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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

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Fusion of Multiview Images for EV Battery Disassembly 11

Fusion of Multiview Images for EV Battery Disassembly

引用

11th international conference on Control, Mechatronics and automation (ICCMA)

作者： Bilal, Muhammad Talha Tyapin, Ilya Choux, Martin Marie Hubert Univ Agder Dept Engg Sci Grimstad Norway

ISBN: (纸本)9798350315684

automation in Electric Vehicle (EV) Li-Ion Battery (LIB) pack disassembly is getting a lot of attraction with increased number of EVs being manufactured. Highly cluttered components placed in complex configurations present a unique challenge for object detection models. To get maximum coverage of the components in the EV battery pack, images from multiple viewpoints are considered and merged using Multiple-View Super Resolution (MVSR). The proposed method provides 7% higher accuracy in the screw detection task than the statistical methods previously derived [1].

关键词： Camera placement Image fusion object localization battery disassembly industrial robotics

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Lightweight Monocular Depth Estimation via Token-Sharing Transformer

Lightweight Monocular Depth Estimation via Token-Sharing Tra...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lee, Dong-Jae Lee, Jae Young Shon, Hyunguk Yi, Eojindl Park, Yeong-Hun Cho, Sung-Sik Kim, Junmo Korea Adv Inst Sci & Technol Dept Elect Engn Daejeon South Korea MOBIS Cheongju South Korea

ISBN: (纸本)9798350323658

Depth estimation is an important task in various robotics systems and applications. In mobile robotics systems, monocular depth estimation is desirable since a single RGB camera can be deployable at a low cost and compact size. Due to its significant and growing needs, many lightweight monocular depth estimation networks have been proposed for mobile robotics systems. While most lightweight monocular depth estimation methods have been developed using convolution neural networks, the Transformer has been gradually utilized in monocular depth estimation recently. However, massive parameters and large computational costs in the Transformer disturb the deployment to embedded devices. In this paper, we present a Token-Sharing Transformer (TST), an architecture using the Transformer for monocular depth estimation, optimized especially in embedded devices. The proposed TST utilizes global token sharing, which enables the model to obtain an accurate depth prediction with high throughput in embedded devices. Experimental results show that TST outperforms the existing lightweight monocular depth estimation methods. On the NYU Depth v2 dataset, TST can deliver depth maps up to 63.4 FPS in NVIDIA Jetson nano and 142.6 FPS in NVIDIA Jetson TX2, with lower errors than the existing methods. Furthermore, TST achieves real-time depth estimation of high-resolution images on Jetson TX2 with competitive results.

关键词： robotics

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Asking for Help: Failure Prediction in Behavioral Cloning through Value Approximation

Asking for Help: Failure Prediction in Behavioral Cloning th...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Gokmen, Cem Ho, Daniel Khansari, Mohi Stanford Univ Stanford CA 94305 USA Everyday Robots Mountain View CA 94043 USA Waymo Mountain View CA USA

ISBN: (纸本)9798350323658

Recent progress in end-to-end Imitation Learning approaches has shown promising results and generalization capabilities on mobile manipulation tasks. Such models are seeing increasing deployment in real-world settings, where scaling up requires robots to be able to operate with high autonomy, i.e. requiring as little human supervision as possible. In order to avoid the need for one-on-one human supervision, robots need to be able to detect and prevent policy failures ahead of time, and ask for help, allowing a remote operator to supervise multiple robots and help when needed. However, the black-box nature of end-to-end Imitation Learning models such as Behavioral Cloning, as well as the lack of an explicit state-value representation, make it difficult to predict failures. To this end, we introduce Behavioral Cloning Value Approximation (BCVA), an approach to learning a state value function based on and trained jointly with a Behavioral Cloning policy that can be used to predict failures. We demonstrate the effectiveness of BCVA by applying it to the challenging mobile manipulation task of latched-door opening, showing that we can identify failure scenarios with with 86% precision and 81% recall, evaluated on over 2000 real world runs, improving upon the baseline of simple failure classification by 10 percentage-points.

关键词： robotics imitation learning failure detection policy evaluation

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A Graph-based Optimization Framework for Hand-Eye Calibration for Multi-Camera Setups

A Graph-based Optimization Framework for Hand-Eye Calibratio...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Evangelista, Daniele Olivastri, Emilio Allegro, Davide Menegatti, Emanuele Pretto, Alberto Univ Padua Dept Informat Engn DEI Via Gradenigo 6-B I-35131 Padua Italy

ISBN: (纸本)9798350323658

Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of one or multiple cameras. Generally, this calibration is solved as a non-linear optimization problem, what instead is rarely done is to exploit the underlying graph structure of the problem itself. Actually, the problem of hand-eye calibration can be seen as an instance of the Simultaneous Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we present a pose-graph approach to the hand-eye calibration problem that extends a recent state-of-the-art solution in two different ways: i) by formulating the solution to eye-on-base setups with one camera;ii) by covering multi-camera robotic setups. The proposed approach has been validated in simulation against standard hand-eye calibration methods. Moreover, a real application is shown. In both scenarios, the proposed approach overcomes all alternative methods. We release with this paper an open-source implementation of our graph-based optimization framework for multi-camera setups.

