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About

Soh Chin Yun is a robotics and artificial intelligence researcher whose work centers on mobile robot navigation, path planning, and intelligent control systems. With a career dedicated to solving the fundamental challenge of guiding autonomous robots through complex, dynamic, and unknown environments, Soh has made meaningful contributions to the field by developing and refining a range of computational approaches. Among his most significant achievements is his work on improved genetic algorithms for optimum path planning, which garnered 53 citations and introduced novel obstacle avoidance strategies that enhanced both efficiency and reliability in robot navigation. His 2011 study on dynamic path planning further extended this work by leveraging genetic algorithms to enable robots to learn and adapt in real time, earning 44 citations. Earlier contributions exploring fuzzy logic and neural network controllers for obstacle avoidance attracted 38 citations, while his neural Q-learning framework — integrating reinforcement learning with artificial neural networks — demonstrated a forward-thinking approach to autonomous decision-making. Across his body of work, Soh has consistently bridged classical optimization methods with modern machine learning techniques, producing research that remains relevant to students and practitioners working on autonomous systems, robotics, and AI-driven navigation. His cumulative citation record reflects a sustained and valued contribution to the robotics research community.