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

共 24622 条记录，以下是301-310 订阅

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Failure-aware Policy Learning for Self-assessable robotics Tasks

Failure-aware Policy Learning for Self-assessable Robotics T...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Xu, Kechun Chen, Runjian Zhao, Shuqi Li, Zizhang Yu, Hongxiang Chen, Ci Wang, Yue Xiong, Rong Zhejiang Univ Hangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350323658

Self-assessment rules play an essential role in safe and effective real-world robotic applications, which verify the feasibility of the selected action before actual execution. But how to utilize the self-assessment results to re-choose actions remains a challenge. Previous methods eliminate the selected action evaluated as failed by the self-assessment rules, and re-choose one with the next-highest affordance (i.e. process-of-elimination strategy [1]), which ignores the dependency between the self-assessment results and the remaining untried actions. However, this dependency is important since the previous failures might help trim the remaining over-estimated actions. In this paper, we set to investigate this dependency by learning a failureaware policy. We propose two architectures for the failureaware policy by representing the self-assessment results of previous failures as the variable state, and leveraging recurrent neural networks to implicitly memorize the previous failures. Experiments conducted on three tasks demonstrate that our method can achieve better performances with higher task success rates by less trials. Moreover, when the actions are correlated, learning a failure-aware policy can achieve better performance than the process-of-elimination strategy.

关键词： Recurrent neural networks

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nerf2nerf: Pairwise Registration of Neural Radiance Fields

nerf2nerf: Pairwise Registration of Neural Radiance Fields

引用

IEEE international conference on robotics and automation (ICRA)

作者： Goli, Lily Rebain, Daniel Sabour, Sara Garg, Animesh Tagliasacchi, Andrea Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada SFU Burnaby BC Canada Univ British Columbia Vancouver BC Canada NVIDIA Santa Clara CA USA Google Res San Francisco CA USA

ISBN: (纸本)9798350323658

We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i.e. ICP) to operate on Neural Radiance Fields (NeRF) neural 3D scene representations trained from collections of calibrated images. NeRF does not decompose illumination and color, so to make registration invariant to illumination, we introduce the concept of a "surface field" - a field distilled from a pre-trained NeRF model that measures the likelihood of a point being on the surface of an object. We then cast nerf2nerf registration as a robust optimization that iteratively seeks a rigid transformation that aligns the surface fields of the two scenes. We evaluate the effectiveness of our technique by introducing a dataset of pre-trained NeRF scenes - our synthetic scenes enable quantitative evaluations and comparisons to classical registration techniques, while our real scenes demonstrate the validity of our technique in real-world scenarios. Additional results available at: https://***

关键词： Iterative methods

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ISS/JEM crew-task analysis to support astronauts with intra-vehicular robotics 32

ISS/JEM crew-task analysis to support astronauts with intra-...

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32nd IEEE international conference on Robot and Human Interactive Communication (RO-MAN)

作者： Yamaguchi, S. P. Itakura, R. Inagaki, T. Japan Aerosp Explorat Agcy JAXA Human Spaceflight Technol Ctr Tsukuba Ibaraki Japan Tohoku Univ Space Robot Lab Sendai Miyagi Japan

ISBN: (纸本)9798350336702

This paper proposes employing robotics and automation technology to manned spaceflight to manage repetitive activities for the crew. Current operations on the international Space Station (ISS) were analyzed to better determine target tasks for automation. Crew tasks on the ISS are precisely planned by the ground control and planning teams and then monitored and documented. In this study, astronauts' tasks related to JAXA's Japanese Experimental Module were analyzed based on their task names and categorized to determine consistently occulting, repetitive types of work. Based on the categorized tasks' durations, three categories were anticipated for future automation: sample and equipment retrieval (swapping), logistics (cargo handling), and monitoring. This paper discusses automation methods for each category based on JAXA's ground and in-orbit robotic research and development.

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A Review on Computational methods Based on Deep Learning and Transfer Learning Techniques for Malaria Detection 10

A Review on Computational Methods Based on Deep Learning and...

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10th international conference on automation, robotics, and Applications, ICARA 2024

作者： Zakariya, Abbas Muhammad Adak, Muhammed Fatih Sakarya University Department of Computer Engineering Sakarya Turkey

ISBN: (纸本)9798350394245

Malaria continues to pose a significant health challenge worldwide, especially in most of under developing countries where most often there is limited healthcare resources. A death toll of nearly a million every year from two decades back is recorded by the World Health Organization. Doctors employ microscopic examination of patient's blood to identify the presence of malaria parasites in its initial phases, yet this method encounters issues related to both time consumption and accuracy. The need for precise, prompt, and effective detection of malaria parasites is crucial in the fight against this infectious disease. This review aims to highlight the recent most effective both preprocessing and classification methodologies used for detecting malaria parasites. © 2024 IEEE.

