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About

Fadhila Lachekhab is an emerging researcher specializing in autonomous robotics, intelligent navigation systems, and machine learning-driven path planning. Her work sits at the intersection of fuzzy logic, reinforcement learning, and robotic control, with a focus on enabling mobile robots and unmanned aerial vehicles (UAVs) to navigate complex, unknown environments efficiently and safely. Among her most notable contributions is her development of a reactive navigation framework that fuses fuzzy control with Q-learning — a hybrid approach that addresses both obstacle avoidance and goal-seeking behaviors in mobile robots. This work, which has garnered consistent citation interest since its 2019 publication, demonstrates practical experimental validation, strengthening its credibility within the robotics community. Her earlier 2015 research on fuzzy actor-critic learning further established her foundation in combining heuristic and adaptive learning techniques for robot control. More recently, Lachekhab has expanded her scope to UAV path planning, with a 2025 review paper on machine learning paradigms in this domain already attracting early citations — signaling growing relevance as UAV applications proliferate across military, civilian, and industrial sectors. With a steadily building citation record across multiple research threads, her work represents a meaningful contribution to the field of intelligent autonomous systems.