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Multi-agent control architecture for large scale multi-robot system

Multi-agent control architecture for large scale multi-robot...

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27th IASTED International conference on Modelling, Identification, and Control

作者： Baglietto, Marco Cannata, Giorgio Capezio, Francesco Grosso, Alberto Sgorbissa, Antonio Zaccaria, Renato Department of Communication Computer and System Sciences Via all'Opera Pia 13 16145 Genova Italy

ISBN: (纸本)9780889867116

As research progresses in distributed robotic systems, more and more aspects of multi-robot systems are being explored. this dissertation focuses on multi-robot control systems and provides the model of an agent-based control architecture following the research that is ongoing in the field of distributed mobile robot systems. In this work, the agent technology is proposed as a possible approach to the design of multi-robot systems. Autonomous agents, or sometimes called intelligent agents, can effectively combine all the advantages of distributed systems with advanced robot control support and artificial intelligence techniques. the coordination of the multi-robot system is based on agent interaction and negotiation, and a communication infrastructure based on open web standards is provided. this paper is the result of the work done within the ROBOSWARM European Project.

关键词： Multipurpose robots

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Motion video annotation and analysis: an overview

Motion video annotation and analysis: an overview

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Asilomar conference on Signals, Systems & computers

作者： H.S. Sawhney Machine Vision Group IBM Almaden Research Center San Jose CA USA

Digital storage of images and videos requires staggering amounts of storage. A mere hour long digitized video can occupy a staggering 30GB of storage. With the increasing dependence of a variety of applications on digital storage, the need for automatic and semiautomatic annotation and analysis tools for intelligent video data indexing and manipulation will be strongly felt. Manual annotation of this data will be practically infeasible and insipid. the paper presents highlights of some of the important problems to be solved for automating the process. computer vision techniques with a human-in-the-loop approach can provide a powerful workbench of algorithms and techniques for the task.< >

关键词： Motion analysis Image storage Image motion analysis Storage automation Indexing computer vision

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An U-Net Semantic Segmentation vision System on a Low-Power Embedded Microcontroller Platform 27

An U-Net Semantic Segmentation Vision System on a Low-Power ...

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27th International conference on Knowledge Based and intelligent Information and Engineering Sytems, KES 2023

作者： Falaschetti, Laura Bruschi, Sara Alessandrini, Michele Biagetti, Giorgio Crippa, Paolo Turchetti, Claudio DII - Department of Information Engineering Università Politecnica delle Marche via Brecce Bianche 12 AnconaI-60131 Italy Università Politecnica delle Marche via Brecce Bianche 12 AnconaI-60131 Italy

In recent years, real-time semantic segmentation on embedded devices has become increasingly popular, largely driven by the growing interest in smart vehicles and robots. the rising of autonomous driving has brought about new challenges for these systems, such as the need for low latency and computation-intensive operations, which can lead to excessive energy consumption and computing power. To address these challenges, this paper focuses on the critical task of semantic segmentation, which is essential for accurate environment perception, and proposes an implementation that achieves high accuracy and low complexity using a UNet as base architecture. the goal is to enable real-time semantic segmentation on low-power cores while preserving performance, which is crucial for the success of autonomous vehicles and robots. the lightweight U-Net architectures have been implemented in a STM32 microcontroller, namely STM32L4R5, as a severe benchmark to meet the low-power, low-cost requirements. © 2023 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Embedded Systems Microcontrollers Semantic Segmentation U-Net Visual SLAM

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AI-based vision system for collision detection in HRC applications 9

AI-based vision system for collision detection in HRC applic...

