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IEEE International Conference on robotics and automation (ICRA)

作者： Xinran Liu Hao Chen Hongbin Liu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Professor at Chinese Academy of Sciences Institute of Automation (CASIA) Executive Director for the Centre of AI and Robotics (CAIR) Hong Kong Institute of Science & Innovation Chinese Academy of Sciences An Adjunct Reader and Director of the Haptic Mechatronics and Medical Robotics (HaMMeR) lab at the School of Biomedical Engineering and Imaging Sciences King’s College London (KCL) UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This work presents a novel shape evaluation and optimization approach for shape sensing, specifically targeting the constrained, irregular, and intricate spatial shapes of flexible bronchoscopes (FB) in human bronchial tree. The proposed evaluation criteria and optimization methods combine clinical significance related to bronchial anatomical structures and address issues related to singular points and discontinuities in traditional shape reconstruction models. Three-dimensional experiments were conducted within eight spatial complex configurations printed from a proportional bronchial model. The 3D experiment results demonstrate an average reduction of approximately 34.1% in shape reconstruction errors across all eight airway models compared to the traditional model, validating the effectiveness and feasibility.

关键词： Bronchoscopy Solid modeling Optical fiber sensors Three-dimensional displays Shape Atmospheric modeling Optimization methods

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Description and fault diagnosis in autonomous underwater vehicles based on ontologies

Description and fault diagnosis in autonomous underwater veh...

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Ocean Studies (ICOS), International Conference on

作者： Timoshenko Alexander Zuev Alexander Mursalimov Eduard Gribova Valeria Inzartsev Alexander Department of Automation and Robotics Laboratory of intelligent systems for marine robots Far Eastern Federal UniversityInstitute of Marine Technology Problems FEB RAS Vladivostok Russia Laboratory of Intelligent Systems Institute of Automation and Control FEB RAS Vladivostok Russia Laboratory of Control Systems Institute of Marine Technology Problems FEB RAS Vladivostok Russia

ISBN: (纸本)9781665473736

The paper proposes new ontologies for describing and diagnosing malfunctions of subsystems of autonomous underwater vehicles (AUV) as a part of the development of a previously created approach to the intelligent diagnosis of these vehicles using knowledge bases. To implement this approach, it is proposed to use two knowledge bases, one of which describes malfunctions, features for their identification and actions necessary to adapt to the negative consequences caused by the appearance of these malfunctions. The other database contains information about the main subsystems of the AUV, devices and replaceable modules, as well as the states they may be in. Knowledge bases are used in the work of the intelligent control-emergency system (ICES) on board the AUV, which allows for comprehensive functional diagnostics of all subsystems of the device, including payload modules, as well as adaptation to the consequences of emerging malfunctions, taking into account the actual cause-and-effect relationships between them. To test the system’s operability, a prototype was developed. The results of the prototype simulation showed that ICES performs the tasks and can be implemented on the onboard computers of existing AUV using well-known programming technologies.

关键词： Performance evaluation Autonomous underwater vehicles Simulation Knowledge based systems Prototypes Ontologies Programming

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Design and implementation of FPGA-based hardware acceleration system for target detection

Design and implementation of FPGA-based hardware acceleratio...

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2022 Chinese automation Congress, CAC 2022

作者： Zhan, Canyu Zhao, Bingjie Zhang, Huangliang Yan, Zhongpeng Wu, Jie Yuan, Da Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Shenyang University of Technology Shenyang China

ISBN: (纸本)9781665465335

Since the application of target detection in convolutional neural networks is becoming more and more widespread, and the computational volume is increasing, the complexity of the network hierarchy and the memory requirements are gradually increasing, so according to the Field Programmable Gate Array (FPGA) parallel computing characteristics, using FPGA to accelerate the convolutional neural network hardware, can design a CPU+FPGA target detection hardware acceleration system. The system adopts the YOLOv2 target detection algorithm and divides the tasks according to the network structure of the algorithm, and at the same time, it quantifies the 32-bit floating-point numbers of the algorithm with 16-bit fixed points, optimizes the convolutional, pooling, and reordering layers of the algorithm, and finally designs the algorithm by using the HLS high-level synthesis. The system uses PYNQ-Z2 platform to implement the convolutional neural network YOLOv2, and the final system performance reaches 27.06GOPs (Giga Operations Per Second, 1 billion operations/s), and the power consumption is only 2.609W, compared to CPU (E5-2620V4), its performance is 6.58 times that of CPU, and the power consumption is only 3% of the CPU, which has certain advantages compared with current target detection systems. © 2022 IEEE.

