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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ramkumar, S. Venusamy, Kanagaraj Eswaran, A. Jeevitha, N. Ramyapriya, G. Kannadhasan, S. Rajalakshmi Engineering College Department of Robotics and Automation Thandalam Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering thandalam Chennai India Roever Engineering College Department of Electrical and Electronics Engineering Perambalur India Study World College of Engineering Department of Electronics and Communication Engineering Coimbatore Chennai India

ISBN: (纸本)9798350335095

The automation scenario in the current industrial as well as domestic applications has seen an exponential growth over the decade. Robot plays an important role in industrial automation but in some cases, it needs some extent of human intervention in quality inspection. This paper focuses on solving problems related to defect identification and segregation of fruits using 3-DOF articulated robot configuration with the integration of machine vision system and deep learning algorithms. To perceive the features of object, A USB camera is utilized. The machine vision algorithm will next use a variety of methods for processing digital images to extract the relevant data and decide whether to continue processing the product, redirect it to a different stage of production, or just throw it away. In recent days, the advancement in deep learning leads to the phenomenal growth in computer vision. There are many state-of-the-art object detection algorithms available in deep learning technology and we are using Inception-V3 algorithm that can be used for the machine vision operation in our solution. Based on the results from the algorithm, segregation operation is carried out successfully. © 2023 IEEE.

关键词： Fruits

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The Role of Higher-Order Cognitive Models in Active Learning

arXiv

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arXiv 2024年

作者： Keurulainen, Oskar Alcan, Gokhan Kyrki, Ville The Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Finland

Building machines capable of efficiently collaborating with humans has been a longstanding goal in artificial intelligence. Especially in the presence of uncertainties, optimal cooperation often requires that humans and artificial agents model each other’s behavior and use these models to infer underlying goals, beliefs or intentions, potentially involving multiple levels of recursion. Empirical evidence for such higher-order cognition in human behavior is also provided by previous works in cognitive science, linguistics, and robotics. We advocate for a new paradigm for active learning for human feedback that utilises humans as active data sources while accounting for their higher levels of agency. In particular, we discuss how increasing level of agency results in qualitatively different forms of rational communication between an active learning system and a teacher. Additionally, we provide a practical example of active learning using a higher-order cognitive model. This is accompanied by a computational study that underscores the unique behaviors that this model produces. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning systems

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Progress in Force-Sensing Techniques for Surgical Robots

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Journal of Shanghai Jiaotong university(Science) 2023年第3期28卷 370-381页

作者：高红岩艾孝杰孙正隆陈卫东高安柱 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghai 200240China School of Science and Engineering The Chinese University of Hong KongShenzhen 518172GuangdongChina

Force sensing is vital for situational awareness and safe interaction during minimally invasive ***,surgical robots with integrated force-sensing techniques ensure precise and safe *** the past few decades,there has been considerable progress in force-sensing techniques for surgical *** review summarizes the existing electrically-and optically-based force sensors for surgical robots,including piezoresistive,piezoelectric,capacitive,intensity/phase-modulated,andﬁber Bragg *** principles,applications,advantages,and limitations are also ***,we summarize our conclusions regarding state-of-the-art force-sensing technologies for surgical robotics.

关键词： force sensing surgical robots minimally invasive surgery

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Evaluation on the microhardness of Ni-TiO2 nanocomposite coatings on AISI 1022 substrate

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Materials Today: Proceedings 2022年 62卷 811-817页

作者： Natarajan, P. Mohanraj, M. Sathish Kumar, R. Thirumoorthy, A. Prabhakaran, S. Department of Mechanical Engineering Government College of Engineering Salem 636011 India Department of Automobile Engineering Hindustan Institute of Technology and Science Chennai 603103 India Department of Mechanical Engineering Velalar College of Engineering and Technology Thindal Erode 638012 India Department of Robotics and Automation Engineering PSG College of Technology Coimbatore 641004 India

