Research Progress
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09/05 -2025-SIA Researchers Achieve Progress in Brain-Inspired Navigation ResearchA research team from the Robotics Laboratory at the Shenyang Institute of Automation(SIA), Chinese Academy of Sciences(CAS), has proposed a real-time collaborative mapping system to fuselocal experience maps into the global map. The team introduced an effective overlapping region detection based on sequencematching is designed to realize data association between local experience maps.
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08/13 -2025-SIA Researchers Make Progress in Lifelong Person Re-Identification Across Scenes ResearchRecently, a research team from the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), proposed a Diverse Representations Embedding (DRE) framework, which effectively enhances a model's anti-forgetting capability and adaptive learning performance.
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07/11 -2025-SIA Researchers Achieves Breakthrough in 3D Inspection of Complex TubesRecently, a research team from the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS) , proposed a complex tube parameters based on the area pre-segmentation of the multi-camera vision of the online measurement method, which can be used to weld the spatial dimensions of the flanged, jointed tube online inspection.
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07/10 -2025-SIA Researchers Make Progress in Automated Manufacturing of Bent Tubes for Aero-engineRecently, a research team from the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences(CAS), has developed an automated production line for aero-engine bending pipes. This line enables automated multi-station manufacturing and inspection of bending pipes with varying dimensions.
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07/09 -2025-SIA Researchers Develop Robotic Grinding System for Large Complex ComponentsRecently, a research team from the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences(CAS), has established a robotic precision grinding and polishing system for large-scale complex curved-surface workpieces. This system is designed for the automated robotic manufacturing and inspection of the aforementioned component types.
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07/09 -2025-SIA Researchers Develop Novel Scene Recognition MethodRecently, a research team at the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), proposed a novel scene recognition method called OSFA (Object-Level and Scene-Level Feature Aggregation), which integrates the CLIP multi-modal model.
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07/08 -2025-SIA Researchers Make Progresson Space Deployable Composite Thin-Walled Structures ReseachRecently, researchers at the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), developed a novel, small-scale cross-section, lightweight, and high-stiffness deployable composite thin-walled structure. The researchers employed a comprehensive methodology integrating theoretical modeling, simulation analysis, and experimental validation to systematically simulate the structure's nonlinear mechanical behavior throughout the coiling and deployment phases.
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07/08 -2025-SIA Achieves Breakthrough in Regulating Anisotropy for Ceramic 3D PrintingRecently, researchers from the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences（CAS）, engineered micron-scale hollow rectangular structures to transform lamellar layers into interlocked "T"-shaped configurations. This redesign improves the section modulus of bending, thereby regulating anisotropic behavior.
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07/04 -2025-Researchers Achieve Breakthrough in Biohybrid Diatom MicroroboticsRecently, researchers at the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), developed biohybrid diatom microrobots capable of intelligent navigation. By implementing deep learning algorithms for real-time detection of microrobots, obstacles, and targets, the team achieved autonomous path planning, magnetic actuation, trajectory tracking, and targeted delivery.
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06/27 -2025-SIA Researchers Propose Electromechanical Co-Stimulation System to Boost Biosyncretic Robot DriveRecently, researchers at the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), have developed an electromechanical co-stimulation system inspired by human skeletal muscle training patterns, which can effectively enhance the driving performance of ASMTs.
