Yogita Gigras
Papers
8
Total Citations
89
H-Index
6
About
Yogita Gigras is a leading researcher in the field of robotic path planning, with a primary focus on developing and comparing metaheuristic optimization algorithms for autonomous navigation. Her work addresses the fundamental challenge of enabling mobile robots to find collision-free, optimal paths from source to destination in dynamic environments while minimizing distance, time, and computational resources. Gigras has made significant contributions by pioneering the application of genetic algorithms, ant colony optimization (ACO), particle swarm optimization (PSO), and hybrid ACO-PSO techniques to path planning problems. Her most cited paper, "Robotic Path Planning using Genetic Algorithm in Dynamic Environment" (2014, 31 citations), demonstrates a novel approach where genetic algorithms are applied at specific points in the problem space rather than the entire space, improving efficiency. She has also conducted comprehensive comparative studies between various metaheuristic algorithms, including bee colony optimization, bat algorithm, cuckoo search, and flower pollination algorithm, providing valuable insights into their relative performance. Her research, spanning from 2012 to 2019, has accumulated over 89 citations, establishing her as a respected authority in autonomous robotic navigation and optimization techniques.
Research Focus
Key Achievements
Top Papers
- 1Robotic Path Planning using Genetic Algorithm in Dynamic Environment31 citations · 2014
- 2A hybrid ACO-PSO technique for path planning15 citations · 2015
- 3Metaheuristic Algorithm for Robotic Path Planning12 citations · 2014
- 4Ant Colony Based Path Planning Algorithm for Autonomous Robotic Vehicles10 citations · 2012
- 5
- 6A Comparison between Bat Algorithm andCuckoo Search for Path Planning7 citations · 2015
- 7Robotic Path Planning Using Flower Pollination Algorithm4 citations · 2019
- 8Path Planning Problem3 citations · 2014