Papers
10
Total Citations
240
H-Index
5
About
Ahmad Reza Khoogar is a robotics researcher whose work spans intelligent control, motion planning, and biomechanical locomotion systems. His most influential contributions lie at the intersection of evolutionary computation and robotic kinematics, most notably his pioneering application of genetic algorithms to solve inverse kinematics problems in redundant robots — a paper that has garnered over 166 citations and remains a foundational reference in the field. Building on this, his early work on GA-based obstacle avoidance for redundant manipulators (34 citations) further established him as a leading voice in optimization-driven robot motion planning. Khoogar has also made significant contributions to neural network-based control, including dual neural network architectures for kinematic control and intelligent calibration techniques for robot link parameters — addressing practical challenges in high-precision robotic applications. A recurring theme throughout his career is bipedal locomotion, where he has developed increasingly realistic models incorporating pneumatic artificial muscles, self-impact joint constraints, and adaptive neural controllers for stair traversal and natural gait replication. His more recent research extends into stereo visual servoing for robotic grasping. Collectively, his body of work reflects a sustained commitment to bridging computational intelligence with practical robotics engineering challenges.
Research Focus
Key Achievements
Top Papers
- 1Inverse kinematics of redundant robots using genetic algorithms166 citations · 2003
- 2Obstacle avoidance of redundant manipulators using genetic algorithms34 citations · 2002
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- 5Controlling a New Biped Robot Model Since Walking Using Neural Network5 citations · 2007
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