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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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Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps

Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Fi...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lundell, Jens Corona, Enric Le, Tran Nguyen Verdoja, Francesco Weinzaepfel, Philippe Rogez, Gregory Moreno-Noguer, Francesc Kyrki, Ville Aalto Univ Sch Elect Engn Helsinki Finland CSIC UPC Inst Robot & Informat Ind Barcelona Spain NAVER LABS Europe Meylan France

ISBN: (纸本)9781728190778

While there exists many methods for manipulating rigid objects with parallel-jaw grippers, grasping with multi-finger robotic hands remains a quite unexplored research topic. Reasoning and planning collision-free trajectories on the additional degrees of freedom of several fingers represents an important challenge that, so far, involves computationally costly and slow processes. In this work, we present Multi-FinGAN, a fast generative multi-finger grasp sampling method that synthesizes high quality grasps directly from RGB-D images in about a second. We achieve this by training in an end-to-end fashion a coarse-to-fine model composed of a classification network that distinguishes grasp types according to a specific taxonomy and a refinement network that produces refined grasp poses and joint angles. We experimentally validate and benchmark our method against a standard grasp-sampling method on 790 grasps in simulation and 20 grasps on a real Franka Emika Panda. All experimental results using our method show consistent improvements both in terms of grasp quality metrics and grasp success rate. Remarkably, our approach is up to 20-30 times faster than the baseline, a significant improvement that opens the door to feedback-based grasp replanning and task informative grasping. Code is available at https: /***/multi-fingan/.

关键词： Training Measurement Taxonomy Grasping Sampling methods Trajectory Planning

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DDCNN: A Promising Tool for Simulation-to-reality UAV Fault Diagnosis

DDCNN: A Promising Tool for Simulation-to-reality UAV Fault ...

引用

IEEE conference on Cybernetics and Intelligent Systems

作者： Wei Zhang Shanze Wang Junjie Tong Fang Liao Yunfeng Zhang Xiaoyu Shen College of Information Science and Technology Eastern Institute of Technology Ningbo China Department of Mechanical Engineering National University of Singapore Singapore Singapore Temasek Laboratories National University of Singapore Singapore Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Identifying the fault in propellers is important to keep quadrotors operating safely and efficiently. The simulation-to-reality (sim-to-real) UAV fault diagnosis methods provide a cost-effective and safe approach to detecting propeller faults. However, due to the gap between simulation and reality, classifiers trained with simulated data usually underperform in real flights. In this work, a novel difference-based deep convolutional neural network (DDCNN) model is presented to address the above issue. It uses the difference features extracted by deep convolutional neural networks to reduce the sim-to-real gap. Moreover, a new domain adaptation (DA) method is presented to further bring the distribution of the real-flight data closer to that of the simulation data. The experimental results demonstrate that the DDCNN+DA model can increase the accuracy from 52.9% to 99.1% in real-world UAV fault detection.

关键词： Fault diagnosis Adaptation models Accuracy Propellers Random access memory Autonomous aerial vehicles Feature extraction

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Multimodal Indoor Localization Using Crowdsourced Radio Maps

Multimodal Indoor Localization Using Crowdsourced Radio Maps

引用

IEEE international conference on robotics and automation (ICRA)

作者： Zhaoguang Yi Xiangyu Wen Qiyue Xia Peize Li Francisco Zampella Firas Alsehly Chris Xiaoxuan Lu School of Informatics University of Edinburgh United Kingdom Huawei Technologies (UK) Department of Computer Science University College London

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Indoor Positioning Systems (IPS) traditionally rely on odometry and building infrastructures like WiFi, often supplemented by building floor plans for increased accuracy. However, the limitation of floor plans in terms of availability and timeliness of updates challenges their wide applicability. In contrast, the proliferation of smartphones and WiFi-enabled robots has made crowdsourced radio maps – databases pairing locations with their corresponding Received Signal Strengths (RSS) – increasingly accessible. These radio maps not only provide WiFi fingerprint-location pairs but encode movement regularities akin to the constraints imposed by floor plans. This work investigates the possibility of leveraging these radio maps as a substitute for floor plans in multimodal IPS. We introduce a new framework to address the challenges of radio map inaccuracies and sparse coverage. Our proposed system integrates an uncertainty-aware neural network model for WiFi localization and a bespoken Bayesian fusion technique for optimal fusion. Extensive evaluations on multiple real-world sites indicate a significant performance enhancement, with results showing ∼ 25% improvement over the best baseline.

关键词： Location awareness Uncertainty Neural networks Buildings Bayes methods IP networks Floors

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A perspective of Intelligent algorithms and manipulator control 4

A perspective of Intelligent algorithms and manipulator cont...

