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Shahed Shojaeipour is a robotics researcher whose work centers on vision-based mobile robot navigation, motion planning, and obstacle avoidance. His major contributions lie in developing cost-effective, computer vision-driven methods that allow mobile robots to perceive and traverse dynamic environments. Shojaeipour pioneered the use of webcams and laser emitters as accessible sensors for real-time path planning, integrating image processing with spatial decomposition techniques like Quad-Tree and Voronoi Diagrams. His most cited paper, “Vision-Based Mobile Robot Navigation Using Image Processing and Cell Decomposition” (2009, 15 citations), established a foundational approach for mapping workspaces and generating collision-free trajectories. Subsequent works, including “Motion planning for mobile robot navigation using combine Quad-Tree Decomposition and Voronoi Diagrams” (2010, 12 citations) and “Webcam-based mobile robot path planning using Voronoi diagrams and image processing” (2010, 10 citations), further refined these algorithms, demonstrating how low-cost hardware could achieve reliable navigation. His research on laser-based rangefinding (“Robot path obstacle locator using webcam and laser emitter,” 2010, 8 citations) provided a practical solution for distance measurement, enhancing robot autonomy. Collectively, Shojaeipour’s work has accumulated over 47 citations, influencing the development of affordable, vision-guided robotic systems for research and education.