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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

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Designing of an Amphibian Hexapod with Computer vision for Rescue Operations 6

Designing of an Amphibian Hexapod with Computer Vision for R...

引用

6th international conference on control, automation and robotics (ICCAR)

作者： Rawat, Parthsarthi Misra, Tanay Mitra, Santanu Sinha, Aakash Shiv Nadar Univ Sch Engn Dadri UP India

ISBN: (纸本)9781728161396

Nowadays robots have been inching towards living beings in terms of performance, agility, accuracy and performance. But there is one domain which is yet to come to limelight i.e. autonomous rescue robots. this involves a parallel use of robot design and control systems to get it executed. Basically, in simpler terms, there is a risk of life is involved in such scenarios and it's always hoped that a robot deployed for such operations be working with high precision and recall. So, in this paper we have described the design and control of an autonomous amphibian all terrain six-legged robot with adaptive gait for rescue operations. To fulfill this, we have used LiDAR and camera in parallel to assist bot in locomotion and detect the victim. Achieving all of this needs a significant amount of on-board processing power, but at the same time has to be compact enough. Keeping this in mind, NVIDIA Jetson Nano seems to be promising choice to get all the processing done onboard.

关键词： autonomous amphibian hexapod rescue robot NVIDIA Jetson Nano bio-mimicking

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Intelligent Waste Sorting Platform Based on Taurus & Pegasus

Intelligent Waste Sorting Platform Based on Taurus & Pegasus

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IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Xiang Chen Liguo Tian Meng Li Jinqi Liu Yuesong Wang Donghui Fang Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin China

Garbage sorting is an action for everyone to participate, but the existing garbage sorting mainly relies on manual discrimination. In this paper, an intelligent garbage sorting platform is designed using Taurus & Pegasus kit, which records the video stream of garbage using the sensor side of Taurus vision development board. the generated video files are then copied to an SD card for dataset creation, data cleaning, and data annotation. ResNetl8 network model is built using Pytorch framework for training (get pytorch), model transformation (model-caffe), quantization and simulation. Do end-to-end modeling and visualization. Network models are combined with HiSilicon NNIE deep learning acceleration engine to implement fast target classification algorithms. Finally, the model is deployed to the Taurus side for image recognition using an SD card. Residents place garbage in front of the camera, and the Taurus side recognizes and displays the garbage classification inference results and probabilities on the screen. At the same time, the result is sent to Pegasus via serial communication. Pegasus receives the classification result and drives the corresponding steering engine to open the garbage can lid and give voice prompt. When no object or background picture is captured in the camera screen, the trash can lid will be closed.

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Transparent Slide Detection and Gripper Design for Slide Transport by Robotic Arm

Transparent Slide Detection and Gripper Design for Slide Tra...

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international Symposium on Computational Intelligence and Informatics

作者： Marianna Dimitrova Kucarov Mátyás Takács Béla Molnár Miklos Kozlovszky Doctoral School of Applied Informatics and Applied Mathematics BioTech Óbuda University 3DHistech Ltd. Budapest Hungary 3DHistech Ltd. Óbuda University University Research and Innovation Center (EKIK) Budapest Hungary 3DHistech Ltd. Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (纸本)9798350398830

Transparent slide detection, pick and place automation are obtained in order to boost speed, accuracy and smooth workflow between slide coverslipping and digital scanning operation in digital pathology. Fully automatic, precise, robust solution has been developed by robotic arm using computer vision and image processing techniques. Magazine (slide holder) and gripper design, camera selection and mapping to robot base coordinate system are all guided. Python based software algorithms for slide detection and 3D position determination applied in dynamically changeable environment are also explained step by step. Complete automatic process control method is implemented and tested for different types of slide.

关键词： Pathology Computer vision three-dimensional displays Robot kinematics Image processing Process control Reliability theory

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A Novel Defect Inspection Approach Based on Self-attention Convolutional Adversarial Auto-Encoder

A Novel Defect Inspection Approach Based on Self-attention C...

