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International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Masaharu Nagashima Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka city Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong-si Korea

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Digital Holographic Microscopy (DHM) can obtain three-dimensional (3D) information about the fine structure of an object by utilizing the phase information of coherent light. In DHM, during the process of reconstructing the shape of an object, phase information is extracted by windowing the sideband containing the phase information. The 3D information of the object is then obtained from the acquired phase information. When windowing the sideband in a narrow range, it can remove noise caused by the DC spectrum, but detailed shape information of the object is lost. On the other hand, when windowing in a wide range, detailed shape information of the object can be obtained, but noise from the DC spectrum increases. To solve this trade-off relationship, we propose a method of determining the range for windowing the sideband using a function and then averaging the windowed sidebands. This proposed method can reduce the impact of noise from the DC spectrum and enables the acquisition of the detailed shape information. In this paper, we explain the reconstruction algorithm of this method and compare it with conventional methods and the previous study, phase differences averaging (PDA).

关键词： Three-dimensional displays Filtering Shape Microscopy Noise Noise reduction Reconstruction algorithms Amplitude modulation Data mining Informatics

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Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping

arXiv

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arXiv 2024年

作者： Gouda, Anas Schwarz, Max Reining, Christopher Behnke, Sven Kirchheim, Alice TU Dortmund Germany Autonomous Intelligent Systems - Computer Science VI Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany Fraunhofer IML Germany

Foundation models are a strong trend in deep learning and computer vision. These models serve as a base for applications as they require minor or no further fine-tuning by developers to integrate into their applications. Foundation models for zero-shot object segmentation such as Segment Anything (SAM) output segmentation masks from images without any further object information. When they are followed in a pipeline by an object identification model, they can perform object detection without training. Here, we focus on training such an object identification model. A crucial practical aspect for an object identification model is to be flexible in input size (number of input images). As object identification is an image retrieval problem, a suitable method should handle multi-query multigallery situations without constraining the number of input images (e.g. by having fixed-size aggregation layers). The key solution to train such a model is the centroid triplet loss (CTL), which aggregates image features to their centroids. CTL yields high accuracy, avoids misleading training signals and keeps the model input size flexible. In our experiments, we establish a new state of the art on the ArmBench object identification task, which shows general applicability of our model. We furthermore demonstrate an integrated unseen object detection pipeline on the challenging HOPE dataset, which requires fine-grained detection. There, our pipeline matches and surpasses related methods which have been trained on dataset-specific data. Code and pretrained models are available. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Modular Sensor Integration into Soft Robots using Stretchable Wires for Nuclear Infrastructure Inspection and Radiation Spectroscopy

Modular Sensor Integration into Soft Robots using Stretchabl...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Calder Wilson Joseph Karam Callen Votzke Farhan Rozaidi Camille J. Palmer Ross L. Hatton Matthew L. Johnston School of Electrical Engineering and Computer Science Corvallis OR USA Collaborative Robotics and Intelligent Systems Institute Corvallis OR USA School of Nuclear Science & Engineering Oregon State University Corvallis OR USA

Soft robots are uniquely suited for applications in inspection, search and rescue, and exploration in confined and unstructured environments. Leveraging the benefits of these soft robots will increasingly require the integration of equally compliant electronic components for sensing, actuation, control, and computation to maintain mechanical conformability at the system level. In this work, we demonstrate the integration of modular electronic sensors into a robotic snake platform using stretchable interconnects. Aimed at applications in nuclear infrastructure inspection, included sensors include visual perception and on-board radiation spectroscopy. In addition to high mechanical compliance, the stretchable interconnect enables physical separation of analog sensors and digital electronics for use in radiation environments. We present details of stretchable electronics fabrication and integration, standalone validation of integrated sensors, and field test results from a simulated nuclear infrastructure inspection task.

关键词： Mechanical sensors Fabrication Spectroscopy Visualization Wires Soft robotics Inspection

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Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition

Shift from Texture-bias to Shape-bias: Edge Deformation-base...

