Papers
12
Total Citations
544
H-Index
6
About
Said Benhlima is a leading researcher in autonomous mobile robotics, specializing in path planning, obstacle avoidance, and intelligent navigation systems. His major contributions lie at the intersection of evolutionary algorithms, multi-agent systems, and reinforcement learning, where he has pioneered novel approaches to enable robots to navigate complex environments autonomously. His most influential work, a 2018 study on genetic algorithm-based path planning for autonomous mobile robots, has garnered 466 citations, establishing a foundational method for solving static environment navigation problems through an improved crossover operator. Benhlima further advanced the field by integrating holonic multi-agent architectures with collaborative Q-learning, introducing innovative concepts such as dual Q-tables (Q-Master and Q-Embedded) to enhance learning efficiency. His recent research extends into deep learning and large language models, including a comprehensive 2025 survey on autonomous navigation that bridges traditional techniques with modern AI, and pioneering work on waypoint-guided trajectory planning using GPT-4.1 mini. With over 530 total citations and a portfolio spanning from fuzzy logic control to deep imitation learning, Benhlima’s work continues to shape the future of intelligent, adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning466 citations · 2018
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- 5Efficient autonomous navigation for mobile robots using machine learning10 citations · 2024
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- 8Fuzzy logic obstacle avoidance by a NAO robot in unknown environment6 citations · 2021
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