Muslikhin Muslikhin
Southern Taiwan University of Science and Technology, Yogyakarta State University
Papers
7
Total Citations
121
H-Index
6
About
Muslikhin Muslikhin is a researcher at the forefront of intelligent robotics and automation, with a focus on integrating computer vision, deep learning, and the Internet of Things (AIoT) to create practical, autonomous systems. His major contributions lie in developing hybrid algorithms for object recognition and robotic manipulation. Notably, his most cited work (43 citations) introduces a novel combination of ANFIS and R-CNN for stereo vision-based object recognition, enabling robots to perceive and manipulate their environment with greater accuracy. He has also pioneered an AIoT-based picking algorithm for online shops, addressing the speed and convenience demands of Industry 4.0 and Society 5.0. Beyond industrial applications, Muslikhin’s work has a human-centered impact; he developed a Chinese chess robotic system using convolutional neural networks to provide cognitive engagement for the elderly, highlighting his commitment to socially beneficial technology. His research extends to motion control for collaborative robots in agriculture and self-correction mechanisms for robotic grasping in cluttered environments. With a growing portfolio of papers accumulating over 120 citations, Muslikhin is establishing himself as a key innovator in making robotic systems more intelligent, adaptable, and accessible for real-world challenges.
Research Focus
Key Achievements
Top Papers
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- 6Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning6 citations · 2021
- 7Kinematics Simulation of Welding Manipulator Based on V-REPPRO EDU1 citations · 2019