Shubham Dutta
Papers
1
Total Citations
22
H-Index
1
About
Shubham Dutta is a researcher whose work lies at the intersection of robotics, optimization, and artificial intelligence. His primary research focuses on developing intelligent path-planning algorithms for autonomous mobile robots, enabling them to navigate efficiently in both known and unknown environments. Dutta’s most notable contribution is the introduction of a novel hybrid approach that integrates oppositional-based learning (OBL) with the invasive weed optimization (IWO) algorithm, creating the oppositional invasive weed optimization (OIWO) method. This technique significantly enhances the convergence speed and solution quality for optimal trajectory planning, addressing a critical challenge in autonomous navigation. His seminal 2018 paper on this topic has garnered 22 citations, reflecting its impact on the field of robotics and computational intelligence. By combining bio-inspired metaheuristics with advanced learning strategies, Dutta has provided a robust framework for mobile robot path planning, paving the way for more adaptive and efficient autonomous systems. His work continues to influence researchers and engineers working on intelligent control and optimization in robotics.
Research Focus
Key Achievements
Top Papers
- 1