The surface strength and performance of the material depends mainly on the appropriate coating techniques and also on the selection of the appropriate process parameters. From in this, electroplating is broadly used to protecting the material's surface and hence increases the life of industrial components. The present study pulse electrodeposition of Ni-TiO2 nanocomposite coating was developed on the surface of AISI 1022 Carbon Steel from watts bath. Such nanocoatings greatly enhanced the better hardness, wear and corrosion resistance in the automotive industry. The present work purpose to examine the influence of the coating process parameters on the microhardness (MH) of Ni-TiO2 nanocomposite coating on AISI 1022 Carbon Steel with the aid of response surface methodology (RSM). The Pulse frequency (Hz), Current density (A/cm2) and Duty cycle (%) parameters are considered and integrated into the RSM three factors and three level orthogonal arrays to determine the optimized condition for improved hardness of the coating. The contribution of the coating parameters was studied with the help of analysis of variance (ANOVA). The result revealed frequency: 20 Hz, current density: 0.2 A/cm2 and duty cycle: 30% coated specimen uniformly distributed cauliflower structure than compared to the frequency: 30 Hz, current density: 0.2 A/cm2 and duty cycle: 20%. To explore that uniformly coated specimen EDX, field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD) respectively. © 2022

关键词： Titanium dioxide

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Time Transfer and Clock Synchronization Analysis over Spine-Leaf Networks

Time Transfer and Clock Synchronization Analysis over Spine-...

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IEEE International Symposium on Precision Clock Synchronization for Measurement, Control and Communication, ISPCS

作者： Alberto Ortega Víctor Vázquez Carlos Megías Jesús González Manuel Rodríguez-Álvarez Javier Díaz Eduardo Ros Department of Computer Engineering Automation and Robotics CITIC University of Granada Granada Spain

ISBN: (数字)9798350366112

ISBN: (纸本)9798350366129

The integration of a time synchronization and time transfer system in data center networks is an increasingly common demand. Since moving the entire infrastructure of a data center network to switches with time-dedicated hardware is nowadays a considerable expense of resources, it is convenient to analyze and study it. In this paper we work on real hardware, presenting a characterization of the problem for a Spine-Leaf network by means of load variation tests proposed by the ITU, using the PTP protocol and common non-PTP aware switches. Through this study we manage to show, thanks to real measurements, the problem of dynamic asymmetry and how it affects the time distribution. Additionally, it is shown that if the network load is quadrupled, the PTP pk-pk offset through the network does not even double. Finally, thanks to long-term tests, we can conclude that time synchronization in a Spine-Leaf network shows considerable resilience to load changes.

关键词： Data centers Protocols Load management Time measurement Hardware ITU Synchronization Resilience

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Five questions and answers about artificial intelligence

arXiv

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arXiv 2024年

作者： Prieto, Alberto Prieto, Beatriz Department of Computer Engineering Automation and Robotics CITIC-UGR University of Granada Spain

Rapid advances in Artificial Intelligence (AI) are generating much controversy in society, often without scientific basis. As occurred the development of other emerging technologies, such as the introduction of electricity in the early 20th century, AI causes both fascination and fear. Following the advice of the philosopher R.W. Emerson's advice 'the knowledge is the antidote to fear', this paper seeks to contribute to the dissemination of knowledge about AI. To this end, it reflects on the following questions: the origins of AI, its possible future evolution, its ability to show feelings, the associated threats and dangers, and the concept of AI singularity. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Behavioral-based circular formation control for robot swarms

Behavioral-based circular formation control for robot swarms

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Jesús Bautista Héctor García de Marina Department of Computer Engineering Automation and Robotics University of Granada (UGR-CITIC) Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper focuses on coordinating a robot swarm orbiting a convex path without collisions among the individuals. The individual robots lack braking capabilities and can only adjust their courses while maintaining their constant but different speeds. Instead of controlling the spatial relations between the robots, our formation control algorithm aims to deploy a dense robot swarm that mimics the behavior of tornado schooling fish. To achieve this objective safely, we employ a combination of a scalable overtaking rule, a guiding vector field, and a control barrier function with an adaptive radius to facilitate smooth overtakes. The decision-making process of the robots is distributed, relying only on local information. Practical applications include defensive structures or escorting missions with the added resiliency of a swarm without a centralized command. We provide a rigorous analysis of the proposed strategy and validate its effectiveness through numerical simulations involving a high density of unicycles.