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4th international conference on robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Kuang, Yaozheng College of Automation and Information Engineering Xi'An University of Technology Xi'an China

ISBN: (纸本)9781450398343

Intelligent manipulator is an important branch of intelligent robots, it plays a vital role in today's industrial production, and is also one of the current research hotspots, it uses many kinds of algorithms to realize automatic control, and meet the needs of industrial production. Nowadays, the field of artificial intelligence and deep learning has developed rapidly, and it can be combined with traditional control strategies in the field of manipulators to achieve higher sensitivity, stability, robustness, and efficiency. In this paper, according to the research status of intelligent algorithm control manipulators, several cases of using different control methods to strengthen manipulator control are analyzed. This paper summarizes the advantages and disadvantages of each intelligent algorithm, sorts out the current problems and future development direction, and puts forward a deep learning algorithm and communication technology that can be used on some occasions to further strengthen the control effect of the manipulators. © 2022 ACM.

关键词： Manipulators

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Constrained Hierarchical Monte Carlo Belief-State Planning

Constrained Hierarchical Monte Carlo Belief-State Planning

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IEEE international conference on robotics and automation (ICRA)

作者： Arec Jamgochian Hugo Buurmeijer Kyle H. Wray Anthony Corso Mykel J. Kochenderfer Stanford University Stanford CA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints, generalizing safe planning under state and transition uncertainty. Unfortunately, online CPOMDP planning is extremely difficult in large or continuous problem domains. In many large robotic domains, hierarchical decomposition can simplify planning by using tools for low-level control given high-level action primitives (options). We introduce Constrained Options Belief Tree Search (COBeTS) to leverage this hierarchy and scale online search-based CPOMDP planning to large robotic problems. We show that if primitive option controllers are defined to satisfy assigned constraint budgets, then COBeTS will satisfy constraints anytime. Otherwise, COBeTS will guide the search towards a safe sequence of option primitives, and hierarchical monitoring can be used to achieve runtime safety. We demonstrate COBeTS in several safety-critical, constrained partially observable robotic domains, showing that it can plan successfully in continuous CPOMDPs while non-hierarchical baselines cannot.

关键词： Uncertainty Monte Carlo methods Costs Runtime Natural languages Search problems Planning

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Fast and Robust Normal Estimation for Sparse LiDAR Scans

Fast and Robust Normal Estimation for Sparse LiDAR Scans

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IEEE international conference on robotics and automation (ICRA)

作者： Igor Bogoslavskyi Konstantinos Zampogiannis Raymond Phan Magic Leap

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Light Detection and Ranging (LiDAR) technology has proven to be an important part of many robotics systems. Surface normals estimated from LiDAR data are commonly used for a variety of tasks in such systems. As most of the today’s mechanical LiDAR sensors produce sparse data, estimating normals from a single scan in a robust manner poses *** this paper, we address the problem of estimating normals for sparse LiDAR data avoiding the typical issues of smoothing out the normals in high curvature *** LiDARs rotate a set of rigidly mounted lasers. One firing of such a set of lasers produces an array of points where each point’s neighbor is known due to the known firing pattern of the scanner. We use this knowledge to connect these points to their neighbors and label them using the angles of the lines connecting them. When estimating normals at these points, we only consider points with the same label as neighbors. This allows us to avoid estimating normals in high curvature *** evaluate our approach on various data, both self-recorded and publicly available, acquired using various sparse LiDAR sensors. We show that using our method for normal estimation leads to normals that are more robust in areas with high curvature which leads to maps of higher quality. We also show that our method only incurs a constant factor runtime overhead with respect to a lightweight baseline normal estimation procedure and is therefore suited for operation in computationally demanding environments.

关键词： Mechanical sensors Laser radar Runtime Smoothing methods Firing Lasers Estimation

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Improving Object Distance Measurement Based on Mono Camera Using Magnification and Yolo methods

Improving Object Distance Measurement Based on Mono Camera U...

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Control & automation, Electronics, robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE international conference on

作者： Herdianto Herdianto Poltak Sihombing Fahmi Fahmi Tulus Tulus Computer Science Universitas Sumatera Utara Medan Indonesia Electrical Engineering Universitas Sumatera Utara Medan Indonesia Mathematics Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

In the Autonomous Driver System (DAS), object detection and distance measurement are among the parameters that are consistently monitored for changes. This is done to modify the action that will be determined, whether to turn left to right or halt in order to prevent a collision. The issue with utilizing a mono camera to measure the distance of objects is that the utmost distance for objects that are measured by humans is only 7 meters. For this reason, the objective of this investigation is to determine the distances to numerous objects using a mono camera, with a measured distance exceeding 15 meters. Magnification and You Only Look Once (YOLO) are employed. The results of this investigation are quantifiable objects that have been tested, such as humans, bottles, and jerry cans. For humans, the measured distance to objects is 31.40 meters, with a body height of 1.75 meters. For bottles, the measured object distance is up to 4.80 meters with an object height of 0.26 meters, while for jerry cans, the measured object is 4.20 meters with an object height of 0.17 meters.

