Arnab Ghosh
Papers
2
Total Citations
21
H-Index
2
About
Arnab Ghosh is a researcher in multi-robot systems and swarm intelligence, with a focused expertise in cooperative manipulation and optimization. His primary research area addresses the complex challenge of enabling multiple robots to work together on physical tasks, specifically the multi-robot cooperative box-pushing problem. Ghosh’s major contribution is a novel formulation of this problem as a multi-objective optimization task, solved using a modified Multi-objective Particle Swarm Optimization (MOPSO) technique. Unlike prior approaches that often limited box movement to simple translation, his method innovatively allows for both turning and translation of the object during transport, significantly enhancing maneuverability and realism. This work, detailed in his most-cited paper from 2012, has garnered over 15 citations, demonstrating its foundational impact in the field. By bridging the gap between theoretical optimization algorithms and practical robotic coordination, Ghosh’s research provides a robust framework for autonomous systems to execute complex, real-world collaborative tasks. His achievements highlight a key advancement in making multi-robot teams more adaptive and efficient for applications in logistics, manufacturing, and exploration.
Research Focus
Key Achievements
Top Papers
- 1
- 2