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robotics, control and automation Engineering (RCAE), international conference on

作者： Jian Wang Yakun Li Zhiyan Han School of Control Science and Engineering Bohai University Jinzhou China

ISBN: (数字)9781665459419

ISBN: (纸本)9781665459426

Defect inspection based on computer vision has gradually replaced the traditional detection method, which is widely used in industry and greatly improving the efficiency of industrial production. Most defect inspection methods use supervised learning, which require vast amounts of label images for training. However, the complete defect inspection task is challenging due to some problems, such as diversity of defects, high similarity defects (e.g., low contrast, ambiguous boundary and small background difference) and large defect scale. To solve the above problems, a novel unsupervised defect inspection approach with Self-attention Convolutional Adversarial Auto-Encoder (SCAAE) is proposed in this paper, based on the encoder-decoder-discriminator structure. In this approach, we improve the encoder and decoder via the self-attention mechanism to enhance the feature extraction ability of convolutional autoencoder (CAE), and then the discriminator helps SCAAE impose the latent variable to cluster by a prior distribution. Finally, large number of experiments on four datasets demonstrate the effectiveness of SCAAE and outperforms state-of-the-art methods.

关键词： Training Industries Computer vision Service robots Supervised learning Production Inspection

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Efficient Multi-task Learning for Road Scene Classification: Scene, Time, and Weather Predictions

Efficient Multi-task Learning for Road Scene Classification:...

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IEEE international conference on Intelligent Computer Communication and Processing (ICCP)

作者： Roman Prykhodchenko Pawel Skruch Department of Automatic Control and Robotics AGH University of Science and Technology Cracow Poland

ISBN: (数字)9798331539979

ISBN: (纸本)9798331539986

this paper addresses the task of road scene classification by leveraging multi-task learning (MTL) to improve model performance and computational efficiency. We employ the BDD100K dataset, which provides diverse driving scenarios, and apply data augmentation and balancing techniques to mitigate class imbalances. Our methodology integrates a ResNet18 backbone pre-trained on ImageNet, extended with task-specific heads for scene type, time of day, and weather classification. Uniform Manifold Approximation and Projection (UMAP) is used for dimensionality reduction and visualization of feature embeddings. Experiments demonstrate that our MTL approach significantly enhances classification accuracy across tasks, with notable performance in time-of-day classification. the proposed model achieves robust results despite class imbalances, validated through confusion matrices and UMAP visualizations. Our findings highlight the benefits of MTL in road scene classification, offering insights for future research in autonomous driving and computer vision.

关键词： Scene classification Accuracy Computational modeling Roads Data visualization Weather forecasting Multitasking Data augmentation Autonomous vehicles Testing

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A Multi-task Deep Network for video-based Person Re-identification 21

A Multi-task Deep Network for video-based Person Re-identifi...

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2021 international conference on control and Intelligent robotics, ICCIR 2021

作者： Wang, Yizhong Xu, Bing Guo, Xiaoyong Yang, Guowei College of Electronic Information and Automation Tianjin University of Science and Technology Tianjin China

ISBN: (纸本)9781450390231

Video-based person re-identification is one of the significant areas of computer vision, and it has huge application potential in video surveillance. Many existing methods address this difficulty by extracting only the characteristics of one person in the network. this paper proposes a Pose-Attribute network (MBNet) through multi-task learning of embedded pose representation, which combines pedestrian attribute recognition with ReID. MBNet extracts 18 human body keypoints and 12 pedestrian attributes that contain gender, shoes, etc. from the image, and simultaneously learns the local and global features. Conclusion of experiments is on two datasets, including the DukeMTMC-VID and MARS. the results show that the mAP of MBNet is higher than the mAP of pose-based or attribute aware for Person re-identification. the experimental results show that the proposed network is better than other video-based people re-identification approaches. © 2021 ACM.

关键词： Security systems

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Measuring the Sim2Real Gap in 3D Object Classification for Different 3D Data Representation 13th

Measuring the Sim2Real Gap in 3D Object Classification for D...