关键词： Diseases

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Robotic Method and Instrument to Efficiently Synthesize Faulty Conditions and Mass-Produce Faulty-Conditioned Data for RotaryMachines

Robotic Method and Instrument to Efficiently Synthesize Faul...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Yeung, Yip Fun Xia, Fangzhou Covarrubias, Juliana Furokawa, Mikio Hirano, Takayuki Youcef-Toumi, Kamal MIT Dept Mech Engn Cambridge MA 02139 USA Japan Steel Works LTD Hiroshima Plant Hiroshima Japan

ISBN: (纸本)9798350323658

Condition synthesis is vital for generating data for fault detection and diagnosis studies. Traditional methods rely heavily on human labor. This study proposes a robotic method and its instrument to efficiently synthesize faulty conditions and mass-produce data to develop fault detection and diagnosis algorithms. The first contribution is the formalization of a new approach called Robotic Condition Synthesis, which shifts the traditionally labor-intensive task of condition synthesis to a robot-based force control task. The second contribution is developing a new robotic manipulator, which is more effective than current lab-grade robots for the tasks involved in the Robotic Condition Synthesis. The third contribution is empirical evidence of the superiority of this new robot in performing the Robotic Condition Synthesis tasks. This study also explores the potential of the new robot by conducting a three-dimensional system identification of a rotordynamic plant, which lays the foundation for more advanced Robotic Condition Synthesis policies in the future.

关键词： Sustainable Production and Service automation Failure Detection and Recovery Engineering for Robotic Systems

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EVOLVING INDUSTRY 4.0: A METHODICAL APPROACH TO OPTIMIZING IOT ONTOLOGIES FOR ENHANCED automation 12

EVOLVING INDUSTRY 4.0: A METHODICAL APPROACH TO OPTIMIZING I...

引用

12th IEEE international Black Sea conference on Communications and Networking (BlackSeaCom)

作者： Ushakov, Serhii Globa, Larysa Kurdecha, Vasyl Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst NN ITS Kyiv Ukraine

ISBN: (纸本)9798350351866;9798350351859

This research advances Industry 4.0 by optimizing IoT ontology models, crucial for enhancing industrial automation's efficiency and adaptability. Employing a mixed-methods approach that integrates a systematic literature review, expert interviews, and case analyses, we identify and address significant gaps in existing IoT ontologies. Our findings reveal that improved interoperability, scalability, and adaptability in IoT ontologies can significantly elevate the robustness and efficiency of Industry 4.0 infrastructures. The study introduces targeted refinement strategies that promise to future-proof IoT ontologies, thereby facilitating more resilient and dynamic industrial automation environments. This work not only sheds light on the current state of IoT ontologies but also sets a forward-looking agenda for their continuous improvement in the evolving landscape of Industry 4.0.

关键词： Industry 4.0. IoT Ontology models Industrial automation Interoperability Future-proofing IoT Mixed-methods Research Scalability

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Leveraging Swarm robotics for Environmental Radiation Monitoring: An Interdisciplinary Perspective 9

Leveraging Swarm Robotics for Environmental Radiation Monito...

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9th international conference on Mechanical Engineering and robotics Research, ICMERR 2025

作者： Ardiny, Hadi Beigzadeh, Amirmohammad Research School NSTRI Radiation Applications Tehran Iran

ISBN: (纸本)9798331509262

This study develops interdisciplinary models and algorithms to enhance the coordination and autonomy of multiple robots in radioactive environments. By integrating dynamic radiation source behaviors into a robotic simulator, we aim to create a comprehensive tool for simulating and testing complex robotic behaviors in hazardous settings. The scenario of exploring and mapping radioactive environments was chosen due to the significant challenges posed by potential hazards. Deploying multiple autonomous robots can expedite exploration and increase the likelihood of detecting radioactive sources. Our study evaluates various factors, including the number of robots, their radiation tolerance, and their efficiency in navigating and mapping contaminated areas. The findings reveal the substantial potential of swarm robotics in nuclear applications and highlight the importance of interdisciplinary collaboration in developing innovative solutions for hazardous environments. These results demonstrate that swarm robotics can significantly enhance operational efficiency, safety, and precision in radiation monitoring and response. © 2025 IEEE.