关键词： robotics

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Comparison of Rating Scale and Pairwise Comparison methods for Measuring Human Co-worker Subjective Impression of Robot during Physical Human-Robot Collaboration

Comparison of Rating Scale and Pairwise Comparison Methods f...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Wang, Qiao Wang, Ziqi Carmichael, Marc G. Liu, Dikai Lin, Chin-Teng Univ Technol Sydney Fac Engn & Informat Technol Robot Inst Ultimo NSW 2007 Australia Univ Technol Sydney Fac Engn & Informat Technol Sch Comp Sci Australian Artificial Intelligence Inst 81 Broadway Ultimo NSW 2010 Australia

ISBN: (纸本)9798350384581;9798350384574

The Rating Scale method has been long deemed the standard for measuring subjective perceptions. However, in the field of physical human-robot collaboration (pHRC), its aptness should be put under scrutiny due to inherent challenges such as response bias, between-subject variations, and the granularity nature. Individual variances can introduce significant bias in the rating scale results. A high granularity in the scale could overwhelm participants, leading to unclear and biased responses, while a low granularity may gloss over the fine nuances of human feelings. Additionally, there's a notable risk of receiving careless responses, which compromise data reliability. Recognizing these challenges, this paper proposes the application of Pairwise Comparison (PC) in pHRC - an alternative survey technique that emphasizes direct comparisons between items on the defined criteria. By using the NASA Task Load Index (NASA-TLX) as a template, RS and PC questionnaires are designed and used in a series of pHRC experiments. Our preliminary findings suggest that PC is more precise and robust than the rating scale method. Compared to RS, PC fosters authentic participant interests in the experiment by intuitive question design and reducing the experimental duration. Besides, the accuracy and reliability of PC are also found to be consistent regardless of the variations in our experimental procedure design.

关键词： NASA

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Weakly-Supervised Depth Completion during Robotic Micromanipulation from a Monocular Microscopic Image

Weakly-Supervised Depth Completion during Robotic Micromanip...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Yang, Han Jin, Yufei Shane, Guangiao Wane, Yibin Zhengl, Yongbin Yul, Jiangfan Sung, Yu Zhang, Zhouran Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China Univ Toronto Dept Mech & Ind Engn Toronto ON Canada

ISBN: (纸本)9798350384581;9798350384574

Obtaining three-dimensional information, especially the z-axis depth information, is crucial for robotic micromanipulation. Due to the unavailability of depth sensors such as lidars in micromanipulation setups, traditional depth acquisition methods such as depth from focus or depth from defocus directly infer depth from microscopic images and suffer from poor resolution. Alternatively, micromanipulation tasks obtain accurate depth information by detecting the contact between an end-effector and an object (e.g., a cell). Despite its high accuracy, only sparse depth data can be obtained due to its low efficiency. This paper aims to address the challenge of acquiring dense depth information during robotic cell micromanipulation. A weakly-supervised depth completion network is proposed to take cell images and sparse depth data obtained by contact detection as input to generate a dense depth map. A two-stage data augmentation method is proposed to augment the sparse depth data, and the depth map is optimized by a network refinement method. The experimental results show that the MAE value of the depth prediction error is less than 0.3 mu m, which proves the accuracy and effectiveness of the method. This deep learning network pipeline can be seamlessly integrated with the robotic micromanipulation tasks to provide accurate depth information.

关键词： Biological Cell Manipulation automation at Micro/Nano Scales Deep Learning Depth Completion

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Optimal Scheduling of models and Horizons for Model Hierarchy Predictive Control

Optimal Scheduling of Models and Horizons for Model Hierarch...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Khazoom, Charles Heim, Steve Gonzalez-Diaz, Daniel Kim, Sangbae MIT Dept Mech Engn Dept 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798350323658

Model predictive control (MPC) is a powerful tool to control systems with non-linear dynamics and constraints, but its computational demands impose limitations on the dynamics model used for planning. Instead of using a single complex model along the MPC horizon, model hierarchy predictive control (MHPC) reduces solve times by planning over a sequence of models of varying complexity within a single horizon. Choosing this model sequence can become intractable when considering all possible combinations of reduced order models and prediction horizons. We propose a framework to systematically optimize a model schedule for MHPC. We leverage trajectory optimization (TO) to approximate the accumulated cost of the closed-loop controller. We trade off performance and solve times by minimizing the number of decision variables of the MHPC problem along the horizon while keeping the approximate closed-loop cost near optimal. The framework is validated in simulation with a planar humanoid robot as a proof of concept. We find that the approximated closed-loop cost matches the simulated one for most of the model schedules, and show that the proposed approach finds optimal model schedules that transfer directly to simulation, and with total horizons that vary between 1.1 and 1.6 walking steps.

