Kusum Gupta
Papers
3
Total Citations
34
H-Index
3
About
Kusum Gupta is a researcher focused on advancing robotic path planning through metaheuristic optimization algorithms. Her work addresses the critical challenge of enabling robots to navigate from source to destination with minimal distance, time, and energy expenditure. Gupta’s most influential contribution is her hybrid ACO-PSO technique for path planning (2015, 15 citations), which combines ant colony optimization with particle swarm optimization to improve navigation efficiency. She has also conducted comparative studies of metaheuristic algorithms, demonstrating that ant colony optimization outperforms particle swarm optimization in robotic path planning (2014, 12 citations). Her research extends to evaluating bat algorithm and cuckoo search for path planning (2015, 7 citations), systematically comparing their performance in minimizing moves and iterations. Through these studies, Gupta has established a framework for selecting and hybridizing nature-inspired algorithms to solve complex navigation problems. Her work provides practical guidance for robotics engineers and researchers seeking efficient, computationally tractable solutions for autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1A hybrid ACO-PSO technique for path planning15 citations · 2015
- 2Metaheuristic Algorithm for Robotic Path Planning12 citations · 2014
- 3A Comparison between Bat Algorithm andCuckoo Search for Path Planning7 citations · 2015