Zongyan Wang
Papers
7
Total Citations
70
H-Index
5
About
Zongyan Wang is a leading researcher in robotics, specializing in the trajectory planning, autonomous navigation, and intelligent control of parallel and mobile robots. His work focuses on enhancing the dynamic performance and precision of industrial robots, particularly Delta parallel robots used in high-speed pick-and-place operations. Wang’s major contributions include developing the Improved Butterfly Optimization Algorithm (IBOA) for optimal time–jerk trajectory planning, which significantly improves robot efficiency and smoothness (24 citations). He also pioneered the IA-DWA algorithm, fusing A* with Dynamic Window Approach for robust autonomous navigation in mobile robots (15 citations), and advanced digital twin technology for real-time status monitoring and positioning compensation in parallel robots (10 citations). His research extends to deep learning hybrid methods like RP-YOLOX-DL for accurate target positioning and sliding mode control for polishing robots. With over 70 total citations across his most-cited works, Wang’s innovations are driving smarter, faster, and more reliable robotic systems for manufacturing and logistics. His work is essential reading for engineers and researchers seeking to push the boundaries of robot autonomy and precision.
Research Focus
Key Achievements
Top Papers
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- 2Research on autonomous navigation of mobile robots based on IA-DWA algorithm15 citations · 2025
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