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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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ReFlow6D: Refraction-Guided Transparent Object 6D Pose Estimation via Intermediate Representation Learning

arXiv

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arXiv 2024年

作者： Gupta, Hrishikesh Thalhammer, Stefan Weibel, Jean-Baptiste Haberl, Alexander Vincze, Markus Vision for Robotics Laboratory Automation and Control Institute TU Wien Austria Industrial Engineering Department UAS Technikum Vienna TU Wien Austria

Transparent objects are ubiquitous in daily life, making their perception and robotics manipulation important. However, they present a major challenge due to their distinct refractive and reflective properties when it comes to accurately estimating the 6D pose. To solve this, we present ReFlow6D, a novel method for transparent object 6D pose estimation that harnesses the refractive-intermediate representation. Unlike conventional approaches, our method leverages a feature space impervious to changes in RGB image space and independent of depth information. Drawing inspiration from image matting, we model the deformation of the light path through transparent objects, yielding a unique object-specific intermediate representation guided by light refraction that is independent of the environment in which objects are observed. By integrating these intermediate features into the pose estimation network, we show that ReFlow6D achieves precise 6D pose estimation of transparent objects, using only RGB images as input. Our method further introduces a novel transparent object compositing loss, fostering the generation of superior refractive-intermediate features. Empirical evaluations show that our approach significantly outperforms state-of-the-art methods on TOD and Trans32K-6D datasets. Robot grasping experiments further demonstrate that ReFlow6D’s pose estimation accuracy effectively translates to real-world robotics task. The source code is available at: https://***/StoicGilgamesh/ReFlow6D and https://***/StoicGilgamesh/matting *** Codes 68T45 © 2024, CC BY-NC-SA.

关键词： Light refraction

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers

ZS6D: Zero-shot 6D Object Pose Estimation using Vision Trans...

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IEEE International Conference on robotics and automation (ICRA)

作者： Philipp Ausserlechner David Haberger Stefan Thalhammer Jean-Baptiste Weibel Markus Vincze Vision for Robotics Laboratory Automation and Control Institute TU Wien Austria Industrial Engineering Department University of Applied Sciences Technikum Vienna Austria

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

As robotic systems increasingly encounter complex and unconstrained real-world scenarios, there is a demand to recognize diverse objects. The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects. Recent novel object pose estimation methods are solving this issue using task-specific fine-tuned CNNs for deep template matching. This adaptation for pose estimation still requires expensive data rendering and training procedures. MegaPose for example is trained on a dataset consisting of two million images showing 20,000 different objects to reach such generalization capabilities. To overcome this shortcoming we introduce ZS6D, for zero-shot novel object 6D pose estimation. Visual descriptors, extracted using pre-trained Vision Transformers (ViT), are used for matching rendered templates against query images of objects and for establishing local correspondences. These local correspondences enable deriving geometric correspondences and are used for estimating the object's 6D pose with RANSAC- based PnP. This approach showcases that the image descriptors extracted by pre-trained ViTs are well-suited to achieve a notable improvement over two state-of-the-art novel object 6D pose estimation methods, without the need for task-specific fine-tuning. Experiments are performed on LMO, YCBV, and TLESS. In comparison to MegaPose, we improve the Average Recall on all three datasets and compared to OSOP we improve on two datasets. The code is available at https://***/PhilippAuss/ZS6D.

关键词： Training Computer vision Visualization Codes Computational modeling Pose estimation Transformers

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Optimization of Milling Robot Trajectory Based on Whale Optimization Algorithm 3rd

Optimization of Milling Robot Trajectory Based on Whale Opti...

引用

3rd International Conference on Artificial Intelligence, robotics, and Communication, ICAIRC 2023

作者： Zhou, Yang Li, Qingxin Sun, Yong Software College Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9789819721993

In response to the problem of vibration and low work efficiency during robot milling, taking into account the work indicators of milling time and stability, combined with the kinematic constraints of the robotic arm, the time impact optimization problem is transformed into a multi-objective optimization problem. The paper proposes an improved whale optimization algorithm, which introduces a differential mutation strategy to improve the problem of traditional whale algorithms easily getting stuck in local optima. Compared with traditional whale optimization algorithms, the improved whale algorithm significantly improves the convergence speed and accuracy during the milling process. Finally, it is verified by a solid robot. The results showed that the pulse value of the planned trajectory was reduced by up to 99.76%, and the efficiency was improved by 47.62%, effectively avoiding robot vibration during the milling process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multiobjective optimization

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PCEB-YOLOV8: An Improved PCB Defect Detection Method Based on YOLOV8

PCEB-YOLOV8: An Improved PCB Defect Detection Method Based o...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Meihui Li Chunhe Song Xia Yu Mengmeng Chu Rui Li Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Shenyang University of Technology Shenyang China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Printed circuit board defect recognition is of great significance to ensuring the quality of electronic products. This paper proposes an improved PCB defect detection method based on YOLOV8. Firstly, the data augmentation method PredMix (prediction mix) is adopted to simulate scenarios such as overlapping and occlusion in images, where the displays are incomplete or blurred, thereby enhancing the robustness of the network model. Secondly, BiFPN is used to replace PAN-FPN in the fusion layer to strengthen the feature fusion capability. Finally, the CESE_C2f module is employed in the Backbone part, leveraging a lightweight attention mechanism to improve the detection effect while reducing the number of parameters. Experimental results show that compared with the original YOLOV8, the MAP value of this improved algorithm reaches $\mathbf{92.6}\%$, with a $\mathbf{2.3\%}$ increase in accuracy.

