Yasamin Borhani
Papers
1
Total Citations
3
H-Index
1
About
Yasamin Borhani’s research lies at the intersection of robotics, reinforcement learning, and computer vision, with a focus on enabling autonomous mobile robots to navigate dynamic environments safely and efficiently. Her most-cited work, “Reinforcement Learning based Sequential Controller for Mobile Robots with Obstacle Avoidance” (2022), introduces a novel framework that integrates a YOLO-v4-based object detection system with a depth camera in ROS to identify obstacles in real-time, then employs a reinforcement learning sequential controller for adaptive path planning. This approach addresses a critical challenge in robotics—balancing goal-directed navigation with reactive obstacle avoidance—and has garnered 3 citations as a foundational contribution to the field. Borhani’s work is notable for its practical implementation in real-world robotic systems, bridging the gap between simulation and deployment. Her research demonstrates how deep learning and reinforcement learning can be synergized to create more intelligent, responsive robots. As an emerging scholar, Borhani’s contributions are paving the way for safer autonomous navigation in applications ranging from warehouse logistics to service robotics, making her a promising voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
- 1