Ali Amamou
Papers
3
Total Citations
36
H-Index
3
About
Ali Amamou is a researcher whose work sits at the intersection of robotics, autonomous navigation, and energy efficiency. His primary research areas include path planning, localization, and energy optimization for self-guided vehicles (SGVs) and automated guided vehicles (AGVs) operating in indoor environments. Amamou’s most notable contribution is an efficient large-map global path planning algorithm for robot navigation, which has already garnered 18 citations since its publication in 2024, signaling its immediate impact on the field. He also developed an energy-efficient local path planning method that accounts for load position, a critical innovation for extending battery life in industrial SGVs—a paper that has earned 14 citations. Additionally, Amamou proposed a Kalman-Particle hybrid filter to improve AGV localization accuracy in complex indoor settings, a foundational step toward reliable autonomous material handling. His work directly addresses real-world industrial challenges, such as reducing energy consumption and enhancing navigation precision, making him a promising voice in the evolution of intelligent, self-guided robotic systems.
Research Focus
Key Achievements
Top Papers
- 1An efficient indoor large map global path planning for robot navigation18 citations · 2024
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