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About

Hassan Khotanlou is a researcher whose work bridges robotics, artificial intelligence, and machine vision, with a focus on enabling more intelligent and human-aware autonomous systems. His primary research areas include the kinematic analysis of parallel robots, human-robot spatial interaction, and scene understanding through RGB-D image processing. A key contribution is his development of a parallel evolutionary neural network approach for solving the forward kinematic problem of spatial parallel robots, a notoriously complex challenge due to their closed-loop structures and nonlinear equations. This work, published in 2022, has garnered 10 citations. Earlier, he tackled the same problem for planar parallel robots using a combined neural network method (2018, 7 citations). Beyond kinematics, Khotanlou has advanced human-robot cohabitation by developing methods for long-term estimation of human spatial interactions using multiple laser ranging sensors (2014, 6 citations), enabling robots to navigate in socially appropriate ways by respecting personal space. He has also contributed to machine vision by enhancing support relation extraction in RGB-D images (2017, 3 citations), improving scene understanding for robotic tasks. His work is notable for applying neural networks to solve fundamental problems in robotics, with cumulative citations reflecting a steady impact on the fields of parallel robotics and human-robot interaction.