Papers
2
Total Citations
19
H-Index
2
About
Rega Rajendra is a robotics researcher whose work focuses on the optimization of bipedal locomotion, particularly the critical double support phase of walking. His major contributions lie in applying and comparing advanced metaheuristic algorithms—specifically, Genetic Algorithms and Particle Swarm Optimization—to solve multi-objective optimization problems in gait planning. By systematically analyzing how these algorithms can balance competing objectives like stability, energy efficiency, and smooth motion, Rajendra has provided foundational insights into the design of more natural and efficient walking patterns for humanoid robots. His most cited work, "Analysis of double support phase of biped robot and multi-objective optimization using genetic algorithm and particle swarm optimization algorithm" (2015), has garnered 16 citations, reflecting its relevance to researchers tackling similar control challenges. While his publication record is concise, it demonstrates a clear, focused trajectory: from an early exploration of algorithm comparison (2011) to a more refined, application-oriented study. Rajendra’s research is particularly valuable for students and engineers seeking practical, algorithm-driven approaches to the complex, real-world problem of stable bipedal walking.
Research Focus
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Top Papers
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