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About

Zulkifli Mohamed is a robotics researcher whose work focuses on developing intelligent, assistive humanoid robots, particularly for elderly care. His most cited paper, "Development of a New Mobile Humanoid Robot for Assisting Elderly People" (32 citations), introduces a robot equipped with visual sensors and laser range finders for object recognition and obstacle detection, directly addressing the challenges of aging populations. Mohamed’s core contributions lie in optimizing humanoid robot motion and control using multi-objective evolutionary algorithms and neural networks. His research on arm motion generation, as seen in papers like "Humanoid Robot Arm Performance Optimization Using Multi Objective Evolutionary Algorithm" (12 citations) and "Adaptive Humanoid Robot Arm Motion Generation by Evolved Neural Controllers" (3 citations), formulates motion planning as an optimization problem to satisfy multiple constraints, including obstacle avoidance and dynamic environments. He also explores multi-robot coordination, as in "Multiple Robots Formation–A Multiobjective Evolution Approach" (7 citations), and sensor-based navigation using platforms like the Nao robot (11 citations). With over 70 total citations, Mohamed’s work bridges evolutionary computation, neural control, and practical robotics, advancing the development of adaptive, safe, and efficient humanoid systems for real-world assistive applications.