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13th international conference on Computer vision Systems, ICVS 2021

作者： Weibel, Jean-Baptiste Rohrböck, Rainer Vincze, Markus Vision for Robotics Laboratory Automation and Control Institute TU Wien Vienna Austria

ISBN: (纸本)9783030871550

Perceiving the environment geometry is necessary for a robot to perform safe motions and actions. To decide upon meaningful actions, however, semantic understanding is also required. At the object level, this semantic classification task can directly be performed using the extracted object 3D data. While continuously improving, the performance of methods designed for this task still decrease on data captured by a robot because of input data differences [18], referred to as the Sim2Real gap. In this paper, we aim to better evaluate that gap for different 3D data representations and understand the impact of a variety of design choices through a set of specific experiments, performed both on the ModelNet dataset [20] to which a variety of alterations is applied and on the ScanObjectNN dataset [18]. Results indicate that occlusions plays an essential part in the gap and that their impact is mitigated by the use of hierarchical representation learned from the surface of the object itself. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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A Robotized Soft Endoscope with Stereo vision for Upper Gastrointestinal Endoscopic Submucosal Dissection (ESD)

A Robotized Soft Endoscope with Stereo Vision for Upper Gast...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jian Chen Shuai Wang Qingxiang Zhao Mingcong Chen Hongbin Liu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Centre of AI and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong School of Biomedical Engineering and Imaging Sciences Kings College London London UK

this work presents a novel dual-segment flexible robotic endoscope designed to enhance reachability and dexterity during ESD surgery. the proposed system is capable of executing multi-angle cutting operations at a small angle relative to the lesion surface, allowing for efficient en-bloc resection. Additionally, the system incorporates two calibrated RGB cameras and a depth estimation algorithm to provide detailed 3D information of the tumour, which is used to guide the control framework. A stereo visual servoing controller is also implemented to improve path-following performance during surgery. Experiments results indicate that the proposed system improves motion stability and precision. the root means square error (RMSE) of circle path following is 1.1991mm with a maximum of 1.4751mm. Ex-vivo testing demonstrates its significant potential for use in endoscopic surgery.

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Analysis of Different Human Body Recognition Methods and Latency Determination for a vision-based Human-robot Safety Framework According to ISO/TS 15066 17

Analysis of Different Human Body Recognition Methods and Lat...

引用

17th international conference on Informatics in control, automation and robotics (ICINCO)

作者： Bricher, David Mueller, Andreas Johannes Kepler Univ Linz Inst Robot Altenbergerstr 69 A-4040 Linz Austria

ISBN: (纸本)9789897584428

Today, an efficient and flexible usage of lightweight robots in collaborative working spaces is strongly limited by the biomechanical safety regulations of ISO/TS 15066. In order to maximize the robot performance without contradicting the technical standards and recommendations, a safety framework is introduced, which makes use of state-of-the-art deep learning algorithms for human recognition and human body part identification. Particularly, a generic vision-based method for the determination of the occurring latencies is proposed. To this end, the different latency contributions from the recognition process up to the process of adapting the robot speed to an ISO-conform level are analyzed in detail.

关键词： Human-robot-collaboration Latency Determination ISO/TS 15066 Deep Learning Human Recognition Body Part Recognition HRC Safety Standard

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the Simulation Design of Robot Automatic Sorting and Palletizing Workstation Based on RobotStudio

The Simulation Design of Robot Automatic Sorting and Palleti...

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robotics, control and automation Engineering (RCAE), international conference on

作者： Guangzhi Li Yongqiang Li Haohong Han Changchun Automobile Industry Insititute Changchun China

ISBN: (数字)9781665459419

ISBN: (纸本)9781665459426

Taking the IRB460 robot as the research object, this paper builds an industrial robot virtual workstation for automatic sorting and palletizing in one-in-two-out mode simulated by the RobotStudio software. the conveyor chain transports the packaged materials to its end, and a Cognex camera is installed at the end to detect the color and position information of the packaged materials. then the robot grabs the material according to the feedback information from the camera. It stacks the red materials to the left side on the pallet, the green materials to the right side on the pallet. this paper creates a workstation model in SolidWorks, and builds a Smart component with dynamic properties in RobotStudio. then adds I/O signals, debugs and optimizes the workstation system, and analyzes the energy consumption during robot operations. the simulation results show that the robot can accurately grasp materials and sort and stack materials of different colors, which can shorten the design cycle and reduce the costs. It provides a theoretical reference for the construction of actual sorting and stacking workstations.

关键词： Energy consumption Service robots Robot vision systems Color Cameras Workstations Trajectory

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