Syamsiah Mashohor
Papers
7
Total Citations
124
H-Index
5
About
Syamsiah Mashohor is a leading researcher in robotics and autonomous systems, with key contributions spanning mobile robot navigation, multi-robot coordination, and sensor network deployment. Her most influential work, "A highly interpretable fuzzy rule base using ordinal structure for obstacle avoidance of mobile robot" (66 citations), introduced an innovative approach to making robot decision-making both transparent and effective—a critical advance for real-world autonomous navigation. She has also made significant strides in applying artificial neural networks to robotics, notably in "Predicting the Motion of a Robot Manipulator with Unknown Trajectories Based on an Artificial Neural Network" (26 citations), where she demonstrated how machine learning can solve complex kinematic and trajectory calculations that traditional methods struggle with. Her research extends to multi-robot systems for efficient terrain coverage and wireless sensor network deployment, addressing practical challenges in vast or hazardous environments. More recently, Mashohor has explored the integration of deep learning with visual SLAM, tackling the challenge of accurate depth prediction for enhanced camera tracking and dense mapping. Her work consistently bridges theoretical innovation with real-world applicability, making her a respected figure in intelligent robotics and autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 5Deploying clustered wireless sensor network by multi-robot system6 citations · 2014
- 6Online Mutual Adaptation of Deep Depth Prediction and Visual SLAM4 citations · 2021
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