Assef Jafar

Higher Institute for Applied Sciences and Technology

Papers

Total Citations

H-Index

About

Assef Jafar is a robotics researcher focused on advancing autonomous navigation and robotic manipulation through intelligent algorithms and machine learning. His work centers on three key areas: path planning for mobile robots, depth perception using deep learning, and kinematic modeling of parallel manipulators. In his most cited work, "An Improved Path Planning Algorithm for Indoor Mobile Robots in Partially-Known Environments" (4 citations), Jafar proposed a two-stage algorithm using restricted tangent graphs to enable robots to navigate dynamic, crowded indoor spaces efficiently. He further contributed to computer vision by developing a real-time 2D LiDAR system from monocular images using deep learning, reducing computational overhead by predicting distance vectors rather than full image matrices. Most recently, Jafar tackled the forward geometric model of a 6-RSU parallel manipulator using a modified NARX Bayesian neural network, enhancing model accuracy and uncertainty quantification. His research demonstrates a commitment to bridging theoretical robotics with practical, resource-efficient solutions, making autonomous systems more reliable and accessible in real-world environments.