关键词： Meters YOLO Robot vision systems Estimation Cameras Distance measurement MONOS devices Internet of Things Robots Monitoring

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A Traffic Model Integrating Long Short-Term Memory Networks with Multi-class Macroscopic Equations

A Traffic Model Integrating Long Short-Term Memory Networks ...

引用

IEEE international conference on automation Science and Engineering (CASE)

作者： K. Binjaku C. Pasquale S. Sacone E. K. Meçe Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Computer Engineering Polytechnic University of Tirana Albania

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Accurate traffic state estimation is crucial for effective transportation management and urban planning. In recent years, deep learning techniques, particularly Long Short-Term Memory (LSTMs) networks, have shown promise in capturing the complex temporal dynamics of traffic systems. This paper proposes a novel approach by integrating LSTM networks with the multi-class METANET model, considering on/off ramps, to enhance traffic state prediction accuracy. We develop both univariate and multivariate versions of the physics-informed multi-class LSTM model, enabling comprehensive exploration of traffic variables such as speed and flow for passenger and heavy vehicles. Experimental results demonstrate the superior predictive capabilities of our approach compared to traditional LSTM models, particularly in capturing the intricate relationship between speed and flow variables. Our findings underscore the significant benefits of incorporating physics-based principles into deep learning architectures for traffic prediction, facilitating more robust and reliable traffic state estimations for real-world applications.

关键词： Accuracy Urban planning Transportation Predictive models Reliability engineering Mathematical models Robustness State estimation Vehicle dynamics Long short term memory

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Describing Robots from Design to Learning: Towards an Interactive Lifecycle Representation of Robots

Describing Robots from Design to Learning: Towards an Intera...

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international conference on Advanced robotics and Mechatronics (ICARM)

作者： Nuofan Qiu Chaoyang Song Fang Wan School of Design Southern University of Science and Technology Shenzhen China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

The robot development process is divided into several stages, which create barriers to the exchange of information between these different stages. We advocate for an interactive lifecycle representation, extending from robot morphology design to learning, and introduce the role of robot description formats in facilitating information transfer throughout this pipeline. We analyzed the relationship between design and simulation, enabling us to employ robot process automation methods for transferring information from the design phase to the learning phase in simulation. As part of this effort, we have developed an open-source plugin called ACDC4Robot for Fusion 360, which automates this process and transforms CAD software into a user-friendly graphical interface for creating and editing robot description formats to solve the Design2Sim problem. Additionally, we offer an out-of-the-box robot model library to streamline and reduce repetitive tasks. All codes are hosted open-source. (https://***/bionicdl-sustech/ACDC4Robot)

关键词： Solid modeling Mechatronics Codes automation Pipelines Morphology Transforms Software Libraries Robots

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Reviewing Fault Diagnosis methods in Electric Drives: Power Subsystem and Electrical Machine 24

Reviewing Fault Diagnosis Methods in Electric Drives: Power ...

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24th IEEE international conference of Young Professionals in Electron Devices and Materials, EDM 2023

作者： Demidova, Galina Chen, Hao Li, Yibin Lukichev, Dmitry Anuchin, Alecksey Itmo University Faculty of Control System and Robotics Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China Institute of Marine Science and Technology Shandong University Shandong China Moscow Power Engineering Institute Department of Electric Drives Moscow Russia

ISBN: (纸本)9798350336870

This paper provides a review of popular fault diagnosis methods in the power subsystem and electrical machine of electric drives. The review aims to explore the different approaches used to detect faults in these components and the most common types of faults encountered in electric drives. The paper first provides an overview of the power subsystem and electrical machine and their respective functions within the electric drive system. It then proceeds to discuss the different types of faults that can occur in these components, including overvoltage, undervoltage, overcurrent, and short circuits. The review evaluates the various fault diagnosis methods available for these components, including model-based, signal-based, and artificial intelligence-based approaches. Model-based approaches use mathematical models of the system to detect and diagnose faults. Signal-based approaches rely on the analysis of measured signals to detect changes in system behavior that indicate a fault. Artificial intelligence-based approaches use machine learning algorithms to identify patterns in data and detect faults. Model-based approaches are highly accurate but can be computationally intensive. Signal-based approaches are less computationally intensive but may be less accurate in certain situations. Artificial intelligence-based approaches are versatile but require significant amounts of data for training. This review provides a comprehensive overview of the current state-of-the-art in fault diagnosis methods for the power subsystem and electrical machine of electric drives. The review concludes by suggesting potential areas for future research to further improve the accuracy and efficiency of fault diagnosis methods for electric drives. © 2023 IEEE.

关键词： Failure analysis

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