Toufik Mzili

Papers

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About

Toufik Mzili is a rising researcher in autonomous robotics, with a focus on trajectory planning and intelligent navigation systems. His work addresses fundamental challenges in enabling mobile robots to operate efficiently in large, complex environments. Mzili’s most-cited paper, “Waypoint-guided trajectory planning for mobile robots using GPT-4.1 mini and ensemble learning-based action prediction” (2025, 5 citations), introduces a novel hybrid approach that leverages large language models alongside ensemble learning to overcome the memory and computational bottlenecks of traditional path planning algorithms. This work demonstrates a creative integration of AI techniques to enhance real-time decision-making in autonomous systems. In his second highly cited paper, “Challenges and Constraints in Trajectory Planning for Autonomous Robots” (2025, 3 citations), Mzili systematically analyzes the limitations of current planning methods, providing a critical framework for future research. By combining theoretical analysis with cutting-edge AI applications, Mzili is contributing to the next generation of more adaptive and efficient autonomous robots. His early citation impact signals growing recognition in the robotics community, positioning him as a promising voice in the evolution of autonomous navigation.