Murugappan Elango
Papers
6
Total Citations
169
H-Index
3
About
Murugappan Elango is a leading researcher in multi-robot systems, with a focused expertise in balanced task allocation and optimization. His foundational work, "Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms" (2010), has garnered 134 citations, establishing him as a key figure in the field. Elango’s major contribution lies in addressing the critical challenge of workload equity among robots—moving beyond simple distance minimization to ensure that robots share tasks and travel distances fairly. This approach, detailed in his 2021 paper on performance analysis of clustering methods, models the Multi-Robot Task Allocation (MRTA) problem with a balance constraint to improve overall system utilization and completion time. He has also explored hybrid methodologies that combine clustering with auction-based mechanisms to simultaneously minimize path length and balance workloads. Beyond core MRTA, Elango has applied decision-making tools like TOPSIS to select humanoid robots for rescue operations, demonstrating the real-world relevance of his work. His research, spanning from simulation studies to algorithmic development, provides foundational solutions for deploying efficient, fair, and scalable multi-robot teams in complex environments.
Research Focus
Key Achievements
Top Papers
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- 4Selection of a Best Humanoid Robot Using “TOPSIS” for Rescue Operation3 citations · 2022
- 5Balancing multi-robot prioritized task allocation: A simulation approach3 citations · 2011
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