关键词： Model predictive control

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Enhancing Cybersecurity with an Investigation into Network Intrusion Detection System Using Machine Learning 3

Enhancing Cybersecurity with an Investigation into Network I...

引用

3rd IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahman, Md Shohanur Tausif Islam, Wahid Ahmed Khan, Md. Rifat Daffodil International University Department of CSE Dhaka Bangladesh American International University Bangladesh Department of CSE Dhaka Bangladesh

ISBN: (纸本)9798331534400

The growing dependence on networked systems underscores the importance of robust security measures to fend off potential intrusions. This research undertakes the task of assessing different machine-learning models concerning network intrusion detection. Following an in-depth examination and comparison, models such as Decision Tree, Logistic Regression, XGBoost, and Random Forest exhibit promising aptitude in precisely identifying potential intrusions. Notably from our proposed model, Random Forest emerged as the top performer among these models, attaining the highest accuracy scores (99%). Its proficiency lies in delivering precise predictions and effectively capturing the intricacies of network intrusion detection. However, the Support Vector Machine (SVM) displayed limitations in its performance throughout this research. These discoveries illuminate the positive and negative aspects of these patterns, providing valuable knowledge about their efficiency in detecting intrusions. The study underscores the necessity for further fine-tuning of successful models and the exploration of advanced techniques to fortify network security. This research, as a whole, signifies an initial and crucial stride in the direction of formulating network intrusion detection systems that are more effective and reliable within the realm of network security. © 2024 IEEE.

关键词： Network intrusion

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Bare Printed Circuit Board Defects Localization and Detection Using YOLOv5 models 6

Bare Printed Circuit Board Defects Localization and Detectio...

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6th international conference on robotics and automation in Industry, ICRAI 2024

作者： Khan, Wajih Ahmed Hamza, Amir Akram, Muhammad Usman Khan, Umar Shahbaz Nawaz, Tahir National University of Sciences and Technology Dept. of Mechatronics Engineering Islamabad Pakistan National University of Sciences and Technology Dept. of Computer Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

A printed circuit board (PCB) is a vital component of any electronic device. Over the years, the need to manufacture PCBs in large volumes has become a necessity due to technological advancement and the expansion of the consumer market. Small defects in the PCB signal trace can have adverse consequences on the functionality of an electronic device. Initially, the quality control process of PCB inspection was carried out manually which was not only tedious but also susceptible to human error. To automate this process, different techniques were developed using classical machine vision that comprised reference comparison, subtraction of images and morphological operations. The methods were effective and reduced inspection time but were sensitive to noise, rotation, and scaling of images. It was difficult to detect defects in complex patterns and only a small fraction of defects could be detected through classical techniques. In this study, four YOLOv5 models were tested for the classification and localization of defects in PCBs. Transfer learning on the bare PCB defect dataset was implemented using pretrained weights of YOLOv5. The PCB dataset was composed of 2,079 images with 6 classes of PCB defects. Data augmentations were carried out to increase the dataset which enhanced performance and generalizability of the models. The models were trained and tested at different image resolutions. Promising experimental results were achieved with the medium YOLOv5 model reaching a mean average precision of 96.92%. © 2024 IEEE.

关键词： Machine vision

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Probabilistic Uncertainty Quantification of Prediction models with Application to Visual Localization

Probabilistic Uncertainty Quantification of Prediction Model...

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IEEE international conference on robotics and automation (ICRA)

作者： Chen, Junan Monica, Josephine Chao, Wei-Lun Campbell, Mark Cornell Univ Mech & Aerosp Engn Dept Ithaca NY 14850 USA Ohio State Univ Dept Comp Sci & Engn Columbus OH USA

ISBN: (纸本)9798350323658

The uncertainty quantification of prediction models (e.g., neural networks) is crucial for their adoption in many robotics applications. This is arguably as important as making accurate predictions, especially for safety-critical applications such as self-driving cars. This paper proposes our approach to uncertainty quantification in the context of visual localization for autonomous driving, where we predict locations from images. Our proposed framework estimates probabilistic uncertainty by creating a sensor error model that maps an internal output of the prediction model to the uncertainty. The sensor error model is created using multiple image databases of visual localization, each with ground-truth location. We demonstrate the accuracy of our uncertainty prediction framework using the Ithaca365 dataset, which includes variations in lighting, weather (sunny, snowy, night), and alignment errors between databases. We analyze both the predicted uncertainty and its incorporation into a Kalman-based localization filter. Our results show that prediction error variations increase with poor weather and lighting condition, leading to greater uncertainty and outliers, which can be predicted by our proposed uncertainty model. Additionally, our probabilistic error model enables the filter to remove ad hoc sensor gating, as the uncertainty automatically adjusts the model to the input data.

关键词： Lighting

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