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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Walid K. Shaker Alexandr Klimchik Institute of Robotics and Computer Vision Innopolis University Republic of Tatarstan Russia School of Computer Science University of Lincoln Lincoln United Kingdom

The springback effect is a common occurrence in incremental forming, where the formed workpiece elastically deforms and slightly shifts from the desired shape after the tool is released. This phenomenon causes an error between the target and obtained shape, leading to reduced geometric accuracy. It is is a significant challenge in incremental forming and it is a reason why the process has lower accuracy compared to conventional forming methods. This paper presents an off-line springback effect compensation model aiming to generate an optimized toolpath that accounts for the material springback effect. The model is based on an off-line numerical simulation conducted on Abaqus/CAE software. The results demonstrated that the proposed model can effectively reduce the error between the desired and obtained shape by 31.8% for aluminum, 63.2% for copper, and 63.1 % for magnesium.

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Self-Prompting Large vision Models for Few-Shot Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Wu, Qi Zhang, Yuyao Elbatel, Marawan The Hong Kong University of Science and Technology Hong Kong Computer Vision and Robotics Institute University of Girona Spain

Recent advancements in large foundation models have shown promising potential in the medical industry due to their flexible prompting capability. One such model, the Segment Anything Model (SAM), a prompt-driven segmentation model, has shown remarkable performance improvements, surpassing state-of-the-art approaches in medical image segmentation. However, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. In this paper, we propose a novel perspective on self-prompting in medical vision applications. Specifically, we harness the embedding space of SAM to prompt itself through a simple yet effective linear pixel-wise classifier. By preserving the encoding capabilities of the large model, the contextual information from its decoder, and leveraging its interactive promptability, we achieve competitive results on multiple datasets (i.e. improvement of more than 15% compared to fine-tuning the mask decoder using a few images). Our code is available at https://***/PeterYYZhang/few-shot-self-prompt-SAM Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Task-Oriented Grasp Prediction with Visual-Language Inputs

arXiv

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arXiv 2023年

作者： Tang, Chao Huang, Dehao Meng, Lingxiao Liu, Weiyu Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta United States

To perform household tasks, assistive robots receive commands in the form of user language instructions for tool manipulation. The initial stage involves selecting the intended tool (i.e., object grounding) and grasping it in a task-oriented manner (i.e., task grounding). Nevertheless, prior researches on visual-language grasping (VLG) focus on object grounding, while disregarding the fine-grained impact of tasks on object grasping. Task-incompatible grasping of a tool will inevitably limit the success of subsequent manipulation steps. Motivated by this problem, this paper proposes GraspCLIP, which addresses the challenge of task grounding in addition to object grounding to enable task-oriented grasp prediction with visual-language inputs. Evaluation on a custom dataset demonstrates that GraspCLIP achieves superior performance over established baselines with object grounding only. The effectiveness of the proposed method is further validated on an assistive robotic arm platform for grasping previously unseen kitchen tools given the task specification. Our presentation video is available at: https://***/watch?v=e1wfYQPeAXU. Copyright © 2023, The Authors. All rights reserved.

关键词： Visual languages

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Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification

arXiv

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arXiv 2023年

作者： Elbatel, Marawan Wang, Hualiang Martí, Robert Fu, Huazhu Li, Xiaomeng The Hong Kong University of Science and Technology Hong Kong Computer Vision and Robotics Institute University of Girona Spain Singapore

In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners. In this paper, we study the inter-client intra-class variations with publicly available self-supervised auxiliary networks. Specifically, we find that employing a shared auxiliary pre-trained model, like MoCo-V2, locally on every client yields consistent divergence measurements. Based on these findings, we derive a dynamic balanced model aggregation via self-supervised priors (MAS) to guide the global model optimization. Fed-MAS can be utilized with different local learning methods for effective model aggregation toward a highly robust and unbiased global model. Our code is available at https://***/xmed-lab/Fed-MAS. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Distortion Correction of AUV-acquired Side-Scan Sonar Data