关键词： Convolution

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ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding

ASI-Seg: Audio-Driven Surgical Instrument Segmentation with ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhen Chen Zongming Zhang Wenwu Guo Xingjian Luo Long Bai Jinlin Wu Hongliang Ren Hongbin Liu Centre for Artificial Intelligence and Robotics (CAIR) Hong Kong Institute of Science & Innovation Chinese Academy of Sciences China Department of Electronic Engineering Chinese University of Hong Kong China State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Surgical instrument segmentation is crucial in surgical scene understanding, thereby facilitating surgical safety. Existing algorithms directly detected all instruments of predefined categories in the input image, lacking the capability to segment specific instruments according to the surgeon’s intention. During different stages of surgery, surgeons exhibit varying preferences and focus toward different surgical instruments. Therefore, an instrument segmentation algorithm that adheres to the surgeon’s intention can minimize distractions from irrelevant instruments and assist surgeons to a great extent. The recent Segment Anything Model (SAM) reveals the capability to segment objects following prompts, but the manual annotations for prompts are impractical during the surgery. To address these limitations in operating rooms, we propose an audio-driven surgical instrument segmentation framework, named ASI-Seg, to accurately segment the required surgical instruments by parsing the audio commands of surgeons. Specifically, we propose an intention-oriented multimodal fusion to interpret the segmentation intention from audio commands and retrieve relevant instrument details to facilitate segmentation. Moreover, to guide our ASI-Seg segment of the required surgical instruments, we devise a contrastive learning prompt encoder to effectively distinguish the required instruments from the irrelevant ones. Therefore, our ASI-Seg promotes the workflow in the operating rooms, thereby providing targeted support and reducing the cognitive load on surgeons. Extensive experiments are performed to validate the ASI-Seg framework, which reveals remarkable advantages over classical state-of-the-art and medical SAMs in both semantic segmentation and intention-oriented segmentation. The source code is available at https://***/Zonmgin-Zhang/ASI-Seg.

关键词： Instruments Semantic segmentation Source coding Surgery Contrastive learning Manuals Cognitive load Safety Intelligent robots Biomedical imaging

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End-To-End Deep Reinforcement Learning for First-Person Pedestrian Visual Navigation in Urban Environments

End-To-End Deep Reinforcement Learning for First-Person Pede...

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IEEE-RAS International Conference on Humanoid Robots

作者： Honghu Xue Rui Song Julian Petzold Benedikt Hein Heiko Hamann Elmar Rueckert Institute for Robotics and Cognitive Systems University of Luebeck Luebeck Institute of Computer Engineering University of Luebeck Luebeck Institute of Automation Technology Helmut Schmidt University Ham-burg Chair of Cyber-Physical-Systems Montanuniversität Leoben Leoben

ISBN: (纸本)9798350309805

We solve a pedestrian visual navigation problem with a first-person view in an urban setting via deep reinforcement learning in an end-to-end manner. The major challenges lie in severe partial observability and sparse positive experiences of reaching the goal. To address partial observability, we propose a novel 3D-temporal convolutional network to encode sequential historical visual observations, its effectiveness is verified by comparing to a commonly-used Frame-Stacking approach. For sparse positive samples, we propose an improved automatic curriculum learning algorithm NavACL + , which proposes meaningful curricula starting from easy tasks and gradually generalizing to challenging ones. NavACL + is shown to facilitate the learning process with 21% earlier convergence, to improve the task success rate on difficult tasks by 40% compared to the original NavACL algorithm [1] and to offer enhanced generalization to different initial poses compared to training from a fixed initial pose.

关键词： Training Deep learning Visualization Three-dimensional displays Navigation Urban areas Humanoid robots

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A 5G-Based Switching Method Between a Local Controller and an Edge Controller

A 5G-Based Switching Method Between a Local Controller and a...

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IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Xiulian Wang Jinggao Lin Xi Jin Changqing Xia Chi Xu College of Automation and Electrical Engineering Shenyang Ligong University China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

With the rapid development of edge computing, industrial automation has evolved a two-layer distributed computing architecture, including local controllers communicating through wired networks and edge controllers communicating wirelessly. The edge controllers in existing studies use WIFI, which has a limited working distance and requires independent network deployment, so they are unsuitable for future large-scale complex industrial production. At present, 5G technology is undergoing rapid development in the field of industrial automation, with wide coverage and excellent mobility, gradually becoming suitable for future large-scale and complex industrial production scenarios. Therefore, this paper proposed an SW5G method based on 5G, which combined the local controller with the edge controller. The controllers were switched according to the constantly changing 5G network transmission delay, thereby optimizing the overall system control performance. Finally, simulation experiments demonstrated good control performance of SW5G in various network scenarios.