关键词： Radiation hazards

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Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors

Collision-aware In-hand 6D Object Pose Estimation using Mult...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Caddeo, Gabriele M. Piga, Nicola A. Bottarel, Fabrizio Natale, Lorenzo Ist Italiano Tecnol Humanoid Sensing & Percept Genoa Italy Univ Genoa DIBRIS Via AllOpera Pia 13 Genoa Italy

ISBN: (纸本)9798350323658

In this paper, we address the problem of estimating the in-hand 6D pose of an object in contact with multiple vision-based tactile sensors. We reason on the possible spatial configurations of the sensors along the object surface. Specifically, we filter contact hypotheses using geometric reasoning and a Convolutional Neural Network (CNN), trained on simulated object-agnostic images, to promote those that better comply with the actual tactile images from the sensors. We use the selected sensors configurations to optimize over the space of 6D poses using a Gradient Descent-based approach. We finally rank the obtained poses by penalizing those that are in collision with the sensors. We carry out experiments in simulation using the DIGIT vision-based sensor with several objects, from the standard YCB model set. The results demonstrate that our approach estimates object poses that are compatible with actual object-sensor contacts in 87.5% of cases while reaching an average positional error in the order of 2 centimeters. Our analysis also includes qualitative results of experiments with a real DIGIT sensor.

关键词： Gradient methods

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Extrinsic calibration for highly accurate trajectories reconstruction

Extrinsic calibration for highly accurate trajectories recon...

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IEEE international conference on robotics and automation (ICRA)

作者： Vaidis, Maxime Dubois, William Guenette, Alexandre Laconte, Johann Kubelka, Vladimir Pomerleau, Francois Univ Laval Northern Robot Lab Quebec City PQ Canada Orebro Univ Mobile Robot & Olfact Lab AASS Res Ctr Orebro Sweden

ISBN: (纸本)9798350323658

In the context of robotics, accurate ground-truth positioning is the cornerstone for the development of mapping and localization algorithms. In outdoor environments and over long distances, total stations provide accurate and precise measurements, that are unaffected by the usual factors that deteriorate the accuracy of Global Navigation Satellite System (GNSS). While a single robotic total station can track the position of a target in three Degrees Of Freedom (DOF), three robotic total stations and three targets are necessary to yield the full six DOF pose reference. Since it is crucial to express the position of targets in a common coordinate frame, we present a novel extrinsic calibration method of multiple robotic total stations with field deployment in mind. The proposed method does not require the manual collection of ground control points during the system setup, nor does it require tedious synchronous measurement on each robotic total station. Based on extensive experimental work, we compare our approach to the classical extrinsic calibration methods used in geomatics for surveying and demonstrate that our approach brings substantial time savings during the deployment. Tested on more than 30km of trajectories, our new method increases the precision of the extrinsic calibration by 25% compared to the best state-of-the-art method, which is the one taking manually static ground control points.

关键词： robotics

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Spectral and Morphological Classification of Celestial Objects using Physics Informed Machine Learning 3

Spectral and Morphological Classification of Celestial Objec...

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

作者： Siddiquee, Md Fairuz Hasan, Md Mehedi University of Dhaka Department of Robotics and Mechatronics Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The classification of celestial objects has traditionally relied on spectral analysis, which involves identifying unique patterns in the light emitted or absorbed by the objects. In this study, we present a novel approach to categorizing celestial objects based on their spectral properties and morphological structures, leveraging physics-informed machine learning (PIML) techniques to enhance the classification accuracy and efficiency. In the spectral analysis, using Sloan Digital Sky Survey (SDSS) dataset, we classified stars, galaxies, and quasars, focusing on redshift and near-infrared values with i and z filters. Our analysis employed three machine learning models (K-Neighbors, SVM, Random Forest), and the performances were verified to be resilient applying hyperparameter tuning and cross-validation. The best-performing model, Random Forest Classifier, achieved 98% accuracy. In the morphological analysis, the YOLO v5 model was utilized on a customized dataset of astronomical images to further classify the Galaxy Class into five distinct categories and the YOLOv5 model detected those with an average precision of 92.6% and recall of 68.9%. In addition, the interpretability of the model is enhanced by the incorporation of physical knowledge, allowing astronomers to gain deeper insights into the underlying processes. Integrating physical knowledge into these ML models enhances interpretability, facilitating automated real-time analysis of big astronomical data and advancing our understanding of the cosmos. © 2024 IEEE.

关键词： Random forests

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