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International Conference on computer Vision (ICCV)

作者： Xilin He Qinliang Lin Cheng Luo Weicheng Xie Siyang Song Feng Liu Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institue of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing University of Leicester

Recent studies have shown the vulnerability of CNNs under perturbation noises, which is partially caused by the reason that the well-trained CNNs are too biased toward the object texture, i.e., they make predictions mainly based on texture cues. To reduce this texture-bias, current studies resort to learning augmented samples with heavily perturbed texture to make networks be more biased toward relatively stable shape cues. However, such methods usually fail to achieve real shape-biased networks due to the insufficient diversity of the shape cues. In this paper, we propose to augment the training dataset by generating semantically meaningful shapes and samples, via a shape deformation-based online augmentation, namely as SDbOA. The samples generated by our SDbOA have two main merits. First, the augmented samples with more diverse shape variations enable networks to learn the shape cues more elaborately, which encourages the network to be shape-biased. Second, semantic-meaningful shape-augmentation samples could be produced by jointly regularizing the generator with object texture and edge-guidance soft constraint, where the edges are represented more robustly with a self information guided map to better against the noises on them. Extensive experiments under various perturbation noises demonstrate the obvious superiority of our shape-bias-motivated model over the state of the arts in terms of robustness performance. Code is available at https://***/C0notSilly/-ICCV-23-Edge-Deformation-based-Online-Augmentation.

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Kalman filtering optimization in Digital Holographic Microscopy (DHM)

Kalman filtering optimization in Digital Holographic Microsc...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Taishi Ono Jongpil Jeong Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Republic of Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Digital Holographic Microscopy (DHM) can obtain a 3D profile of a micro-object. However, the obtained 3D profile includes the noise. One reason for this is the problem of the extraction method of phase information in DHM. Conventional methods cannot extract only the phase information from the Fourier domain, and noise components are included. To solve this problem, we propose a use of the Kalman filter in DHM systems. In this method, multiple frequency components are obtained from the Fourier domain as time-series data for Kalman filtering. 3D profiles are obtained from each frequency component and stored in an array as time-series data. This is used to remove randomly generated noise. In this process, it remains unclear how many 3D profiles obtained from frequency components are needed as time-series data. Therefore, the optimization of the number of windowed sidebands in the Fourier domain is the objective in this paper. The objective is to use the Kalman filter efficiently by performing quantitative evaluation using image quality metrics and optimization based on processing time.

关键词： Measurement Time-frequency analysis Three-dimensional displays Accuracy Filtering Microscopy Noise Kalman filters Data mining Optimization

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A research on optimization of fog estimation process in Peplography

A research on optimization of fog estimation process in Pepl...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Seiya Ono Jin-Ung Ha Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Republic of Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

In recent years, imaging techniques are essential in our lives, such as security cameras and drive recorders. However, the visualization quality of these techniques sometimes is affected by scattering media, such as smoke at a fire or fog during driving. To solve this problem, Peplography has been proposed. Peplography is an optical algorithm that emphasizes the object information presented in the scattering media, even when the density of the scattering media varies within the image. On the other hand, Peplography estimates the scattering media in each local region, and when the region is not defined appropriately, the estimation of the scattering media will not be visualized. Therefore, the purpose of this research is to find the optimal processing for each environment by experiment with various situations, such as when the size of the object in the image is small, when the density of the scattering media is heavy, and when the target object is various.

关键词： YOLO Visualization Optical recording Scattering Process control Estimation Media Security Optical scattering Optimization

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Efficient Arm Motion Planning with Reduced Viapoints for Mars Rover Operations

Efficient Arm Motion Planning with Reduced Viapoints for Mar...

