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About

Fateh Seghir is a rising researcher in autonomous mobile robotics, with a focused expertise in path planning and navigation. His work addresses the critical challenge of enabling robots to move efficiently and safely through complex environments. Seghir’s key contributions lie in the development of hybrid and bio-inspired algorithms. Notably, his 2024 paper on a combined A-Star and Artificial Potential Field method (3 citations) introduces a powerful approach that merges global route optimization with local reactive control, overcoming the limitations of each technique used in isolation. He further advanced the field with a Particle Swarm Optimization (PSO)-based global path planner (2 citations), demonstrating how swarm intelligence can effectively navigate cluttered static environments. His most recent work in 2025 (2 citations) tackles the practical challenge of trajectory tracking and obstacle avoidance using a novel Fuzzy Dynamic Feedback Linearization technique, aiming to reduce computational demands while maintaining precision. Through these interconnected studies, Seghir is systematically building a more robust and efficient navigation toolkit, establishing himself as an innovative voice in the next generation of mobile robotics research.