Faikul Umam
Papers
7
Total Citations
40
H-Index
4
About
Faikul Umam is a robotics researcher whose work centers on autonomous mobile robot navigation, obstacle avoidance, and human-robot interaction. Over the course of a decade-long research career, he has made meaningful contributions to the development of intelligent robotic systems capable of operating independently in complex real-world environments. Umam's most impactful work includes a 2023 study on stereo vision-based obstacle avoidance for omni-directional robots, which has garnered 15 citations and represents a significant advance in sensor-driven navigation. His earlier foundational research on fuzzy logic controllers for autonomous path planning (2013, 9 citations) established his expertise in AI-driven robot control. More recently, he explored practical applications of autonomous robotics, developing a museum tour guide robot powered by TensorFlow object detection (2022, 5 citations), demonstrating a commitment to translating research into socially useful systems. Umam has also investigated human-robot interaction through gesture recognition using convolutional neural networks, and examined mobile robot stability across varying terrain conditions. Collectively, his publications reflect a researcher dedicated to bridging intelligent control theory with real-world robotic deployment, accumulating over 40 citations across his body of work and contributing valuable insights to the growing field of autonomous robotics.
Research Focus
Key Achievements
Top Papers
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- 4Optimalization of Detection and Navigation Smart Bin Robot Using Camera5 citations · 2017
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