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About

Sarah Barman is a pioneering researcher in autonomous swarm robotics, with a focus on developing decentralized control strategies for multi-robot systems. Her work centers on the design of reactive coordination algorithms and adaptive lattice formations for mobile robotic swarms, particularly for surveillance and target localization in complex environments. Barman's major contributions include the introduction of physics-inspired artificial local forces to govern the spatial organization of VTOL UAV swarms, enabling autonomous ground area surveillance without centralized control. She further advanced the field by proposing a fully autonomous swarm-based solution for multi-target localization and monitoring, with applications to counter-IED operations, validated through both 3D simulations and laboratory experiments. Her research demonstrates how varying parameters in local force laws can optimize swarm behavior, and she introduced a learning algorithm to enhance coordination efficiency. While her most-cited papers have garnered 7, 5, and 4 citations respectively, they represent foundational work in reactive swarm coordination that has influenced subsequent studies in autonomous surveillance and search-and-rescue robotics. Barman's contributions are particularly notable for bridging theoretical control laws with practical, real-world robotic applications.