Hamed Mesgari

Papers

Total Citations

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About

Hamed Mesgari’s research lies at the intersection of robotic manipulation, grasp planning, and intelligent control systems. His work focuses on solving one of robotics’ most fundamental challenges: how to enable a manipulator to stably and optimally grasp an object for task execution. Mesgari introduced the Multi Aspect Grasp (MAG) performance index, a novel metric that holistically evaluates grasp quality by considering multiple physical and geometric factors. This framework, detailed in his 2011 paper “Application of MAG index for optimal grasp planning” (7 citations), provides a systematic method for assessing and selecting optimal grasp configurations. He further advanced this work by integrating neural networks, as shown in “A Neural Network Approach for optimal grasp planning” (2 citations), where he used co-simulation with MSC.ADAMS and MATLAB to train a model that predicts the best grasp points on an object for a 6-DOF Staubli manipulator. Though his citation counts are modest, Mesgari’s contributions are notable for their practical, simulation-driven methodology, offering a foundation for more adaptive and intelligent robotic grasping systems. His work is particularly relevant for researchers exploring data-driven approaches to dexterous manipulation.