Chaofan Ren
Papers
2
Total Citations
27
H-Index
2
About
Chaofan Ren is a researcher specializing in robotics and trajectory optimization, with a primary focus on enhancing the efficiency and cost-effectiveness of automated systems. His major contributions lie in the development of time-optimal and energy-optimal trajectory planning methods for industrial robots, particularly through the integration of high-degree polynomial interpolation and metaheuristic optimization algorithms. Ren’s most cited work, "Robot Time-Optimal Trajectory Planning Based on Quintic Polynomial Interpolation and Improved Harris Hawks Algorithm" (2023), has garnered 23 citations and addresses a critical challenge in robotics: minimizing operational time while maintaining smooth motion. He further advanced this line of research in his 2025 paper on freight train cleaning robots, where he employed seventh-degree polynomial interpolation and an enhanced Harris Hawks Optimizer to simultaneously optimize time and energy consumption. By refining swarm intelligence algorithms for real-world robotic applications, Ren’s work directly impacts manufacturing, logistics, and maintenance sectors, offering scalable solutions for reducing cycle times and operational costs. His research is particularly valuable for students and engineers seeking practical, computationally efficient approaches to trajectory planning in constrained environments.
Research Focus
Key Achievements
Top Papers
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