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About

Pranab K. Muhuri is a leading researcher in computational intelligence and multi-robot systems, with a primary focus on optimizing task allocation and coalition formation in dynamic, resource-constrained environments. His major contributions center on developing immigrant-based adaptive genetic algorithms to solve the complex multi-robot coalition formation (MRCF) problem, where multiple robots must cooperate to execute tasks efficiently. His seminal 2016 work on "Multi-robot coalition formation problem: Task allocation with adaptive immigrants based genetic algorithms" has garnered 34 citations, establishing a foundational approach for adaptive task distribution in robotic swarms. Expanding on this, his 2017 paper on "Immigrants Based Adaptive Genetic Algorithms for Task Allocation in Multi-Robot Systems" (31 citations) further refined these methods to address computational challenges in assigning robot resources to tasks. Muhuri’s research also extends to path planning, as seen in his 2020 work on zone-based mobile robot navigation using genetic algorithms. His work is notable for bridging theoretical optimization with practical robotics applications, offering scalable solutions for autonomous systems. With a growing citation impact, Muhuri continues to influence the fields of evolutionary computation and cooperative robotics, making his research essential for students and engineers working on intelligent multi-agent coordination.