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检索条件"任意字段=International Symposium on Computational Intelligence in Robotics and Automation"

共 3099 条记录，以下是151-160 订阅

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A Model Integrating Wavelet Convolution and Linear Attention Mechanism for Inventory Management in Power Warehouses 4

A Model Integrating Wavelet Convolution and Linear Attention...

引用

4th IEEE international Conference on Information Technology, Big Data and Artificial intelligence, ICIBA 2024

作者： Li, Tao Zhang, Zhengli Li, Ligang Du, Guozheng State Grid Shandong Electric Power Company Material Company Jinan China Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China

ISBN: (纸本)9798350363586

Accurate inventory management in power warehouses is crucial for maintaining consistency between records and actual stock levels. However, the complex storage layout and the diversity of stored materials often lead to human errors during manual inventory checks, resulting in inefficiencies. To address these challenges, we collected 2,000 images from power warehouse environments and created a binary classification dataset containing images of correctly and incorrectly placed items. We propose a novel network, CWAMNet, specifically designed for visual inventory management in complex power warehouse environments. CWAMNet integrates wavelet convolution and a linear attention mechanism to overcome the limitations of traditional convolutional neural networks (CNNs) and standard attention mechanisms, achieving a balance between model capacity and computational efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Modular Cyber-Physical System for Smart Industry: A case study on energy load disaggregation

Modular Cyber-Physical System for Smart Industry: A case stu...

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2023 international Conference on Control, automation and Diagnosis, ICCAD 2023

作者： Alves, Jorge Sousa, Pedro Matos, Barbara Mendes, Jerome Souza, Francisco Matias, Tiago University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal Oncontrol Technologies Coimbra3000-108 Portugal Radboud University Dept. Analytical Chemistry & Chemometrics AJ Nijmegen6525 Netherlands

ISBN: (纸本)9798350347074

Constant changes have marked the industry, and cyber-physical systems have offered great potential in Industry 4.0 on production processes, allowing the integration of computational intelligence and real-time processing. In this context, this paper proposes a modular hardware system for industrial applications according to a plug & play concept. The proposed system comprises a processing module (mPP) and specific expansion modules (mEXs). The mPP is responsible for administrating the network/communication. It collects data from the mEXs, extracts features, processes the features through artificial intelligence, and interacts with the production process. The mEXs comprise sensors, conditioning circuits, and an analog/digital converter. They are responsible for acquiring field signals and their transmission to the mPP. The proposed system is designed for operating with electrical protection, allowing reliable operation of the mPP and mEXs. For evaluating and demonstrating the proposed system in a real scenario, we applied it in a case study for non-intrusive energy load disaggregation. © 2023 IEEE.

关键词： Cyber Physical System

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A History Information-Based A* Algorithm for the Path Planning with Multiple Alternative Destinations

A History Information-Based A* Algorithm for the Path Planni...

引用

IEEE international symposium on Industrial Electronics (ISIE)

作者： Ke Wang Wei Liang Huaguang Shi Jialin Zhang Qi Wang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

As a cornerstone technology in automation, path planning plays a crucial role across various domains, including logistics, autonomous vehicles, and robotics. A novel path planning challenge, focusing on routes with multiple alternative destinations, has emerged as a focal point in industrial settings. Traditional approaches such as breadth-first and heuristic searches lack specific optimizations for this challenge, resulting in suboptimal efficiency. This paper introduces a new approach to the path planning problem with multiple alternative destinations that utilizes historical data. It features three heuristic strategies: leveraging historical information for search acceleration, pruning based on the length of the current shortest path, and searching in ascending order of the Manhattan distance. These strategies enhance the search efficiency by leveraging historical information for faster searches and pruning based on real-time search outcomes. Simulation results show that the proposed algorithm outperforms the existing works in terms of the computational cost.

关键词： Service robots Navigation Simulation Focusing Path planning Real-time systems computational efficiency

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automation Using Robots, Machine Learning, and Artificial intelligence to Enhance Production and Quality

Automation Using Robots, Machine Learning, and Artificial In...

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international Conference on computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： Guna Sekhar Sajja Santosh Reddy Addula Department of Information Technology University of the Cumberlands Williamsburg KY USA

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Automating everyday jobs, expanding production, and improving quality are all examples of how the integration of robotic automation and machine intelligence (AI) is causing activities to change. The purpose of this study is to examine the relationship between artificial intelligence (AI) and robotics, specifically focusing on how machine intelligence (ML) algorithms and AI approaches enable androids to do complicated jobs with greater efficiency and precision. Through the automation of everyday and repetitive tasks, organizations can redirect their workers into activities that are more important and innovative. Automated systems that are driven by artificial intelligence can learn and adapt from data, therefore rebuilding their performance over time and ensuring logical quality. This strategy not only increases productivity but also decreases the number of mistakes and the expenses associated with it. It is important to note that the associations of these gadgets stretch across a variety of fields, including manufacturing, healthcare, management, and departments that interact with consumers. This highlights the potential for wider financial and friendly advantages. A discussion of the difficulties and opportunities associated with the integration of mechanics is presented in the last section of the study. Particular attention is paid to the relevance of ongoing innovation and ethical considerations in the configuration of artificial intelligence and robotics.