Distortion Correction of AUV-acquired Side-Scan Sonar Data

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OCEANS

作者： Valerio Franchi Hayat Rajani Rafael Garcia Borja Martinez-Clavel Nuno Gracias Computer Vision and Robotics Research Institute (VICOROB) University of Girona Girona Spain Tecnoambiente (A Tradebe Company) Barcelona Spain

Side scan sonars (SSS) are types of sonars that produce representations of the sea-bed by emitting and receiving acoustic fan-shaped pulses perpendicularly to the motion of the sensor in the water. Generally, SSS are deployed from a towed vehicle pulled from a boat at high speed to avoid oscillatory motions and thus achieve a clean and consistent sea-bed scan. Our approach targets the use of SSSs on small AUVs with limited computing power, and travelling at slow speeds, where the waterfall is directly used for real-time classfication. Due to the increased oscillatory motion, the SSS waterfall tends to have significant distortion, that will preclude the classifier from obtaining correct results. The algorithm described in this paper performs a geometric correction of the sonar scanlines by taking into account the AUV’s change in height, yaw and pitch to calculate correctly their positions on the seabed. The data is mapped into a 2D grid using the scanline intensities to form a mosaic, and the missing mosaic pixels are interpolated using a Gaussian-based kernel. The waterfall is recreated by considering a modified AUV trajectory with smooth changes in heading and a null pitch. The results show a significant improvement in the waterfall output, removing unwanted distortions and displaying the seabed structures with higher geometric consistency. In addition, the concentration of the ping data on the waterfall is constant all around, compared to the varying contribution density in the original one.

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Robot-based Incremental Forming: Springback Effect Compensation Model for Various Materials *

Robot-based Incremental Forming: Springback Effect Compensat...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Walid K. Shaker Alexandr Klimchik Institute of Robotics and Computer Vision Innopolis University Republic of Tatarstan Russia Lincoln Centre for Autonomous Systems (L-CAS) School of Computer Science University of Lincoln Lincoln United Kingdom

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The springback effect refers to the tendency of a material to partially return to its original shape after deformation, especially in incremental metal forming processes. It poses a significant challenge in achieving precise and accurate final shapes, often requiring compensation strategies to reduce its impact. This study focuses on the evaluation and development related to a springback compensation model for incremental sheet metal forming processes. The proposed model aims to optimize toolpaths to counteract springback by addressing deviations between the formed and intended shapes. Performance assessment involved offline numerical simulations using Abaqus/CAE software across various materials. Results demonstrate significant efficacy, with mean error reductions exceeding 60% across different materials. Additionally, the model maintains sheet thickness, preserving material strength during forming.

关键词： Accuracy Shape Deformation Lead Numerical simulation Software Numerical models Magnesium Information technology Robots

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Robotic Approach Trajectory Using Reinforcement Learning with Dual Quaternions

Robotic Approach Trajectory Using Reinforcement Learning wit...

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Iberian robotics Conference (ROBOT)

作者： Daniel Frau-Alfaro Santiago T. Puente Ignacio De Loyola Páez-Ubieta Edison Velasco-Sánchez AUtomatics RObotics and Artificial Vision Lab University Institute for Computer Research San Vicente Spain University Institute for Engineering Research University Miguel Hernández Elche Spain

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

Manipulation tasks in robotics usually involve two phases: an approach to the object and the grasp itself. The first action allows the robot to reach a certain pose in space that is likely to allow the object to be manipulated. Reinforcement Learning (RL) techniques allow a policy to be learned through experience given a set of states and actions, so this is a powerful tool for developing controllers for specific tasks such as positioning the robot in a particular point in space. However, when manipulating an object, orientation is as relevant as position. For this reason, a method of RL for positioning the robot's end effector in a suitable position and orientation for manipulation in simulation is presented. This approach models the problem of computing the distance for the reward function using dual quaternions parameterisation, an element that can represent the pose and attitude of a rigid body in Euclidean space in a compact way without having to apply any constraints.

