Shamina Akter
Papers
1
Total Citations
3
H-Index
1
About
Shamina Akter is a researcher whose work bridges computational intelligence and autonomous navigation, with a particular focus on path planning for mobile robots. Her most cited contribution, "Grid Based Path Planning Using CNN & Artificial Potential Field Method" (2013), introduces a novel hybrid approach that integrates cellular neural networks (CNN) with artificial potential field techniques. This method leverages CNN-based grayscale image processing to interpret environmental grids, while the artificial potential field provides a global path-planning framework, assigning each point in the environment a potential value to guide obstacle avoidance and route optimization. Though her citation count of 3 reflects a niche but foundational contribution, the work is notable for its early fusion of neural network image processing with classical robotics planning—a concept that has since gained traction in intelligent systems. Akter’s research sits at the intersection of robotics, computer vision, and control theory, offering a computationally efficient solution for real-time navigation in complex environments. Her work serves as a stepping stone for students and researchers exploring hybrid AI-driven approaches to autonomous mobility, demonstrating how combining neural architectures with physics-based models can yield practical, grid-based solutions for path planning challenges.
Research Focus
Key Achievements
Top Papers
- 1