Max Robotics — Global Service Robot Directory + US Certification

About

Song Han is a leading researcher in robotics and intelligent automation, whose work bridges the gap between theoretical control systems and practical industrial applications. His primary research areas include robot calibration, motion control for mobile robots, and visual sorting systems for logistics. Han’s major contributions are highlighted by his innovative use of dual quaternion algebra for precise robot base frame calibration, a method that has garnered 42 citations for its simplicity and accuracy in enabling high-precision motion planning. He has also advanced adaptive motion control for mobile robots operating in challenging environments, such as those with wheel slipping, using neural networks to enhance control accuracy—a work cited 27 times. In the realm of intelligent logistics, Han pioneered multi-task deep learning and multi-modal information fusion for visual sorting of express parcels, achieving efficient detection and sorting of disorderly stacks, with his 2020 paper earning 31 citations. His notable achievements include developing cooperative control strategies for multiple nonholonomic robots to enclose and track targets while avoiding obstacles, demonstrating his impact on multi-robot systems. With over 120 total citations, Song Han’s research is essential for students and engineers seeking robust solutions in automation, robotics, and smart logistics.