Papers
8
Total Citations
176
H-Index
5
About
Cheng-Yaw Low is a robotics researcher whose work centers on intelligent path planning, autonomous navigation, and rehabilitation robotics. His most significant contributions lie in advancing Q-learning algorithms for mobile robot path planning, where he has introduced innovative modifications—such as distance metrics, virtual targets, and distortion concepts—to dramatically improve convergence speed and computational efficiency in dynamic environments. His 2022 paper on modified Q-learning with distance metrics and virtual targets has garnered 75 citations, reflecting its impact on the field. Low has also explored terrain classification for agricultural robots, aiming to enable autonomous crop care and selective harvesting with minimal human supervision. In the biomedical domain, he has contributed to robot-assisted post-stroke rehabilitation through the development of a finger orthosis system, and to humanoid robotics with an adaptive controller for a 2-DOF robot arm that mimics human motor control. His work on anomaly detection algorithms for clinical data further demonstrates his versatility. With over 170 cumulative citations, Low’s research bridges theoretical reinforcement learning and practical robotic applications, offering tangible solutions for autonomous navigation, agriculture, and healthcare.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Terrain Classification for Track-driven Agricultural Robots19 citations · 2014
- 4
- 5Adaptive Controller Algorithm for 2-DOF Humanoid Robot Arm18 citations · 2014
- 6Systematic Development of Machine for Abnormal Muscle Activity Detection4 citations · 2021
- 7Path optimization using genetic algorithm evolution3 citations · 2010
- 8