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About

Nizar Rokbani is a leading researcher in the intersection of robotics and computational intelligence, with a primary focus on solving the complex inverse kinematics problems that underpin robotic motion, path planning, and trajectory optimization. His most significant contribution lies in pioneering the application of nature-inspired metaheuristic algorithms—particularly Particle Swarm Optimization (PSO) and the Firefly Algorithm—to develop efficient, heuristic-based inverse kinematic solvers. His seminal 2013 paper, "Inverse Kinematics Using Particle Swarm Optimization, A Statistical Analysis," has garnered 95 citations and established a benchmark for evaluating PSO variants in this domain. Rokbani has further advanced the field by introducing novel algorithmic variants, including a fitness-based multi-objective modified PSO for 5-DOF robotic arms (47 citations) and the Beta distributed β-PSO (19 citations), which explicitly manages the exploration-exploitation trade-off. Beyond inverse kinematics, his work extends to biped robot control and gait generation, as seen in his 2010 paper (35 citations) and the architectural proposal for the IZiman humanoid. With over 300 total citations across his most-cited works, Rokbani’s research is distinguished by its practical, low-cost prototyping approach—exemplified by his use of educational robotics kits—and its consistent drive to translate bio-inspired algorithms into robust, real-world robotic solutions.