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About

Alaa Hassan Shabeeb is a robotics and automation researcher whose work spans two interconnected domains: mobile robot navigation and robotic manipulator kinematics. His research has made meaningful contributions to the field of intelligent path planning, particularly through the application of swarm intelligence algorithms to autonomous mobile robots. Shabeeb has explored multiple optimization techniques, including Particle Swarm Optimization (PSO) with inertia weight variants and the Grey Wolf Optimization (GWO) algorithm, developing efficient path planners capable of navigating complex, obstacle-laden environments with minimal computational overhead. His most cited work (2020) on PSO-based autonomous navigation has garnered 10 citations, reflecting growing interest in bio-inspired approaches to robot motion planning. Complementing this, Shabeeb has contributed to industrial robotics through rigorous kinematic analysis of 5-DOF robot manipulators. His papers on both forward kinematics using the Denavit-Hartenberg scheme and inverse kinematics via closed-form solutions address fundamental challenges in predicting and controlling robotic end-effector positioning. Together, his body of work — accumulating over 37 citations — bridges theoretical modeling and practical autonomous systems, making his research particularly valuable for students and engineers working at the intersection of optimization algorithms and modern robotics.