Shitian Zhang

Dalian Maritime University, Research Institute of Radio

Papers

Total Citations

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About

Shitian Zhang is a robotics researcher focused on advancing computer vision and manipulation for industrial automation. His primary research areas include 6D object pose estimation, robotic grasping, and deep learning-based perception for irregular objects. Zhang’s major contributions center on developing robust methods for real-world robotic applications. His most cited work, "Robotic Grasping Position of Irregular Object Based Yolo Algorithm" (2020, 15 citations), addresses a critical challenge in automation by proposing an adaptive grasping position detection method using a modified YOLO algorithm, enabling robots to autonomously identify and grasp irregularly shaped objects—a task that previously led to frequent failures. More recently, Zhang has tackled the complex problem of multi-instance 6D pose estimation in his 2025 paper, introducing a robust multi-view point pair feature (PPF) method that overcomes challenges like pseudo outliers, occlusions, and low model-instance overlap. This work has significant implications for industrial robots operating in cluttered environments. With a growing citation record and contributions that bridge deep learning and traditional geometric approaches, Zhang is establishing himself as a promising voice in robotic perception and manipulation.