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A magnetic patch robot with photothermal-activated multi-modality for targeted anti-postoperative adhesion

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International Journal of Extreme Manufacturing 2025年第5期7卷

作者： Wei, Tanyong Hu, Yang Yang, Ming Shi, Chaoyang Hu, Chengzhi Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China The Institute of Maternal-Fetal Integrated Intelligent Diagnostics Xiangyang Central Hospital Affiliated Hospital of Hubei University of Arts and Science Xiangyang441106 China School of Mechanical Engineering Tianjin University Tianjin300072 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

Highlights The developed magnetic patch robot enables targeted Janus adhesion for deep tissue wounds. One side of the patch bonds well to tissues;the other side is anti-fouling. Programmable shape transformations were designed for robust and effective in vivo delivery. Targeted actuation and adhesion were successfully demonstrated in ex vivo stomach model. © 2025 The Author(s). Published by IOP Publishing Ltd on behalf of the IMMT.

关键词： Medical applications

Enhancing trajectory tracking accuracy of industrial robots through temporal–spatial mapping and multi-measurement alignment

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robotics and Computer-Integrated Manufacturing 2025年 96卷

作者： Wang, Chengzhi Zheng, Tianjiao Xu, Tian Zhao, Shize Yang, Ziyuan Zhao, Sikai Cai, Hegao Zhao, Jie Zhu, Yanhe State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China School of Materials Science and Engineering Harbin Institute of Technology Harbin150001 China

Robotic machining and automatic offline programming has been developing rapidly over the last decade, yet the absolute accuracy of industrial robots significantly impacts the processing performance, limiting their application in high-precision manufacturing fields. For dynamic non-contact continuous robotic machining tasks, such as laser cutting, precise trajectory tracking performance is especially critical. In this paper, a parallel tracking error compensation framework is proposed to improve the tracking performance of industrial robots, based on temporal–spatial mapping and multi-measurement alignment (TSM-MMA). Major tracking errors originate from nonlinear motor control lag and non-geometric motion transmission errors. The proposed method incorporates both servomotor encoder feedback and laser tracker measurements, enabling parallel distinction and compensation of these errors across trials. Typical linear and circular trajectories are analyzed using TSM to normalize multi-sensor data. Gaussian process regression (GPR) is employed in the MMA process to model the regularity of repetitive measurements, facilitating targeted error compensation. Physical experiments are conducted with an EFORT ER14-1400 robot and a Leica AT960 laser tracker to validate the effectiveness of the proposed framework. © 2025 Elsevier Ltd

关键词： Industrial robots

Dynamic Collision Avoidance Using Velocity Obstacle-Based Control Barrier Functions

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arXiv 2025年

作者： Huang, Jihao Zeng, Jun Chi, Xuemin Sreenath, Koushil Liu, Zhitao Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Institute of Intelligence Science and Engineering Shenzhen Polytechnic University Shenzhen China Hybrid Robotics Group The Department of Mechanical Engineering UC Berkeley United States

Designing safety-critical controllers for acceleration-controlled unicycle robots is challenging, as control inputs may not appear in the constraints of control Lyapunov functions (CLFs) and control barrier functions (CBFs), leading to invalid controllers. Existing methods often rely on state-feedback-based CLFs and high-order CBFs (HOCBFs), which are computationally expensive to construct and fail to maintain effectiveness in dynamic environments with fast-moving, nearby obstacles. To address these challenges, we propose constructing velocity obstacle-based CBFs (VOCBFs) in the velocity space to enhance dynamic collision avoidance capabilities, instead of relying on distance-based CBFs that require the introduction of HOCBFs. Additionally, by extending VOCBFs using variants of VO, we enable reactive collision avoidance between robots. We formulate a safety-critical controller for acceleration-controlled unicycle robots as a mixed-integer quadratic programming (MIQP), integrating state-feedback-based CLFs for navigation and VOCBFs for collision avoidance. To enhance the efficiency of solving the MIQP, we split the MIQP into multiple sub-optimization problems and employ a decision network to reduce computational costs. Numerical simulations demonstrate that our approach effectively guides the robot to its target while avoiding collisions. Compared to HOCBFs, VOCBFs exhibit significantly improved dynamic obstacle avoidance performance, especially when obstacles are fast-moving and close to the robot. Furthermore, we extend our method to distributed multi-robot systems. Copyright © 2025, The Authors. All rights reserved.

