Ee Soong Low
Papers
8
Total Citations
459
H-Index
5
About
Dr. Ee Soong Low is a leading researcher in autonomous mobile robotics, with a primary focus on intelligent path planning using reinforcement learning. His most significant contribution lies in advancing Q-learning algorithms for navigation, notably through his highly cited 2019 work "Solving the optimal path planning of a mobile robot using improved Q-learning" (321 citations), which introduced novel modifications to accelerate convergence and optimize routes. Dr. Low has systematically addressed classical Q-learning limitations—slow convergence and high computational time—by developing techniques such as distance metrics, virtual targets, and distortion concepts, demonstrated in subsequent papers with 75 and 35 citations respectively. His work extends beyond simulation to dynamic environments, and he has also explored vision-based navigation using flower pollination algorithms for automated guided vehicles. Notably, Dr. Low’s research portfolio includes an innovative application in sports robotics: designing a badminton robot capable of serving shuttlecocks like a human player. With over 450 total citations, his contributions are foundational for students and researchers seeking efficient, real-world solutions for autonomous robot navigation in both static and dynamic settings.
Research Focus
Key Achievements
Top Papers
- 1Solving the optimal path planning of a mobile robot using improved Q-learning321 citations · 2019
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- 6A BADMINTON ROBOT - SERVING OPERATION DESIGN5 citations · 2016
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- 8Mobile Robot Path Planning using Q-Learning with Guided Distance2 citations · 2018