Rahul Bhaumik
Papers
1
Total Citations
24
H-Index
1
About
Rahul Bhaumik is a robotics and optimization researcher whose work focuses on developing intelligent algorithms for multi-robot systems. His primary research areas include path planning, task allocation, and swarm intelligence, with applications in industrial automation and inspection. His most notable contribution is the application of the Teaching–Learning-Based Optimization (TLBO) algorithm to coordinate multiple robots for plant inspection tasks, as detailed in his highly cited 2021 paper (24 citations). This work addresses critical challenges in autonomous systems, such as minimizing energy consumption and maximizing coverage efficiency. Bhaumik’s research has practical implications for manufacturing, agriculture, and disaster response, where multi-robot coordination is essential. His achievements include advancing bio-inspired optimization techniques for real-world robotics problems, and his work is recognized for bridging theoretical algorithms with tangible engineering solutions. With a growing citation impact, Bhaumik continues to contribute to the fields of computational intelligence and autonomous systems, making his research valuable for students and engineers interested in the intersection of AI, robotics, and optimization.
Research Focus
Key Achievements
Top Papers
- 1