Muhammad Imam Muthahhar
Papers
1
Total Citations
10
H-Index
1
About
Muhammad Imam Muthahhar is a robotics researcher whose work centers on the intersection of parallel-link manipulators, inverse kinematics, and artificial neural networks. His most cited paper, "Self-Learning of Delta Robot Using Inverse Kinematics and Artificial Neural Networks" (2021, 10 citations), introduces a novel approach to enabling delta robots—a type of parallel-link manipulator with three arms and a central gripper end-effector—to autonomously learn motion control. By converting end-effector trajectories through inverse kinematic analysis and leveraging neural network training, Muthahhar’s method reduces reliance on manual programming, enhancing adaptability in industrial automation. This contribution addresses key challenges in precision and self-correction for high-speed pick-and-place tasks. While his citation count reflects an emerging career, the work demonstrates significant potential for advancing intelligent robotic systems. Muthahhar’s research is particularly relevant for students and engineers exploring machine learning integration in robotics, offering a practical framework for self-learning mechanisms in complex kinematic structures. His focus on delta robots—widely used in manufacturing, packaging, and medical applications—positions him as a promising voice in the field of autonomous manipulation and adaptive control.
Research Focus
Key Achievements
Top Papers
- 1