Papers
2
Total Citations
83
H-Index
2
About
Jinxian Qi is a robotics researcher whose work centers on robotic manipulation, computer vision, and autonomous grasping in unstructured environments. His primary contributions lie in developing algorithms that enable humanoid manipulators to intelligently interact with their surroundings. In his highly cited 2021 study, "Grasping posture of humanoid manipulator based on target shape analysis and force closure" (75 citations), Qi pioneered a method that analyzes an object's external geometry to plan stable, force-closure grasps—a critical step for robots operating in unpredictable settings. He further advanced object perception in his 2023 work, "Object pose estimation based on stereo vision with improved K-D tree ICP algorithm" (8 citations), where he enhanced stereo vision systems for simultaneous localization and mapping (SLAM) by refining the iterative closest point algorithm with a K-D tree structure. This innovation improves the accuracy of pose estimation for robotic grasping. Qi’s research bridges the gap between shape analysis, force closure, and real-time vision, offering practical solutions for industrial and service robotics. His work is essential reading for students and engineers interested in dexterous manipulation and perception-driven autonomy.
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