Hamid Moghadam-Fard
Papers
2
Total Citations
13
H-Index
2
About
Hamid Moghadam-Fard’s research lies at the intersection of humanoid robotics and adaptive control, with a focus on enabling more natural and robust robot motion. His work on walking pattern generation addresses a fundamental challenge in humanoid control: creating stable, human-like gaits. In his 2014 paper on pattern generation for humanoid robots, Moghadam-Fard advanced Zero Moment Point (ZMP)-based approaches, comparing and refining models like the Linear Inverted Pendulum Mode (LIPM) and Gravity Compensated Inverted Pendulum Mode (GCIPM) to produce more natural ZMP trajectories. This work has garnered 7 citations, reflecting its relevance to researchers tackling bipedal locomotion. Beyond walking, Moghadam-Fard has contributed to vision-based control of robot manipulators, developing adaptive controllers that operate reliably even when camera parameters are uncertain. His 2014 paper on this topic, cited 6 times, introduced an image-based controller using the image Jacobian, demonstrating how interpolation techniques can enhance robotic precision in unstructured environments. Together, these contributions showcase Moghadam-Fard’s commitment to making robots both more autonomous and more adaptable—key goals for the next generation of intelligent machines.
Research Focus
Key Achievements
Top Papers
- 1Pattern generation for humanoid robot with natural ZMP trajectory7 citations · 2014
- 2