Mohamed Hasan

University of Leeds, Egypt-Japan University of Science and Technology

Papers

Total Citations

H-Index

About

Mohamed Hasan’s research lies at the intersection of robotics, computer vision, and human-robot interaction, with a core focus on enabling robots to perceive and manipulate their environments with human-like efficiency. His most impactful work, “Human-like Planning for Reaching in Cluttered Environments” (2020, 18 citations), addresses a fundamental challenge in robotics: how to plan reaching motions in high-dimensional, obstacle-dense spaces. By drawing inspiration from human motor strategies, Hasan’s approach offers a computationally efficient alternative to traditional random-sampling planners, which often struggle with complexity. This contribution is particularly significant for applications in assistive robotics and autonomous manipulation, where real-time, safe interaction with cluttered surroundings is critical. Earlier in his career, Hasan made notable advances in monocular visual SLAM, including the experimental verification of a direct depth computing technique (2012, 3 citations) and the development of a new closed-form solution for depth from motion (2012, 2 citations). These works provide elegant, mathematically rigorous methods for extracting 3D information from single-camera systems, enhancing the robustness of SLAM in resource-constrained platforms. His research demonstrates a consistent drive to bridge biological inspiration with algorithmic innovation, offering practical solutions that push the boundaries of autonomous perception and planning.