Can Tang

Beihang University, ITRI International, China Agricultural University

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About

Can Tang’s research lies at the intersection of medical robotics and agricultural automation, with a focus on designing novel robotic systems for constrained, high-precision environments. His major contributions include pioneering a hybrid robot architecture for CT-guided surgery, combining serial and parallel mechanisms to achieve 9 degrees of freedom within the limited workspace of a CT scanner—a solution that addresses the limitations of conventional robots in minimally invasive procedures. This work, published in 2009, has garnered 13 citations and laid the groundwork for subsequent kinematics analyses of 7-DOF hybrid robots for surgical applications (6 citations). Tang’s expertise extends beyond the operating room: in 2024, he advanced agricultural robotics with a BlendMask-BiFPN-based method for detecting the relative position of clustered tomatoes, enabling manipulators to navigate unstructured environments during robotic harvesting (2 citations). His ability to translate complex kinematic theory into practical, application-driven solutions—from screw theory and displacement manifold analysis to deep learning for crop detection—demonstrates a versatile and impactful career. Tang’s work continues to inspire innovations in both surgical and agricultural robotics, bridging the gap between theoretical mechanics and real-world automation challenges.