Bryan Tan Shun Xing

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About

Bryan Tan Shun Xing is a robotics researcher whose work centers on intelligent navigation and obstacle avoidance for autonomous mobile systems. His primary research areas include fuzzy logic control, wheeled mobile robot navigation, and real-time environmental perception. Tan’s most cited paper, “A Wheeled Mobile Robot Obstacles Avoidance for Navigation Control in a Static and Dynamic Environments” (2023), introduces a Sugeno Fuzzy Inference System (FIS) that enables robots to dynamically avoid obstacles in both static and changing environments. By testing his approach in the Webots simulation platform, he demonstrated how fuzzy logic can enhance a robot’s ability to respond to unpredictable surroundings—a critical challenge in autonomous robotics. Though early in his career, Tan’s work has already garnered citations from peers exploring similar control strategies, reflecting its relevance to the growing field of mobile robotics. His research offers practical insights for developing safer, more adaptive robots for applications ranging from warehouse logistics to assistive technologies. Tan’s contributions highlight the potential of soft computing methods in bridging the gap between simulation and real-world robotic performance.