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Graphics Capsule: Learning Hierarchical 3D Face Representations from 2D Images

Graphics Capsule: Learning Hierarchical 3D Face Representati...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chang Yu Xiangyu Zhu Xiaomei Zhang Zhaoxiang Zhang Zhen Lei State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

The function of constructing the hierarchy of objects is important to the visual process of the human brain. Previous studies have successfully adopted capsule networks to decompose the digits and faces into parts in an unsupervised manner to investigate the similar perception mechanism of neural networks. However, their descriptions are restricted to the 2D space, limiting their capacities to imitate the intrinsic 3D perception ability of humans. In this paper, we propose an Inverse Graphics Capsule Network (IGC-Net) to learn the hierarchical 3D face representations from large-scale unlabeled images. The core of IGC-Net is a new type of capsule, named graphics capsule, which represents 3D primitives with interpretable parameters in computer graphics (CG), including depth, albedo, and 3D pose. Specifically, IGC-Net first decomposes the objects into a set of semantic-consistent part-level descriptions and then assembles them into object-level descriptions to build the hierarchy. The learned graphics capsules reveal how the neural networks, oriented at visual perception, understand faces as a hierarchy of 3D models. Besides, the discovered parts can be deployed to the unsupervised face segmentation task to evaluate the semantic consistency of our method. Moreover, the part-level descriptions with explicit physical meanings provide insight into the face analysis that originally runs in a black box, such as the importance of shape and texture for face recognition. Experiments on CelebA, BP4D, and Multi-PIE demonstrate the characteristics of our IGC-Net.

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DG-NBV: A Cognitive Framework for Direct Generation of Next Best View in Continuous View Space

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IEEE Transactions on Cognitive and Developmental systems 2024年

作者： Liu, Zhicheng Cao, Zhiqiang Li, Jianjie Guan, Peiyu Yu, Junzhi Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Peking University Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

The next best view (NBV) plays an important role in the three-dimensional object reconstruction. Existing NBV methods mainly adopt the generate-and-test strategy to select the best view in the discrete view space. This affects the adaptation to diverse objects. To solve this problem, a new NBV paradigm to directly generate the next best view in the continuous view space according to the input point cloud is proposed. Specifically, a point cloud feature extraction module with learnable view and position tokens is presented. These tokens are added upon the neighborhood and the position features of the point cloud to fully mine global contextual information, enhancing the feature representation. The predicted view from the proposed network is linked to a pre-trained view evaluation network. By duplicating this prediction view and then concatenating the duplication result with the extracted point cloud feature, the evaluation network is endowed with the ability to evaluate arbitrary views. Take the evaluation score of the evaluation network corresponding to the predicted view as the supervision signal, the network is trained. In this way, an effective solution of the next best view selection in the continuous view space is obtained. Accordingly, the adaptability to different objects is reinforced. Experimental results on the ShapeNet and MIT CSAIL datasets demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Feature extraction

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Method for detecting surface defects of ceramic tile based on improved Cascade RCNN 4

Method for detecting surface defects of ceramic tile based o...

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4th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Cao, Yu Wang, Yu Feng, Haiwen Wang, Ting School of Software College Shenyang University of Technology Shenyang110870 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)9798350321951

In view of the problems of small scale and low contrast of ceramic tile defects, various types of ceramic tile surface defects, and difficulty in realizing high-precision ceramic tile defect detection, a ceramic tile surface defect detection model based on improved Cascade RCNN is proposed to locate and identify the types of ceramic tile surface defects in different texture backgrounds. The improved ResNest network is used to improve the classification ability of the algorithm and optimize the performance of the model. The improved feature pyramid enhanced the feature extraction ability of the algorithm, and improved the accuracy of detecting small-scale defects and low-contrast defects of ceramic tiles. The whole connection structure of the last layer of cascade detectors was modified to double-head structure, which improved the ability of detectors to perform classification and regression tasks, and solved the problem of various kinds of defects on the surface of ceramic tiles. After data collection, 2810 tiles defect pictures were obtained, and then 7934 tiles defect slices were obtained by image segmentation, and the slices were made into tiles defect data sets. Experiments show that this method can achieve 77.8% MAP and 93.9% average positive detection rate, which is higher than Faster RCNN and original Cascade RCNN. © 2022 IEEE.

关键词： Deep learning

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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