Arifiansyah Zody
Papers
1
Total Citations
10
H-Index
1
About
Arifiansyah Zody is a robotics researcher whose work centers on the intelligent control and optimization of parallel-link manipulators, particularly the Delta robot. His major contribution lies in bridging classical kinematics with modern machine learning, as demonstrated in his highly cited paper, "SELF-LEARNING OF DELTA ROBOT USING INVERSE KINEMATICS AND ARTIFICIAL NEURAL NETWORKS" (2021, 10 citations). In this work, Zody developed a novel approach that uses inverse kinematics to generate training data for an artificial neural network, enabling the Delta robot to self-learn its own motion without explicit programming. This method significantly simplifies the calibration and control of these fast, precise robots, which are widely used in pick-and-place and assembly tasks. By combining analytical modeling with adaptive learning, Zody’s research has practical implications for making industrial robotics more accessible and efficient. His work is particularly notable for its interdisciplinary approach, merging robotics, control theory, and AI. With a growing citation impact, Zody is establishing himself as a key figure in the advancement of intelligent robotic systems, offering a blueprint for how robots can learn from their own physical constraints.
Research Focus
Key Achievements
Top Papers
- 1