Papers
5
Total Citations
65
H-Index
4
About
Vikram Garg is a rising researcher in swarm robotics and cooperative multi-robot systems, with a focused expertise in bio-inspired algorithms for autonomous target search and rescue. His work centers on adapting collective intelligence models—particularly Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Fruit Fly algorithms—to enable robot teams to efficiently locate and track targets in dynamic, communication-limited environments. Garg’s major contributions include the development of AERPSO, an adaptive exploration robotic PSO algorithm that achieved 33 citations for its novel approach to cooperative multiple target searching, and E2RGWO, an exploration-enhanced variant of GWO designed for robotic swarms. His research addresses critical real-world challenges: in disaster scenarios where every minute counts, his algorithms help robotic teams coordinate with minimal intercommunication to rapidly find survivors. With over 65 total citations across his most-cited works from 2021-2022, Garg’s comparative analyses of fruit fly-inspired multi-robot strategies provide valuable benchmarks for the search and rescue community. His work bridges theoretical swarm intelligence with practical, life-saving applications—offering creative, scalable solutions for humanitarian robotics.
Research Focus
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