Imane Arrouch
Papers
1
Total Citations
36
H-Index
1
About
Dr. Imane Arrouch is a leading researcher in autonomous robot navigation and sensor fusion, with a focus on enhancing safety in close-proximity environments. Her most-cited work, "Close Proximity Time-to-collision Prediction for Autonomous Robot Navigation: An Exponential GPR Approach" (2022, 36 citations), pioneers a novel method that fuses X-band Doppler radar with infrared sensors to predict time-to-collision (TTC) with remarkable precision. By addressing the poor ranging performance of infrared sensors through an exponential Gaussian Process Regression (GPR) model, Dr. Arrouch enables robots to detect obstacle speed and direction more reliably, even in cluttered or dynamic settings. This contribution is critical for applications ranging from warehouse automation to assistive robotics, where split-second decisions can prevent accidents. Her research bridges the gap between theoretical sensor fusion and practical deployment, earning recognition for its impact on real-time collision avoidance. Dr. Arrouch’s work continues to shape the future of autonomous systems, offering scalable solutions that enhance robot perception and responsiveness in high-stakes scenarios.
Research Focus
Key Achievements
Top Papers
- 1