Jawad Abdouni
Papers
8
Total Citations
53
H-Index
5
About
Jawad Abdouni is a leading researcher in autonomous navigation for mobile robots, bridging traditional techniques with cutting-edge machine learning and large language models. His work addresses the core challenges of path planning, obstacle avoidance, and trajectory generation, aiming to create fully autonomous systems that require minimal human intervention. Abdouni’s major contributions include a comprehensive survey (2025, 10 citations) that maps the evolution from graph-based methods to deep learning and LLM-driven approaches, and an efficient machine learning framework (2024, 10 citations) that simplifies the complex subtasks of navigation. He introduced a novel deep hybrid model for end-to-end navigation (2024, 9 citations) and a new sampling strategy to enhance path planning algorithms like RRT* (2022, 8 citations). More recently, he pioneered the use of GPT-4.1 mini for waypoint-guided trajectory planning (2025, 5 citations), showcasing the integration of generative AI into robotics. With over 50 total citations across his publications, Abdouni’s work is shaping the future of autonomous systems, offering scalable, intelligent solutions for real-world mobile robot deployment.
Research Focus
Key Achievements
Top Papers
- 1
- 2Efficient autonomous navigation for mobile robots using machine learning10 citations · 2024
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- 7Challenges and Constraints in Trajectory Planning for Autonomous Robots3 citations · 2025
- 8