Ibrahim Kamel
Papers
8
Total Citations
132
H-Index
5
About
Ibrahim Kamel is an emerging robotics and autonomous systems researcher whose work centers on path planning, obstacle avoidance, and multi-robot coordination. His research addresses one of the field's most persistent challenges: the trade-off between computational speed and path quality in real-time robotic systems. Kamel has made notable contributions through systematic reviews of reinforcement learning-based dynamic obstacle avoidance and multi-robot path planning, with his 2023 survey on reinforcement learning for autonomous navigation accumulating an impressive 61 citations, signaling its rapid adoption as a key reference in the field. Beyond survey work, Kamel has developed original algorithmic solutions, including the Sobel Potential Field method for UAV navigation, a fluid dynamics-inspired approach to resolving local minima in artificial potential fields, and a swarm robotics framework integrating particle swarm optimization. His 2021 routing protocol approach to real-time path planning further demonstrates his commitment to practical, deployable solutions. More recently, he has explored applied robotics through vision-ultrasonic sensor fusion for agricultural maintenance robots. With over 130 cumulative citations across his publications, Kamel's growing body of work positions him as a promising contributor to the advancement of intelligent, autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning61 citations · 2023
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- 4Path Planning Techniques for Multi-robot Systems: A Systematic Review7 citations · 2023
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