H. S. Behera
Papers
11
Total Citations
646
H-Index
9
About
H. S. Behera is a prominent researcher specializing in autonomous robotics, swarm intelligence, and computational intelligence, with a particular focus on multi-robot path planning and optimization algorithms. His work has made significant contributions to solving one of robotics' most challenging problems: enabling multiple robots to navigate complex, dynamic environments efficiently and autonomously. Behera's most impactful contribution is his development of hybrid optimization frameworks, most notably a fusion of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA), which garnered 283 citations and demonstrated remarkable improvements in multi-robot trajectory optimization. He has consistently pushed boundaries by enhancing classical algorithms — improving Q-learning's notoriously slow convergence rates, refining PSO with perturbed velocity mechanisms, and augmenting GSA with memory and social cognition factors borrowed from PSO. With a body of work accumulating over 640 citations, Behera has addressed critical limitations in existing approaches, including computational inefficiency, slow convergence, and poor performance in cluttered or dynamic environments. His research, spanning from foundational improvements to Q-learning to sophisticated hybrid metaheuristic methods, has established him as a valuable contributor to the intelligent robotics and computational optimization communities, providing practical and scalable solutions for real-world autonomous navigation challenges.
Research Focus
Key Achievements
Top Papers
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- 5An Improved Q-learning Algorithm for Path-Planning of a Mobile Robot23 citations · 2012
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- 7An improved particle swarm optimization for multi-robot path planning18 citations · 2016
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