关键词： Robot kinematics MIMICs Numerical simulation Fish Formation control Vectors Orbits

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Color Transfer Algorithm for Interior Design Based on Deep Learning

Color Transfer Algorithm for Interior Design Based on Deep L...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Kayalvizhi Rajadurai Elango Vaidyanathan Madhan Mohankumar Nandini Kumaresan Arun Joseph Santhosh Sivaraj Department of Robotics and Automation Easwari Engineering College Chennai

It is suggested that a topologically informed colour transfer method for interior design be used to match areas that have significant colour variability and colours that are just moderately rich. First, the image of the interior design is segmented, then the topology complete information of each region is calculated, and last, the region-matching relationship is determined based on the topology in order to improve the accuracy of the colour transfer; Second, colour is transferred between matched zones, and colour correction is applied to areas that do not get colour input in order to enhance the color's integrity as it is transferred; In conclusion, the algorithm for colour harmony works to improve colour harmony after the colour transfer by lowering the amount of noise. According to the findings of the experiments, the algorithm is able to keep the image's rich colour throughout while also improving the matching relationship and making colour adjustments locally, which leads to better colour transfer results. Targets can be captured and tracked with TOP technology. The use of neural networks and deep learning allows ambient perception to train and self-play the network, allowing it to continually update and locate the optimal grid path. The method of vertical falling grabbing is utilised in order to obtain and determine the quickest path to return to the grid in order to complete the procedure.

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A New Energy Efficient Routing Scheme with Normalized Condition Parameters for Wireless Sensor Network 7

A New Energy Efficient Routing Scheme with Normalized Condit...

引用

7th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2022

作者： Kamaruzzaman, Md. Chandra, Abhijit The Neotia University Department of Robotics & Automation Kolkata India Jadavpur University Department of Instrumentation and Electronics Engineering Kolkata India

ISBN: (纸本)9781665496483

Wireless sensor network (WSN) has become popular for its potential applications in the field of environmental science, robotics, military, disaster management system, medical science and so on. However, limited energy of the sensor nodes is emerging as one of the key constraints as far as the application of WSNs is concerned. Hence, researchers are developing divergent algorithms for the minimization of energy consumption of the sensor nodes (SNs), In this article, we have introduced a novel routing algorithm in which a fitness function is used. Moreover, all the parameters in the fitness function are normalized so that equal contribution is assured from each of them. MATLAB simulation on the proposed scheme has been carried out in order to exhibit its effectiveness over few existing approaches. It is finally concluded that the proposed scheme outperforms previous algorithms in terms of network lifetime, time for first sensor node die and standard deviation. © 2022 IEEE.

关键词： Sensor nodes

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Trash Detection in Water Bodies Using Yolo With Explainable AI Insight

Trash Detection in Water Bodies Using Yolo With Explainable ...

引用

International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Shafaq Rasheed Amal Mirza Muhammad Saad Saeed Muhammad Haroon Yousaf Department of Computer Engineering University of Engineering and Technology Taxila Pakistan Swarm Robotics Lab National Center of Robotics and Automation Taxila Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Waste disposal into water bodies is a serious concern for environmental engineers, often resulting in urban flooding, soil degradation in agricultural areas, and freshwater pollution. Additionally, trash accumulation in water bodies threatens ma-rine life by introducing contaminants through storm drains, rivers, and other channels. Furthermore, the unique character-istics of underwater environments, including high noise levels, low visibility, and color variation, pose challenges for trash detection. Thus, addressing this issue is crucial for environmental sustainability. The work focuses on both floating and submerged trash in water bodies. Typically, the detection of trash on water bodies is impacted by environmental variability, diverse appearance, and background clutter. These conditions also make usual object detection techniques less effective. Deep learning methods have shown promise in handling challenging visual tasks, therefore researchers have turned to them to get over these challenges. In this work, we focus on applying different versions of YOLO to the problem of water trash detection. To assess these models' performance, we use four different datasets: Trash Flow rate Dataset, DeepTrash, FloW, and TrashICRA. Each dataset represents various trash types under different conditions such as underwater trash and floating trash. Our study attempts to identify the best method suitable for the detection of aquatic waste. Our results highlight the effectiveness of YOLO variants in identifying the presence of trash in water ecosystems. By leveraging Grad-CAM we gain insights into the interpretability and transparency of deep learning models thus enabling Explainable AI. This approach will also contribute to the understanding and trustworthiness of artificial systems deployed for trash detection applications by providing a transparent view of decision making process of deep learning models.

关键词： Deep learning YOLO Visualization Explainable AI Storms Biological system modeling Soil Water pollution Waste disposal robotics and automation

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