Mohammad Rasoul Abazari
Papers
1
Total Citations
3
H-Index
1
About
Mohammad Rasoul Abazari is a robotics researcher whose work focuses on the intersection of path planning, optimization, and obstacle avoidance for robotic systems. His most-cited paper, "Optimize path planning of a planar parallel robot to avoid obstacle collision using genetic algorithm" (2014), introduces a novel approach for the 3-RRR planar robot, employing a reforming polynomial to refine trajectories when obstacles are encountered. By integrating a genetic algorithm to determine end-effector positions over time, Abazari demonstrates a computationally efficient method for real-time collision-free navigation. This contribution addresses a critical challenge in robotics—enabling autonomous systems to adapt dynamically to cluttered environments. While his citation count is modest, the work reflects a practical, algorithm-driven mindset that bridges theoretical optimization with real-world robotic applications. Abazari’s research is particularly relevant for students and engineers interested in parallel robotics, evolutionary algorithms, and motion planning, offering a clear example of how genetic algorithms can enhance robotic autonomy in constrained spaces.
Research Focus
Key Achievements
Top Papers
- 1