Pradipta Kumar Das
Veer Surendra Sai University of Technology, Institute of Engineering
Papers
32
Total Citations
1,149
H-Index
17
About
Pradipta Kumar Das is a prominent researcher in the fields of robotics, swarm intelligence, and computational optimization, with a particular focus on multi-robot path planning in complex and dynamic environments. Over more than a decade of sustained scholarship, Das has made significant contributions to solving one of robotics' most challenging problems: enabling multiple autonomous robots to navigate efficiently through cluttered and unpredictable spaces. His most influential work centers on hybridizing nature-inspired metaheuristic algorithms — including Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Q-learning, and Cuckoo Search — to develop smarter, faster path-planning solutions. His 2016 paper combining improved PSO with GSA has garnered 283 citations, establishing it as a landmark contribution to the field. He further advanced the domain by incorporating reinforcement learning frameworks, proposing improved Q-learning strategies that address computational bottlenecks inherent in classical approaches. Das's research trajectory — from early work on artificial immune systems and extended Q-learning (2010–2012) to sophisticated multi-algorithm hybridizations — reflects both depth and evolution in thinking. With a cumulative citation count exceeding 880 across his top publications, his work continues to serve as a foundational reference for researchers developing intelligent autonomous robotic systems worldwide.
Research Focus
Key Achievements
Top Papers
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- 8Artificial Immune System Based Path Planning of Mobile Robot30 citations · 2012
- 9Extended Q-Learning Algorithm for Path-Planning of a Mobile Robot29 citations · 2010
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