Mohammad Aghaabbasloo

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About

Mohammad Aghaabbasloo’s research centers on bipedal locomotion and humanoid robotics, with a particular focus on optimizing walking patterns through evolutionary algorithms. His major contributions lie in applying computational intelligence techniques—specifically Particle Swarm Optimization (PSO) and Genetic Algorithms (GA)—to generate stable, human-like gait trajectories. In his most-cited work, “Biped robot joint trajectory generation using PSO evolutionary algorithm” (2013, 11 citations), he demonstrated how PSO can effectively tune Central Pattern Generator (CPG) parameters to produce smooth, adaptive walking motions. He extended this line of inquiry in “Evaluating GA and PSO evolutionary algorithms for humanoid walk pattern planning” (2014), providing a comparative analysis that highlighted the strengths and trade-offs of each algorithm for real-time gait planning. By bridging evolutionary computation with biomechanical modeling, Aghaabbasloo’s work offers practical pathways toward more autonomous and energy-efficient humanoid robots. His research is especially valuable for students and engineers seeking to understand how nature-inspired optimization can solve complex control problems in robotics, making his contributions a solid foundation for further exploration in legged locomotion and intelligent motion planning.