关键词： Accuracy Costs Printed circuits Diversity reception Production Feature extraction Data augmentation Data models Real-time systems Defect detection

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A Miniaturized Pneumatic Electrode Gripper for Robotic Cochlear Implant Surgery

A Miniaturized Pneumatic Electrode Gripper for Robotic Cochl...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Gao, Hongyan Zou, Yun Huang, Shaoping Chen, Weidong Gao, Anzhu Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Institute of Medical Robotics and Department of Automation Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781665481090

Cochlear implant surgery is one of the most challenging surgeries due to the limited workspace and delicate inner structure. A traditional electrode implant instrument is a passive elastic forceps, which needs another forceps to help grip or release the electrode. It is not convenient to achieve delicate operations in a confined space. This paper develops a miniaturized pneumatic electrode gripper at the tip of forceps to achieve active gripping. It transmits the pressurized air through 3D-printed inner lumens to form two inflated soft bubbles to push the electrode to the rigid shell for accurate positioning. The finite element analysis is carried out to evaluate the shape of inflated soft bubbles with variable soft films. A prototype of a gripper with inner tortuous airways is fabricated using micro 3D printing technique, and experiments are carried out to validate its accuracy and demonstrate its ability to grip and release. Finally, robotic teleoperation using a robotic arm and a joystick is implemented for cochlear implant surgery. Results demonstrate that the developed miniaturized pneumatic gripper has successfully achieved electrode implantation in a 3D-printed cochlear phantom. © 2022 IEEE.

关键词： Electrodes

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An analysis method of industrial data relationship based on business context 3

An analysis method of industrial data relationship based on ...

引用

3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Liu, Yang Zhang, Tianshi Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Industrial big data processing is an important support for improving industrial production efficiency. Because industrial big data involves complex business logic in production scenarios, there is a widespread problem that the relationship between data is difficult to understand. Therefore, this paper proposes a relational analysis method of industrial data based on business context. Firstly, a semantic data dictionary is established according to the production business process, and then a data object recognition model based on deep learning is trained;then a context-based automatic association method of industrial data relations is proposed;finally, the experimental verification is carried out on the actual production data, and the results show that The method in this paper is effective in improving the relationship analysis of industrial data. © 2022 IEEE.

关键词： Big data

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A Relational Retrieval Model for Industrial Production Systems 2

A Relational Retrieval Model for Industrial Production Syste...

引用

2nd International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2022

作者： Liu, Yang Zhang, Tianshi Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781665459280

ISBN: (纸本)9781665459280

Industrial big data analysis is an important approach to explore the essence of industrial production processes. Traditional keyword retrieval and semantic retrieval are difficult to adapt to the industrial manufacturing systems with the characteristics of strong coupling of business associations. Therefore, according to the characteristics of the association in the industrial production system, an association retrieval model for the industrial production system is proposed in this paper. Firstly, the industrial Internet of things system is analyzed and abstracted, and the key factors of correlation retrieval are derived;then through the analysis of the relationship between key factors, the invalid relationship is screened out, the correlation relationship with practical significance is obtained, and the establishment of the relevance retrieval model is completed. Finally, the feasibility and effectiveness of the association retrieval model are verified by actual industrial scene data. © 2022 IEEE.

关键词： Manufacture

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Lightweight LiDAR-Camera 3D Dynamic Object Detection and Multi-Class Trajectory Prediction

arXiv

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arXiv 2025年

作者： He, Yushen Zhao, Lei Deng, Tianchen Fang, Zipeng Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

Service mobile robots are often required to avoid dynamic objects while performing their tasks, but they usually have only limited computational resources. So we present a lightweight multi-modal framework for 3D object detection and trajectory prediction. Our system synergistically integrates LiDAR and camera inputs to achieve real-time perception of pedestrians, vehicles, and riders in 3D space. The framework proposes two novel modules: 1) a Cross-Modal Deformable Transformer (CMDT) for object detection with high accuracy and acceptable amount of computation, and 2) a Reference Trajectory-based Multi-Class Transformer (RTMCT) for efficient and diverse trajectory prediction of mult-class objects with flexible trajectory lengths. Evaluations on the CODa benchmark demonstrate superior performance over existing methods across detection (+2.03% in mAP) and trajectory prediction (-0.408m in minADE5 of pedestrians) metrics. Remarkably, the system exhibits exceptional deployability - when implemented on a wheelchair robot with an entry-level NVIDIA 3060 GPU, it achieves realtime inference at 13.2 fps. To facilitate reproducibility and practical deployment, we release the related code of the method at https://***/TossherO/3D Perception and its ROS inference version at https://***/TossherO/ros packages. Copyright © 2025, The Authors. All rights reserved.

关键词： Mobile robots

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