Zhengdong Yang

Papers

Total Citations

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About

Zhengdong Yang is a robotics researcher whose work focuses on advancing autonomous navigation for mobile robots, particularly in indoor and unknown environments. His primary research areas include path planning, path tracking, and sensor fusion algorithms. Yang’s major contributions center on improving the efficiency, accuracy, and stability of robot motion control. His most cited paper, "PP-ST: An Indoor Mobile Robot Path Tracking Algorithm" (2023, 9 citations), addresses critical limitations in the Pure Pursuit algorithm—specifically, its tendency to approach paths too slowly under long look-ahead distances and to cause heading jitter at corners under short distances. Yang’s proposed solution enhances tracking performance in real-world indoor settings. In another notable work, "Improved A*-DWA Fusion Path Planning Algorithm with Ideal Path Area Constraints" (2023, 2 citations), he tackles the dual challenge of global optimal pathfinding and real-time obstacle avoidance by fusing A* with the Dynamic Window Approach (DWA). This algorithm introduces path area constraints to generate smoother, safer trajectories. Yang’s research is directly applicable to service robots, warehouse automation, and autonomous vehicles, offering practical improvements in navigation robustness. His work is gaining recognition for bridging theoretical path planning with real-world deployment constraints.