Vahid Mortazavy
Papers
1
Total Citations
3
H-Index
1
About
Vahid Mortazavy’s research lies at the intersection of robotics, intelligent control, and neural network optimization, with a focus on real-time autonomous navigation. His most cited work introduces a novel framework for optimal trajectory planning of wheeled mobile robots, combining Generalized Regression Neural Networks (GRNN) with optimal control theory to achieve real-time, kinodynamically constrained path planning in obstacle-laden environments. This approach bridges the gap between mathematical precision and computational efficiency, enabling robots to generate collision-free, time-optimal paths without sacrificing accuracy. By integrating neural networks with classical control, Mortazavy’s method offers a scalable solution for dynamic, unstructured settings—a critical advance for autonomous systems in manufacturing, logistics, and service robotics. Though his citation count is modest, the work’s conceptual rigor and practical relevance have established a foundation for further research in adaptive, real-time robot motion planning. His contributions highlight a commitment to solving tangible engineering challenges, making his research a valuable reference for students and engineers exploring intelligent, constraint-aware navigation systems.
Research Focus
Key Achievements
Top Papers
- 1