Xiaonan Chang
Papers
2
Total Citations
27
H-Index
2
About
Xiaonan Chang is a researcher advancing the frontiers of robotics and intelligent optimization, with a focused expertise in trajectory planning for industrial and service robots. Their major contributions lie in developing computationally efficient, multi-objective optimization frameworks that simultaneously minimize operational time and energy consumption—critical challenges in real-world automation. Chang’s most-cited work (2023, 23 citations) introduces a novel approach combining quintic polynomial interpolation with an improved Harris Hawks Algorithm (HHO) to achieve time-optimal robot trajectories, directly addressing cost and efficiency demands in practical applications. Building on this, their 2025 study extends the methodology to freight train cleaning robots, employing seventh-degree polynomial interpolation and an enhanced HHO for combined time-energy optimization, demonstrating the versatility of their approach across different robotic domains. By integrating smooth, continuous path generation with bio-inspired metaheuristics, Chang’s research provides robust, implementable solutions that reduce operational costs while maintaining precision and safety. Their work is particularly notable for bridging theoretical optimization algorithms with tangible industrial needs, making it highly relevant for students and researchers in robotics, automation, and intelligent systems design.
Research Focus
Key Achievements
Top Papers
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