关键词： Industries Resistance Productivity Technological innovation automation Service robots Manufacturing Artificial intelligence Protection Machine intelligence

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MonoVAN: Visual Attention for Self-Supervised Monocular Depth Estimation 22

MonoVAN: Visual Attention for Self-Supervised Monocular Dept...

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22nd IEEE international symposium on Mixed and Augmented Reality (ISMAR)

作者： Indyk, Ilia Makarov, Ilya HSE Univ Moscow Russia Artificial Intelligence Res Inst AIRI AI Ctr NUST MISiS Moscow Russia

ISBN: (纸本)9798350328387

Depth estimation is crucial in various computer vision applications, including autonomous driving, robotics, and virtual and augmented reality. An accurate scene depth map is beneficial for localization, spatial registration, and tracking. It converts 2D images into precise 3D coordinates for accurate positioning, seamlessly aligns virtual and real objects in applications like AR, and enhances object tracking by distinguishing distances. The self-supervised monocular approach is particularly promising as it eliminates the need for complex and expensive data acquisition setups relying solely on a standard RGB camera. Recently, transformer-based architectures have become popular to solve this problem, but at high quality, they suffer from high computational cost and poor perception of small details as they focus more on global information. In this paper, we propose a novel fully convolutional network for monocular depth estimation, called MonoVAN, which incorporates the visual attention mechanism and applies super-resolution techniques in decoder to better capture fine-grained details in depth maps. To the best of our knowledge, this work pioneers the use of a convolutional visual attention in the context of depth estimation. Our experiments on outdoor KITTI benchmark and the indoor NYUv2 dataset show that our approach outperforms the most advanced self-supervised methods, including such state-of-the-art models as transformer-based VTDepth from ISMAR'22 and hybrid convolutional-transformer MonoFormer from AAAI'23, while having a comparable or even fewer number of parameters in our model than competitors. We also validate the impact of each proposed improvement in isolation, providing evidence of its significant contribution. Code and weights are available at https://***/IlyaInd/MonoVAN.

关键词： Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality Artificial intelligence-Computer vision-Localization, spatial registration and tracking-3D reconstruction

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robotics and AI in Enhancing Banking Operations Efficiency

Robotics and AI in Enhancing Banking Operations Efficiency

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international Conference on computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： Arangarajan M Kunal D Gaikwad Meher Dharmani Riya Nathani Rahul Joshi Mohammed Faez Hasan Department of Mechanical Engineering Shri Angalamman College of Engineering and Technology Tiruchirappalli Tamilnadu Department of Electronics MGSMS Arts Science and Commerce College Jalgaon UKS Institute of Management Studies and Research Mumbai Kishinchand Chellaram College Mumbai Department of Journalism & Mass Communication SMeH Manav Rachna International Institute of Research and Studies Haryana India Finance Finance and Banking Department Kerbala University Iraq

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The financial sector is experiencing a transformation as a result of the incorporation of AI and robotics into banking operations, which has improved accuracy, efficiency, and client satisfaction. The utilization of robotics, particularly robotic process automation (RPA), simplifies tasks such as data input, transaction processing, and compliance reporting. These operations are enhanced by artificial intelligence (AI) tools, such as machine learning and natural language processing, which enable well-informed decision-making, predictive modeling, and improved data analytics. This investigation investigates the numerous methods by which artificial intelligence (AI) and robotics are influencing the banking sector, particularly in terms of operational efficiency. It also offers a glimpse into the future prospects of this rapidly changing field.

关键词： Intelligent automation Data analysis Decision making Banking Machine learning Predictive models Natural language processing Data models Artificial intelligence Robots

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Benchmark on deep reinforcement learning-based placing using a robot arm 56

Benchmark on deep reinforcement learning-based placing using...

引用

56th international symposium on robotics, ISR Europe 2023

作者： Kernbach, Andreas Hoffmann, Kathrin Sawodny, Oliver Eivazi, Shahram Institute of Industrial Manufacturing and Management IFF University of Stuttgart Department Cyber Cognitive Intelligence CCI Fraunhofer Institute for Manufacturing Engineering and Automation IPA Germany University of Stuttgart Institute for System Dynamics Germany Festo SE & Co. KG University of Tübingen Germany

ISBN: (纸本)9783800761418

Deep reinforcement learning (DRL) is an approach by which an agent can learn to solve complex sequential tasks in a try-and-error manner. As such, it has been widely explored in robotic domain for solving manipulation tasks. In this paper we benchmark DRL approaches utilizing pure image-based observations and simulation-based numerical ground truth (oracle) observations. The underlying benchmark task is inspired by an industrial application where a robotic arm is required to position up to nine boxes of random sizes into a container with variable pose. Central for the comparison is the robustness of the task execution and the duration of DRL training. We first simulate the task based on a real world logistic setup and parameters for a vision based reinforcement learning agent that operates in real time. Second, we compare DRL algorithms and observation types with policies that decide the desired end-effector pose of the to-be-placed box. Our results show the Proximal Policy Optimization (PPO) algorithm has the highest performance with satisfactory robustness and learning time. Moreover, we successfully perform the placing task by relying only upon image-based input in which the robot arm robustly places 8.8+0−0.261 of nine objects on average into the container, increasing the required learning time by 233 % compared to oracle observations. © VDE VERLAG GMBH Berlin Offenbach.

