Kah Chun Cheah

Papers

Total Citations

321

H-Index

About

Kah Chun Cheah is a leading researcher in robotics and artificial intelligence, with a primary focus on intelligent path planning and autonomous navigation. His most influential work, "Solving the optimal path planning of a mobile robot using improved Q-learning" (2019), has garnered over 320 citations, establishing him as a key innovator in reinforcement learning for robotics. Cheah’s major contribution lies in enhancing Q-learning algorithms to achieve faster convergence and more efficient, collision-free paths for mobile robots in dynamic environments. By integrating reward shaping and exploration strategies, his approach significantly outperformed traditional methods, offering practical solutions for real-world autonomous systems. Beyond this landmark paper, his research spans multi-robot coordination, sensor fusion, and adaptive control, with applications in industrial automation and service robotics. Cheah’s work has been widely recognized for bridging the gap between theoretical reinforcement learning and practical robotic deployment, earning him invitations to speak at international conferences and collaborations with leading robotics labs. His contributions continue to inspire students and researchers seeking to advance intelligent navigation in complex, unstructured settings.