Amir Seyyedabbasi
Papers
7
Total Citations
378
H-Index
6
About
Amir Seyyedabbasi is a leading researcher at the intersection of artificial intelligence, robotics, and sustainable agriculture. His primary contributions lie in developing novel hybrid algorithms that combine reinforcement learning with metaheuristic methods to solve complex global optimization problems—work that has garnered over 146 citations. He is perhaps best known for creating the Adapted-RRT algorithm, a hybrid sampling and metaheuristic approach for three-dimensional path planning, which has become a cornerstone for autonomous robot navigation in challenging environments. Seyyedabbasi has also pioneered the Sand Cat Swarm Optimization (SCSO) algorithm, a nature-inspired metaheuristic used for feedback controller design and inverse kinematics in robotic arms. His research has a strong applied focus, particularly in smart agriculture, where his adaptive methods enable autonomous robots to perform tasks from cultivation to harvest, addressing critical global food security challenges. With multiple papers accumulating over 80 citations each, Seyyedabbasi’s work is widely recognized for its practical impact, bridging theoretical optimization with real-world robotic systems. His innovative algorithms continue to influence both academic research and industrial applications in autonomous systems and sustainable technology.
Research Focus
Key Achievements
Top Papers
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- 4A Smart and Mechanized Agricultural Application: From Cultivation to Harvest29 citations · 2022
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