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About

Chaymaa Lamini is a leading researcher in autonomous mobile robotics, specializing in path planning and obstacle avoidance through hybrid artificial intelligence techniques. Her work masterfully integrates genetic algorithms, reinforcement learning, multi-agent systems, and fuzzy logic to create robust, adaptive navigation solutions for robots in both static and unknown environments. Lamini’s seminal 2018 paper, "Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning," has garnered 466 citations, establishing a foundational method for optimizing collision-free routes using an improved crossover operator. She further advanced the field by pioneering collaborative Q-learning within a Holonic Multi-Agent System (H-MAS), introducing innovative concepts like dual Q-tables (Q-Master and Q-Embedded) to enhance learning efficiency. Her research extends to humanoid robotics, as demonstrated in her 2021 work on fuzzy logic obstacle avoidance for the NAO robot. Lamini’s contributions have significantly impacted the development of intelligent, cooperative robotic systems, enabling more efficient and autonomous navigation in complex environments. Her work remains highly influential for researchers and students exploring the intersection of evolutionary computation, multi-agent coordination, and real-world robotic applications.