Heru Wahyu Herwanto
Papers
2
Total Citations
18
H-Index
2
About
Heru Wahyu Herwanto is a researcher focused on computational robotics, particularly the application of neural networks to solve complex kinematic problems. His primary research area centers on inverse kinematics for robotic manipulators—the mathematical challenge of determining the joint angles required to position a robot arm’s end-effector at a desired location. Herwanto’s major contributions involve pioneering the use of specialized neural network training algorithms to address this non-linear problem. In his most cited work (14 citations), he demonstrated how Bayesian regularization backpropagation can effectively solve inverse kinematics for planar manipulators, offering improved generalization and robustness over traditional methods. He further advanced this field by exploring alternative architectures, such as the Levenberg-Marquardt backpropagation algorithm, to enhance trajectory tracking accuracy. Though his citation counts are modest, Herwanto’s focused body of work provides foundational insights into integrating machine learning with robotic control systems. His research is particularly valuable for students and engineers seeking practical, data-driven approaches to robot arm control, bridging the gap between theoretical neural network models and real-world robotic applications.
Research Focus
Key Achievements
Top Papers
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