforcement learning and the seeker optimization algorithm" (2022, 25 citations), he addressed critical limitations of traditional SOA—namely, slow convergence and poor solving ability—by proposing a novel hybrid approach. This work has been recognized for its practical impact on mobile robot navigation. He further refined this methodology in a subsequent paper, "DDPG-Based Improved Seeker Optimization Algorithm for Robot Path Planning" (2022, 3 citations), which leveraged the Deep Deterministic Policy Gradient (DDPG) framework to boost algorithmic intelligence. Together, these contributions demonstrate Xing’s commitment to creating more adaptive, autonomous robotic systems. His research offers valuable insights for students and engineers working on real-world robot navigation challenges, bridging the gap between classical optimization and modern reinforcement learning techniques.
Research Focus
Key Achievements
Top Papers
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