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About

Artur Sagitov is a robotics researcher whose work spans mobile robot design, autonomous navigation, human-robot interaction, and robotic manipulation. His most cited contribution, "Comparative Analysis of Mobile Robot Wheels Design" (2018, 48 citations), provides a foundational examination of holonomic and non-holonomic wheeled platforms, serving as a valuable reference for engineers designing task-specific robotic systems. His early work on ROS-based SLAM within Gazebo-simulated environments (2015, 39 citations) demonstrated practical approaches to indoor robot localization and mapping, reflecting his strong grounding in simulation-driven development. Sagitov has made particularly notable contributions to fiducial marker systems, authoring multiple comparative studies — including analyses of ARTag, AprilTag, and CALTag — that help roboticists select optimal marker systems under real-world conditions like occlusion and rotation (30 and 12 citations respectively). His research extends into surgical robotics, exploring haptic feedback integration for automated wound suturing, and humanoid robotics, with work on fall detection, manipulator self-calibration, and grasping algorithms for the AR-601 platform. More recently, he has explored social robotics in education, investigating humanoid robot assistants for language learning in preschool settings. Collectively, his publications reflect a versatile researcher bridging theoretical design and applied robotics across diverse domains.