Yassine Bouslimani
Papers
9
Total Citations
60
H-Index
5
About
Yassine Bouslimani is a robotics and automation researcher whose work spans industrial robotics, computer vision, autonomous systems, and human-robot interaction. Based at the Université de Moncton's Robotics, Electronics and Industry 4.0 Laboratory, Bouslimani has made significant contributions to advancing intelligent robotic systems for both industrial and domestic applications. His most influential work focuses on precise robot calibration and vision-guided manipulation, including a widely recognized tool center point calibration method for six-degree-of-freedom industrial robots (16 citations) and multiple vision-based object detection systems for KUKA and FANUC robot arms. Notably, Bouslimani has applied deep learning techniques, including YOLOv7, to automate lobster processing — a critical challenge for Canada's Atlantic seafood industry, which generates over CAD 800 million in export revenue. His research extends into 3D mobile mapping using SLAM-based georeferencing, omnidirectional autonomous platforms, and sensor fusion combining electromagnetic and inertial technologies for teleoperation. Beyond industrial applications, Bouslimani has explored assistive robotics, developing a smart home management robot designed to support elderly people through AI-driven voice interaction. With a growing citation record across multiple domains, his research bridges fundamental robotics engineering with real-world societal and industrial impact.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Smart Assistant Robot for Smart Home Management8 citations · 2020
- 4
- 5Omnidirectional Platform for Autonomous Mobile Industrial Robot5 citations · 2021
- 6
- 7Detection and Location of Sheet Metal Parts for Industrial Robots4 citations · 2021
- 8Electromagnetic and inertial motion sensor fusion3 citations · 2021
- 9Location and Vision Techniques to Control a KUKA KR6 R900 Sixx Robot Arm2 citations · 2020