Pauline Ong
Papers
8
Total Citations
570
H-Index
6
About
Dr. Pauline Ong is a leading researcher in autonomous robotics and computational intelligence, with a primary focus on path planning for mobile robots and automated guided vehicles (AGVs). Her most significant contribution lies in advancing Q-learning algorithms for robot navigation, where she has developed innovative modifications—including distance metrics, virtual targets, and distortion concepts—that dramatically improve convergence speed and reduce computational time compared to classical methods. Her seminal 2019 paper on improved Q-learning for optimal path planning has garnered 321 citations, establishing it as a foundational reference in the field. Dr. Ong has also pioneered nature-inspired optimization algorithms, most notably the carnivorous plant algorithm (2020, 116 citations), which offers a novel approach to solving global optimization problems. Her work extends to computer vision applications, where she applied the flower pollination algorithm for cost-effective AGV path detection. With over 570 total citations across her most-cited works, Dr. Ong’s research bridges reinforcement learning, bio-inspired computing, and practical robotics, making her a key figure in developing more efficient, adaptive navigation systems for autonomous mobile robots in both static and dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1Solving the optimal path planning of a mobile robot using improved Q-learning321 citations · 2019
- 2A carnivorous plant algorithm for solving global optimization problems116 citations · 2020
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- 8Mobile Robot Path Planning using Q-Learning with Guided Distance2 citations · 2018