Mostafa Azarkaman

Papers

Total Citations

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About

Mostafa Azarkaman is a researcher in robotics and artificial intelligence, with a primary focus on bipedal locomotion and humanoid robot control. His work addresses one of the most challenging problems in robotics: achieving stable, human-like walking. Azarkaman’s major contributions lie in the application of evolutionary algorithms, particularly Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), to optimize gait generation. His most cited paper, “Biped robot joint trajectory generation using PSO evolutionary algorithm” (2013, 11 citations), explores the use of Central Pattern Generators (CPGs) to produce complex, nonlinear oscillation patterns for stable walking. This work, along with his follow-up study “Evaluating GA and PSO evolutionary algorithms for humanoid walk pattern planning” (2014, 3 citations), systematically compares these optimization methods, offering valuable insights for researchers seeking efficient, adaptive control strategies. While his citation counts reflect a focused, emerging impact, Azarkaman’s research is notable for bridging bio-inspired CPG models with computational intelligence, providing a foundation for more autonomous and robust humanoid robots. His work is particularly relevant for students and engineers interested in evolutionary robotics, gait synthesis, and the intersection of neural-inspired control and optimization.