Max Robotics — Global Service Robot Directory + US Certification

About

Mohammad Taghi Manzuri is a prominent researcher whose work spans robotics, autonomous systems, computer vision, and smart city technologies. His foundational contributions to mobile robot navigation include pioneering a genetic algorithm-based path planning method (2009, 27 citations) that introduced a more computationally efficient environment representation, and a fuzzy artificial potential fields approach (2007, 12 citations) enabling real-time navigation in dynamic environments. His 2007 work on real-time trajectory generation further cemented his expertise in autonomous mobility. Manzuri's research has evolved significantly toward deep learning and intelligent visual interpretation. His Glimpse-Gaze framework (2018, 24 citations) advanced visual analytics for smart farming and jungle environments, while his cross-altitude visual interpretation system (2018, 22 citations) demonstrated multi-agent robotic platforms for smart city applications. He has also contributed to autonomous vehicle navigation through GPU-accelerated RANSAC-based rotation estimation and a vision-based online gyroscope system. More recently, Manzuri has pursued geo-spatiotemporal intelligence for eco-cyber-physical agricultural systems and deep learning solutions for autonomous landscaping robots in green urban environments. His cumulative body of work reflects a consistent drive to translate intelligent algorithms into real-world autonomous and smart environment applications, earning him well over 100 citations across disciplines.