Ashok Urlana

Rajiv Gandhi University of Knowledge Technologies

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About

Ashok Urlana is a researcher in swarm robotics and bio-inspired computation, with a primary focus on translating the subtle mating behaviors of butterflies into multi-robot coordination strategies. His work centers on the Butterfly Mating Optimization (BMO) algorithm, a meta-heuristic that models how butterflies communicate and converge during mate selection. Urlana’s major contribution lies in implementing this biological metaphor on physical robotic platforms, most notably through his "BflyBot" system. In his seminal 2018 paper, *BflyBot: Mobile robotic platform for implementing Butterfly mating phenomenon* (4 citations), he demonstrated how a swarm of simple robots could autonomously locate and pursue dynamic signal sources—such as chemical plumes or sound—by mimicking butterfly flight patterns. This work bridges the gap between theoretical swarm intelligence and practical, deployable robotics. His subsequent studies, including *Butterfly Inspired Multi-robotic Swarm for Signal Source Localization* (3 citations) and *Bflybots: A Novel Robotic-Swarm in Pursuit of Dynamic Signal Sources* (2 citations), further refined these algorithms for real-time target tracking. Though his citation counts are modest, Urlana’s research is notable for its originality in applying a less-explored biological model to a tangible engineering challenge, offering a fresh perspective for students and researchers interested in nature-inspired robotics and decentralized control systems.