关键词： Training Quaternions Computational modeling Process control Reinforcement learning Aerospace electronics End effectors Trajectory Robots

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Complexity based investigation in collaborative assembly scenarios via non intrusive techniques

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Procedia computer Science 2023年 217卷 478-485页

作者： Sotirios Panagou Monica Sileo Konstantinos Papoutsakis Fabio Fruggiero Ammar Qammaz Antonis Argyros School of Engineering University of Basilicata 85100 Potenza Italy Computational Vision and Robotics Laboratory Institute of Computer Science Foundation for Research and Technology – Hellas 70013 Heraklion Greece

Human and robot collaboration in assembly tasks is an integral part in modern manufactories. Robots provide advantages in both process and productivity with their repeatability and usability in different tasks, while human operators provide flexibility and can act as safeguards. However, process complexity increases which can lower the overall quality. Increased complexity can negatively influence decision making due to cognitive load on human operators, which can lead to lower quality, be it product, process or human work. Moreover, it can lead to safety risks, human-system error and accidents. In this work, we present the preliminary results on an experiment performed with student-participants, based on an assembly task. The experiment was set up to emulate an industrial assembly, and data collection was performed through qualitative and non-intrusive quantitative methods. Questionnaires were used to assess perceptual task complexity and cognitive load, while a stereo camera provided recordings for after-task analysis on process errors and human work quality based on a 3D skeleton-based human pose estimation and tracking method. The aim of the study is to investigate causes of errors and implications on quality. Future direction of the work is discussed.

关键词： collaboration assembly task complexity cognitive load human pose estimation tracking

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Inspecting Mega Solar Plants through computer vision and Drone Technologies

Inspecting Mega Solar Plants through Computer Vision and Dro...

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2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Ahmed, Syed Umaid Affan, Muhammad Raza, Muhammad Ilyas Harris Hashmi, Muhammad National University of Computer and Emerging Sciences Department of Computer Sciences Karachi Pakistan University of Toulon SeaTech School of Engineering La Garde France Vision Innopolis University Institute of Robotics and Computer Innopolis Russia University of Alabama at Birmingham Department of Computer Science Birmingham United States

ISBN: (纸本)9798350345933

This research presents a unique approach for monitoring the large-scale grid-connected photovoltaic modules in solar power plants using state-of-art object detection YOLOv5 algorithm and classical image processing techniques. We have highlighted an integral part of the fully automated system in which a drone takes flight over the solar park and shoot the videos. Videos are preprocessed and used for trained YOLOv5 model to recognize the clean and dirty panels. The process is defined for a selected site and can be implemented using a Raspberry Pi. This system processes the images taken by drones, generates a report, and sends it to the concerned department automatically via email every day so that timely maintenance can be done for the long-life and safe operation of solar arrays. The inspection timeline for the same process was about one hundred and twenty hours, reduced to five minutes. It means that 99.93% of the time is saved through vision and robust automation techniques. © 2022 IEEE.

关键词： Drones

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Backbone Based Feature Enhancement for Object Detection 15th

Backbone Based Feature Enhancement for Object Detection

引用

15th Asian Conference on computer vision, ACCV 2020

作者： Ji, Haoqin Lu, Weizeng Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030695347

FPN (Feature Pyramid Networks) and many of its variants have been widely used in state of the art object detectors and made remarkable progress in detection performance. However, almost all the architectures of feature pyramid are manually designed, which requires ad hoc design and prior knowledge. Meanwhile, existing methods focus on exploring more appropriate connections to generate features with strong semantics features from inherent pyramidal hierarchy of deep ConvNets (Convolutional Networks). In this paper, we propose a simple but effective approach, named BBFE (Backbone Based Feature Enhancement), to directly enhance the semantics of shallow features from backbone ConvNets. The proposed BBFE consists of two components: reusing backbone weight and personalized feature enhancement. We also proposed a fast version of BBFE, named Fast-BBFE, to achieve better trade-off between efficiency and accuracy. Without bells and whistles, our BBFE improves different baseline methods (both anchor-based and anchor-free) by a large margin (∼ 2.0 points higher AP) on COCO, surpassing common feature pyramid networks including FPN and PANet. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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