Napsiah Ismail
Papers
15
Total Citations
705
H-Index
7
About
Napsiah Ismail is a prominent robotics and automation researcher whose work spans mobile robot navigation, manipulator kinematics, and autonomous vehicle localization. With a career dedicated to advancing intelligent robotic systems, Ismail has made significant contributions to both theoretical foundations and practical implementations in robotics engineering. Among her most influential contributions is a comprehensive review of visual odometry techniques (2016), which has garnered an impressive 283 citations and serves as an essential reference for researchers tackling autonomous vehicle localization challenges. Her pioneering work on solving the inverse kinematics problem for 6 DOF serial robot manipulators using adaptive-learning algorithms (2006, 101 citations) established her as a leading voice in manipulator control, further reinforced by her neural network-based Jacobian solutions for handling singular configurations (2009, 99 citations). Ismail has also advanced mobile robot behavior through fuzzy cognitive mapping for reactive navigation (2012, 81 citations) and minimum avoidance systems (2008, 89 citations). Her recurring application of artificial neural networks and fuzzy logic demonstrates a consistent commitment to bio-inspired computational approaches. Collectively accumulating nearly 700 citations, her body of work has meaningfully shaped how researchers approach autonomous navigation and robotic control in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1Review of visual odometry: types, approaches, challenges, and applications283 citations · 2016
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- 5An expert fuzzy cognitive map for reactive navigation of mobile robots81 citations · 2012
- 6A new adaptive learning algorithm for robot manipulator control11 citations · 2007
- 7A Review on Positioning Techniques and Technologies: A Novel AI Approach10 citations · 2009
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