Ali Akbar Akbari
Ferdowsi University of Mashhad, Chiba University, University of Birjand
Papers
7
Total Citations
57
H-Index
4
About
Ali Akbar Akbari is a pioneering researcher in the intersection of robotics, bio-inspired control systems, and precision manufacturing. His work spans three key areas: bipedal locomotion, robotic grinding and polishing, and neural network-based control of musculoskeletal systems. Akbari’s most influential contribution is the development of an online bio-inspired trajectory generator for seven-link biped robots using Takagi-Sugeno fuzzy systems, a paper that has garnered 24 citations and laid groundwork for adaptive humanoid walking. In manufacturing, his early investigations into autonomous tool adjustment for robotic grinding (11 citations) and optimum mirror polishing of aluminum alloys (4 citations) have advanced precision automation, demonstrating practical applications in surface finishing. His research on new automated learning central pattern generators (CPGs) for rhythmic patterns (9 citations) bridges neuroscience and robotics, enabling more natural movement generation. More recently, Akbari has explored deep neural networks for social group detection and adaptive neurofuzzy control of the human arm’s musculoskeletal system, reflecting a shift toward human-robot interaction and rehabilitation. With over 57 total citations across his most-cited works, Akbari’s contributions are foundational for students and researchers interested in bio-inspired robotics, intelligent control, and automated manufacturing.
Research Focus
Key Achievements
Top Papers
- 1
- 2Autonomous tool adjustment in robotic grinding11 citations · 2002
- 3New automated learning CPG for rhythmic patterns9 citations · 2012
- 4
- 5
- 6Deep Neural Network with Extracted Features for Social Group Detection2 citations · 2021
- 7An Investigation of Tool Adjustment in Robot Grinding2 citations · 1999