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Space robotics (iSpaRo), International Conference on

作者： Justin Huang Valen Yamamoto Ana Warner Jet Propulsion Laboratory California Institute of Technology Pasadena CA Department of Electrical Engineering and Computer Science UC Irvine Irvine CA Department of Robotics University of Michigan Ann Arbor MI

ISBN: (数字)9798350367232

ISBN: (纸本)9798350367249

This paper describes additions and enhancements we made to the standard RRT-Connect motion planning algorithm to suit the unique needs of operating the robotic arm on the Perseverance Mars Rover. One constraint we have is to maintain a minimum distance between the terrain and any part of the arm hardware. Another goal specific to rover planning is to minimize the number of viapoints in our motion plans. These algorithms should also run efficiently so that rover planners can generate arm motion sequences in time for uplink windows. We applied the RRT-Connect algorithm to the rover’s robotic arm and tested its performance, and we found that it could be used successfully for rover planning with a few changes. First, we implemented the terrain clearance requirement using a software tool called Proximeter. Second, we simplify the paths computed by RRT-Connect to minimize the number of viapoints in a motion plan. Third, we leverage Proximeter to reduce the number of expensive terrain collision checks done by the algorithm, speeding up motion planning.

关键词： Space vehicles Mars Costs Software algorithms Manipulators Hardware Planning

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Development of a navigation performance evaluation system

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science China(Information sciences) 2020年第12期63卷 263-265页

作者： Fangfang ZHAO Wei ZHANG Shuzhi Sam GE School of Computer Science and Engineering Center for RoboticsUniversity of Electronic Science and Technology of China Department of Electrical and Computer Engineering National University of Singapore Institute for Future (IFF) Qingdao University

Dear editor,Owing to little exploration experience and great technical difficulty of deep space exploration autonomous navigation, the corresponding navigation performance evaluation system is seldom perfected [1].

关键词： Autonomous navigation Performance evaluation Evaluation system Evaluation indicator Evaluation method

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Dynamic Inference on Graphs using Structured Transition Models

arXiv

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arXiv 2022年

作者： Saxena, Saumya Kroemer, Oliver Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA15123 United States

Enabling robots to perform complex dynamic tasks such as picking up an object in one sweeping motion or pushing off a wall to quickly turn a corner is a challenging problem. The dynamic interactions implicit in these tasks are critical towards the successful execution of such tasks. Graph neural networks (GNNs) provide a principled way of learning the dynamics of interactive systems but can suffer from scaling issues as the number of interactions increases. Furthermore, the problem of using learned GNN-based models for optimal control is insufficiently explored. In this work, we present a method for efficiently learning the dynamics of interacting systems by simultaneously learning a dynamic graph structure and a stable and locally linear forward model of the system. The dynamic graph structure encodes evolving contact modes along a trajectory by making probabilistic predictions over the edges of the graph. Additionally, we introduce a temporal dependence in the learned graph structure which allows us to incorporate contact measurement updates during execution thus enabling more accurate forward predictions. The learned stable and locally linear dynamics enable the use of optimal control algorithms such as iLQR for long-horizon planning and control for complex interactive tasks. Through experiments in simulation and in the real world, we evaluate the performance of our method by using the learned interaction dynamics for control and demonstrate generalization to more objects and interactions not seen during training. We introduce a control scheme that takes advantage of contact measurement updates and hence is robust to prediction inaccuracies during execution. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamics

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Multi-Task Self-Supervised Learning for Image Segmentation Task

arXiv

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arXiv 2023年

作者： Gao, Lichun Khamesra, Chinmaya Kumbhar, Uday Aglawe, Ashay Computer Science Department Worcester Polytechnic Institute WorcesterMA United States Robotics Department Worcester Polytechnic Institute WorcesterMA United States Data Science Department Worcester Polytechnic Institute WorcesterMA United States

Thanks to breakthroughs in AI and Deep learning methodology, computer vision techniques are rapidly improving. Most computer vision applications require sophisticated image segmentation to comprehend what is image and to make an analysis of each section easier. Training deep learning networks for semantic segmentation required a large amount of annotated data, which presents a major challenge in practice as it is expensive and labor-intensive to produce such data. The paper presents 1. Self-supervised techniques to boost semantic segmentation performance using multi-task learning with Depth prediction and Surface Normalization . 2. Performance evaluation of the different types of weighing techniques (UW, Nash-MTL) used for Multi-task learning. NY2D dataset was used for performance evaluation. According to our evaluation, the Nash-MTL method outperforms single task learning(Semantic Segmentation). Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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