Yacine Slimani
Papers
1
Total Citations
2
H-Index
1
About
Yacine Slimani is a researcher at the forefront of autonomous navigation and intelligent robotics, with a primary focus on cost-effective, real-time perception systems. His work centers on developing practical solutions for obstacle detection and avoidance in autonomous ground vehicles (AGVs), bridging the gap between advanced computer vision and affordable hardware. Slimani’s major contribution is the integration of YOLOv8, a state-of-the-art deep learning model, with RGB-D sensors like the Microsoft Kinect V1 to create a robust, low-cost navigation system. His most-cited paper, "Cost-Effective Real-Time Obstacle Detection and Avoidance for AGVs using YOLOv8 and RGB-D Sensors" (2025), has already garnered 2 citations, signaling early impact in the field. This work demonstrates how combining depth data with real-time object detection can enable AGVs to navigate complex environments without expensive LiDAR systems, making autonomous technology more accessible. Slimani’s research is particularly notable for its emphasis on practical deployment, offering a scalable solution for industrial and service robotics. His achievements highlight a commitment to democratizing advanced robotic capabilities, and his ongoing work promises to further refine the efficiency and reliability of autonomous systems in real-world settings.
Research Focus
Key Achievements
Top Papers
- 1