Abhinav Aggarwal

Papers

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About

Abhinav Aggarwal’s research lies at the intersection of collective robotics, swarm intelligence, and autonomous exploration, with a focus on designing resilient algorithms for real-world, high-stakes environments. His major contributions include a rigorous comparative analysis of central-place foraging algorithms (CPFAs), demonstrating that naive implementations can lead to catastrophic inefficiencies—a finding with direct implications for planetary exploration, automated mining, and search-and-rescue operations. This work, “Ignorance is Not Bliss,” has garnered 8 citations and is recognized for challenging conventional assumptions in multi-robot coordination. Aggarwal also developed LoCUS, a loss-tolerant algorithm that enables drone swarms to survey volcanic plumes by following gas concentration gradients while coping with frequent drone failures. This pioneering approach, published in 2020 (6 citations), addresses a critical gap in hazardous environment monitoring. His work is notable for bridging theoretical swarm algorithms with practical deployment constraints, earning him recognition as a rising figure in resilient multi-robot systems. For students and researchers, Aggarwal’s research offers a compelling model of how rigorous algorithmic analysis can solve pressing environmental and industrial challenges.