关键词： State feedback

T3: Multi-modal Tailless Triple-Flapping-Wing Robot for Efficient Aerial and Terrestrial Locomotion

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arXiv 2025年

作者： Xu, Xiangyu Zheng, Zhi Wang, Jin Chen, Yikai Huang, Jingyang Wu, Ruixin Yu, Huan Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Flapping-wing robots offer great versatility;however, achieving efficient multi-modal locomotion remains challenging. This paper presents the design, modeling, and experimentation of T3, a novel tailless flapping-wing robot with three pairs of independently actuated wings. Inspired by juvenile water striders, T3 incorporates bio-inspired elastic passive legs that effectively transmit vibrations generated during wing flapping, enabling ground movement without additional motors. This novel mechanism facilitates efficient multi-modal locomotion while minimizing actuator usage, reducing complexity, and enhancing performance. An SE(3)-based controller ensures precise trajectory tracking and seamless mode transition. To validate T3’s effectiveness, we developed a fully functional prototype and conducted targeted modeling, real-world experiments, and benchmark comparisons. The results demonstrate the robot’s and controller’s outstanding performance, underscoring the potential of multi-modal flapping-wing technologies for future aerial-ground robotic applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna grounds

Environment-Driven Online LiDAR-Camera Extrinsic Calibration

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

Sonification of Sleep EEG 2017

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Sonification of Sleep EEG

8th International Conference on Digital Arts, ARTECH 2017

作者： Fernandes, Carlos M. Migotina, Daria Rosa, Agostinho C. LARSyS: Laboratory for Robotics and Systems in Engineering and Science University of Lisbon Portugal

ISBN: (纸本)9781450352734

This paper introduces a new methodology for converting sleep Electroencephalogram (EEG) signals into sound. The main goal is to investigate the possibility of encoding sleep events into sequences of notes and breaks, generating musical sound that is consistent and audible, while allowing a global appraisal of sleep dynamics. © 2017 Copyright is held by the owner/author(s).

关键词： Electroencephalography

SAF: Local Shape-aware Face-based Garment Collision Handling via Neural SDFs

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SAF: Local Shape-aware Face-based Garment Collision Handling...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Minzhe Tang Ruisheng Yuan Dongliang Kou Mingyang Sun Lihua Zhang Academy for Engineering and Technology Fudan University Shanghai China Institute of Metaverse & Intelligent Medicine Fudan University Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Learning-based garment prediction presents an appealing alternative to physics-based methods owing to its high efficiency. However, the predicted garments can exhibit noticeable penetrations into the body. Many collision-handling methods operate in the point domain, which has an inherent limitation in addressing the penetration issues with the mesh faces. In light of this, we propose a local Shape-Aware, Face-based collision handling approach (SAF) that can be applied to garment prediction networks to achieve real-time collision handling. Considering the nature of garments, we design a compact formulation to model the continuity of the garment surface, and utilize neural Signed Distance Fields (SDFs) to accomplish penetration resolution. Recognizing the significant impact of the body’s local shape on collision handling, we further propose using the angles between SDF gradients to characterize the sharpness of the body. Our approach can compensate for the inaccuracy of neural SDFs and preserve local smoothness and details. Extensive experiments demonstrate the outstanding performance and generalizability of our method.

关键词： Shape Face recognition Clothing Signal processing Real-time systems Acoustics Character recognition Speech processing Faces

BloomScene: Lightweight Structured 3D Gaussian Splatting for Crossmodal Scene Generation

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arXiv 2025年

作者： Hou, Xiaolu Li, Mingcheng Yang, Dingkang Chen, Jiawei Qian, Ziyun Zhao, Xiao Jiang, Yue Wei, Jinjie Xu, Qingyao Zhang, Lihua Academy for Engineering and Technology Fudan University China Institute of Metaverse & Intelligent Medicine Fudan University China Engineering Research Center of AI and Robotics Ministry of Education China Jilin Provincial Key Laboratory of Intelligence Science and Engineering China Artificial Intelligence and Unmanned Systems Engineering Research Center of Jilin Province China ByteDance Inc China

With the widespread use of virtual reality applications, 3D scene generation has become a new challenging research frontier. 3D scenes have highly complex structures and need to ensure that the output is dense, coherent, and contains all necessary structures. Many current 3D scene generation methods rely on pre-trained text-to-image diffusion models and monocular depth estimators. However, the generated scenes occupy large amounts of storage space and often lack effective regularisation methods, leading to geometric distortions. To this end, we propose BloomScene, a lightweight structured 3D Gaussian splatting for crossmodal scene generation, which creates diverse and high-quality 3D scenes from text or image inputs. Specifically, a crossmodal progressive scene generation framework is proposed to generate coherent scenes utilizing incremental point cloud reconstruction and 3D Gaussian splatting. Additionally, we propose a hierarchical depth prior-based regularization mechanism that utilizes multi-level constraints on depth accuracy and smoothness to enhance the realism and continuity of the generated scenes. Ultimately, we propose a structured context-guided compression mechanism that exploits structured hash grids to model the context of unorganized anchor attributes, which significantly eliminates structural redundancy and reduces storage overhead. Comprehensive experiments across multiple scenes demonstrate the significant potential and advantages of our framework compared with several baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Virtual reality

Abstracting the Abstract: A Swarm Art Project 2017

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Abstracting the Abstract: A Swarm Art Project

8th International Conference on Digital Arts, ARTECH 2017

作者： Fernandes, Carlos M. LARSyS: Laboratory for Robotics and Systems in Engineering and Science University of Lisbon Lisbon Portugal

ISBN: (纸本)9781450352734

关键词： Abstracting