Prajit KrisshnaKumar
Papers
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1
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About
Prajit KrisshnaKumar is a pioneering researcher in decentralized multi-robot systems, with a focus on scalable swarm intelligence and real-time decision-making under uncertainty. His work bridges the gap between theoretical Bayesian inference and practical robotic deployment, particularly in high-stakes domains like hazard localization and search-and-rescue. KrisshnaKumar’s most notable contribution is the development of learning-based down-sampling techniques for Bayes-Swarm search, enabling swarms to maintain real-time performance without sacrificing accuracy—a critical advancement for resource-constrained robots. His 2025 paper, "Learning-Based Real-Time Down-Sampling for Scalable Decentralized Decision-Making in Bayes-Swarm Search," introduces a novel framework that dynamically reduces computational load while preserving belief model fidelity, achieving efficient coordination even in large-scale swarms. Though early in its citation trajectory, this work has already garnered attention for its practical impact on autonomous exploration. KrisshnaKumar’s research empowers swarms to act as cohesive, intelligent units, pushing the boundaries of what decentralized systems can achieve in dynamic, uncertain environments. His contributions are shaping the future of collaborative robotics, offering scalable solutions for real-world challenges.
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