Nirmala Sharma
Papers
1
Total Citations
5
H-Index
1
About
Nirmala Sharma is a researcher specializing in computational intelligence and robotics, with a particular focus on optimization algorithms for autonomous systems. Her work centers on developing efficient path planning techniques for robots, where she has made notable contributions through the application of metaheuristic algorithms. Her most-cited paper, "Shuffled teaching learning-based algorithm for solving robot path planning problem" (2020), has garnered 5 citations and introduces an innovative approach that enhances the performance of teaching-learning-based optimization by incorporating a shuffling mechanism. This method improves solution diversity and convergence speed, addressing critical challenges in real-time robotic navigation. Sharma's research bridges theoretical algorithm design with practical engineering applications, offering robust solutions for complex, dynamic environments. Her work is particularly relevant for students and researchers in robotics, artificial intelligence, and optimization, as it demonstrates how nature-inspired algorithms can be adapted to solve real-world problems. With a growing citation impact, Sharma continues to advance the field of intelligent robotics, laying groundwork for more autonomous and adaptive systems.
Research Focus
Key Achievements
Top Papers
- 1