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Nantes Université

Academic institutioneurope · fr
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Research library13linked papers
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Selected work

Representative Papers

An analysis of higher-order kinematics formalisms for an innovative surgical parallel robot

Mar 27, 2025Mechanism and Machine Theory

Conventional first- and second-order kinematic models for surgical parallel robots suffer from insufficient trajectory accuracy, smoothness, and dynamic response—critical limitations in minimally invasive pancreatic surgery. Method: This paper proposes a high-order kinematic modeling and analysis framework tailored for modular hybrid parallel robots. It introduces, for the first time in surgical parallel robotics, a unified high-order differential kinematic formulation incorporating acceleration and jerk terms. Leveraging screw theory and Lie algebra, we derive a high-order Jacobian chain model, integrating symbolic computation with Simscape Multibody dynamic validation. Contribution/Results: Experimental evaluation demonstrates a 42% reduction in average end-effector trajectory tracking error and a 3.8× increase in transient response bandwidth. The framework provides a verifiable, real-time compatible high-order kinematic foundation for ultra-precise master–slave teleoperation.

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Recent publications

Latest Papers

Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank

Aug 17, 2026

This study addresses the challenges of label bias-induced pseudo-label degradation and feature misalignment in semi-supervised remote sensing segmentation. We propose UFFM, a Unified Flow Framework that integrates visual foundation models with domain-specific teachers to generate unbiased pseudo-labels within a unified training paradigm. Furthermore, a dynamic feature memory bank is designed to align class-level features, enabling joint optimization of labeled and pseudo-labeled data. Extensive experiments demonstrate that UFFM outperforms state-of-the-art methods across multiple remote sensing datasets. By effectively bridging the gap between optimization objectives and feature representations, this work establishes a novel paradigm for semi-supervised learning in remote sensing applications.

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