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About

Anirban Ghosh is a leading researcher in multi-robot systems, specializing in the intersection of autonomous navigation, communication constraints, and information-driven path planning. His most impactful work tackles the challenge of coordinating robot teams to efficiently collect environmental data while maintaining continuous connectivity—a critical problem for search-and-rescue, environmental monitoring, and exploration missions. In his highly cited 2019 paper (37 citations), Ghosh introduced a novel approach to multi-robot informative path planning that ensures robots remain within communication range throughout their mission, maximizing information gain in polygonal environments. He further advanced the field by addressing communication efficiency in large-scale networks (2020) and developing methods for unknown environments through continuous region partitioning (2019). Earlier in his career, Ghosh demonstrated versatility by applying fuzzy-genetic algorithms to mobile robot navigation among static obstacles (2003, 23 citations), showcasing his long-standing commitment to intelligent robotics. His work bridges theoretical optimization with practical deployment, offering scalable solutions for real-world multi-robot coordination. With a career spanning nearly two decades, Ghosh continues to shape how autonomous systems collaborate under real-world constraints.