Mohit Ahuja

Papers

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About

Mohit Ahuja’s research lies at the intersection of swarm robotics, fuzzy logic, and autonomous exploration, with a focus on enabling multi-agent systems to navigate complex, unknown environments. His most-cited work, “Fuzzy Counter Ant Algorithm for Maze Problem” (2010, 4 citations), introduces a novel approach that integrates fuzzy logic into ant-inspired swarm algorithms, allowing a team of robots to collaboratively explore and exploit obstacle-rich 2D mazes. This contribution addresses a fundamental challenge in robotics: how to balance exploration and exploitation under uncertainty. By demonstrating that fuzzy decision-making can enhance the efficiency of swarm-based navigation, Ahuja’s work provides a scalable framework for applications ranging from search-and-rescue to automated mapping. Though early in its citation impact, the study has been recognized as a creative test bed for generalized maze problems, inspiring further research into hybrid intelligence systems. Ahuja’s efforts underscore a commitment to bridging theoretical algorithms with practical robotic deployment, offering valuable insights for students and researchers interested in adaptive, decentralized control.