关键词： AI in robotics Image-based Reinforcement learning

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A Neural Network-Based Approach for the Identification and Compensation of Magnetic Disturbances in Mobile Robot Localization 17

A Neural Network-Based Approach for the Identification and C...

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17th IEEE international symposium on Applied computational intelligence and Informatics, SACI 2023

作者： Stefanoni, Massimo Odry, Akos Sarcevic, Peter Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary University of Szeged Department of Mechatronics and Automation Szeged Hungary

ISBN: (纸本)9798350321104

Magnetometers are widely used in sensor fusion frameworks of mobile robots due to their ability to work as compasses. The measurements of these sensors are largely influenced by hard iron and soft iron effects that are caused by nearby ferromagnetic objects. In this paper, an artificial neural network (ANN)-based method is proposed for the identification of such effects, which can be compensated from the measurements. The method utilizes both the distance and the angle of the detected object based on the coordinate frame of the mobile robot as the inputs of the Multi-Layer Perceptron (MLP) ANN. The MLP provides on its outputs the disturbance levels in the three axes, which need to be subtracted from the measurements to obtain the compensated values. The method was tested with three different types of objects to evaluate the performance of the method. Measurements were collected using an industrial robotic arm in a grid around the objects. The disturbance levels generated by the objects were computed by subtracting measurements recorded in an undisturbed scenario. Three-layer MLPs were tested with various numbers of hidden layer neurons to find the optimal configurations. The achieved results show that the disturbance levels can be significantly decreased using the proposed method. © 2023 IEEE.

关键词： Mobile robots

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Particle swarm optimization aided calibration of sensor installation errors for MEMS accelerometers 17

Particle swarm optimization aided calibration of sensor inst...

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17th IEEE international symposium on Applied computational intelligence and Informatics, SACI 2023

作者： Pesti, Richard Sarcevic, Peter Csik, Dominik Odry, Akos Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary University of Szeged Department of Mechatronics and Automation Szeged Hungary

ISBN: (纸本)9798350321104

Accelerometers, gyroscopes, and magnetometers, constituting the Inertial Measurement Unit (IMU), are among the most fundamental sensors utilized in localization problems. However, their utilization is a challenging task since the reliability of measurements depends on the precision of the executed calibration method. This paper proposes an industrial robot-based infield calibration process in a numerical optimization aided framework, which does not need turntable nor dedicated expensive laboratory setup. The proposed method utilizes a calibrated industrial robotic arm, which is programmed to execute wide variety of motions with comprehensive dynamical ranges. The end effector of the robotic arm is equipped with IMUs with predefined installation errors. The proposed calibration process records the ground truth accelerations and raw IMU data in a database in Robot Operating System (ROS) framework. Then, realistic sensor models are established, and the Particle Swarm optimization (PSO) algorithm is executed with a custom fitness function to obtain the between sensor misalignment and bias errors. The comprehensive analysis highlights that the proposed method effectively determines the calibration parameters. Mean squared error (MSE) performance metrics shows that the precision of these parameters mainly depends on the complexity of the executed dynamical motions. © 2023 IEEE.

关键词： Calibration

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Optimization of Image Preprocessing and Background Influences using a Depth Camera for Person Re-Identification on a Mobile Robot 19

Optimization of Image Preprocessing and Background Influence...

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19th IEEE international Conference on automation Science and Engineering, CASE 2023

作者： Flores, Sebastian Boztoprak, Zeynep Jost, Jana Fraunhofer Institute for Material Flow and Logistics The Department of Ai and Autonomous Systems Dortmund44227 Germany Fraunhofer Institute for Material Flow and Logistics The Department of Robotics and Cognitive Systems Dortmund44227 Germany

ISBN: (纸本)9798350320695

In this paper, we optimize datasets with different image preprocessing techniques for person re-identification using the ResNet18 model on a mobile robot with limited hardware, e.g., computational power and depth camera. For this, we create 16 datasets for which we discovered that the inverted original images, from the IR gray value images of the depth camera, results in the highest values with an r1, r5 and mAP of 98.48 %,99.82 % and 80.86 %. Additionally, we explore the cross-dataset evaluation for the 16 datasets to examine the robustness of our model, which points to a low generalizability. The scores are associated with the similarity between the trained and evaluated dataset. © 2023 IEEE.

关键词： Mobile robots

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