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About

Chenwei Deng is a robotics researcher whose work centers on intelligent control systems for autonomous mobile robots, with a particular focus on wall-following behavior. His major contributions lie in optimizing PID controllers using bio-inspired algorithms to enhance robot navigation accuracy and robustness. Deng’s most influential work, "Improving a Wall-Following Robot Performance with a PID-Genetic Algorithm Controller" (2018), has garnered 20 citations, demonstrating its impact on the field. He further advanced this line of research by applying Particle Swarm Optimization (PSO) in his 2019 paper, which has received 15 citations, showcasing his systematic approach to improving autonomous robot performance through automated parameter tuning. Additionally, Deng has explored multimodal fusion for robotics (2018), indicating a broader interest in integrating sensor data to enhance robotic perception and decision-making. His research is particularly valuable for students and engineers seeking practical, optimization-driven solutions for mobile robot control. By combining classical control theory with evolutionary computation, Deng has created a clear pathway for developing more adaptive and efficient autonomous systems.