Papers
9
Total Citations
59
H-Index
5
About
Abderrahim Waga is a leading researcher in autonomous mobile robotics, specializing in navigation, path planning, and obstacle avoidance. His work bridges traditional techniques with cutting-edge artificial intelligence, including deep learning and large language models. Waga’s major contributions include a comprehensive survey on autonomous navigation (2025, 10 citations) that systematically categorizes methods from graph-based algorithms to modern AI-driven approaches, and an efficient machine learning framework for end-to-end navigation (2024, 10 citations). He introduced a novel deep hybrid model for real mobile robots (2024, 9 citations) and a new sampling strategy to enhance path planning algorithms like RRT* (2022, 8 citations). His research also explores fuzzy logic for humanoid robot obstacle avoidance (2021, 6 citations) and waypoint-guided trajectory planning using GPT-4.1 mini (2025, 5 citations). With over 50 citations across his most-cited works, Waga’s impact is evident in his ability to integrate classical robotics with emerging AI paradigms. His notable achievements include developing supervised learning-based navigation systems and deep imitation learning for optimal policy acquisition, making him a pivotal figure in advancing autonomous robot capabilities.
Research Focus
Key Achievements
Top Papers
- 1
- 2Efficient autonomous navigation for mobile robots using machine learning10 citations · 2024
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- 5Fuzzy logic obstacle avoidance by a NAO robot in unknown environment6 citations · 2021
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- 8Challenges and Constraints in Trajectory Planning for Autonomous Robots3 citations · 2025
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