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9th CIRP conference on Assembly Technology and Systems, CATS 2022

作者： Makris, Sotiris Aivaliotis, Panagiotis Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras26504 Greece

Human-Robot Collaboration (HRC) enabling mechanisms require real-time detection of potential collisions among human and robots. Taking under consideration the already existing standards and the literature, most of collision detection techniques require the integration of sensorial systems on the robot aiming to identify the contact events. this paper deals with a novel approach for the identification of human and robot collision based on vision systems. Moreover, Artificial intelligent (AI) algorithms are required to classify the captured data near real-time and to provide a score about the collision status (contact or non-contact) between a human and the robot. Accordingly, the AI models should be trained using the appropriate image data enabling an accurate classification. the proposed system has been developed in a lab environment. A detailed presentation of the system implementation, its performance and the potential integration in a real industrial environment are discussed in this paper. © 2022 Elsevier B.V.. All rights reserved.

关键词： computer vision

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Active learning for robot manipulation 16

Active learning for robot manipulation

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16th European conference on Artificial Intelligence, ECAI 2004

作者： Morales, Antonio Chinellato, Eris Fagg, Andrew H. Del Pobil, Angel P. Intelligent Robotics Lab. Univertitat Jaume I Castellon Spain Computer Science Department University of Massachusetts AmherstMA United States

ISBN: (纸本)9781586034528

this paper describes a novel application of active learning techniques in the field of robotic grasping. A vision-based grasping system has been implemented on a humanoid robot. It is able to compute a set of feasible grasps and to execute any of them and measure their actual reliability. An algorithm aimed at predicting the performance of an untested grasp using the results observed on previous similar attempts is presented. the previous experience is stored using a set of vision-based grasp descriptors. Moreover, a second algorithm that actively selects the next grasp to be executed in order to improve the predictive quality of the accumulated experience is introduced. An exhaustive database of experimental data is collected and used to test and validate both algorithms.

关键词： Anthropomorphic robots

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6th International Symposium on Signal Processing and intelligent Recognition Systems, SIRS 2020

6th International Symposium on Signal Processing and Intelli...

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6th International Symposium on Advances in Signal Processing and intelligent Recognition Systems, SIRS 2020

ISBN: (纸本)9789811604249

the proceedings contain 27 papers. the special focus in this conference is on Signal Processing and intelligent Recognition Systems. the topics include: Improved Long-Short Term Memory U-Net for Image Segmentation;Extraction of Parcel Boundary from UAV Images Using Deep Learning techniques;Grad-CAM-Based Classification of Chest X-Ray Images of Pneumonia Patients;periocular Recognition Under Unconstrained Image Capture Distances;acoustic Prediction of Elephants for Localization and Movement Tracking Using Sensors and Distance Metrics;community Detection algorithms in Complex Networks: A Survey;a Systematic Review on the Influence of User Personality in Rumor and Misinformation Propagation through Social Networks;HRIDAI: A Tale of Two Categories of ECGs;channel-Aware Decision Fusion with Rao Test for Multisensor Fusion;detection of Breast Cancer from Mammogram Images Using Deep Transfer Learning;Comprehending the Dynamics of EEG Generated Under Various Odorant Stimulation on the Brain;Robust Beamforming Against DOA Mismatch with Null Widening for Moving Interferences;6G Ultra-Low Latency Communication in Future Mobile XR Applications;applying Neural Style Transfer to Spectrograms of Environmental Audio;identification of Indian English by Speakers of Multiple Native Languages;Robust and Imperceptible Digital Image Watermarking Based on DWT-DCT-Schur;comparative Study of Maturation Profiles of Neural Cells in Different Species with the Help of computer vision and Deep Learning;deep Learning algorithms to Detect and Localize Acute Intracranial Hemorrhages;Explainable NLP: A Novel Methodology to Generate Human-Interpretable Explanation for Semantic Text Similarity;a Leak Detection in Water Pipelines Using Discrete Wavelet Decomposition and Artificial Neural Network;supervised Feature Learning for Music Recommendation.

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Real-time background modeling based on classified dynamic objects for human robot application

Real-time background modeling based on classified dynamic ob...

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9th International conference on Networked Sensing Systems, INSS 2012

作者： Moro, Alessandro Mumolo, Enzo Nolich, Massimiliano Terabayashi, Kenji Umeda, Kazunori Department of Precision Mechanics Faculty of Science and Engineering Chuo University/CREST 1-13-27 Kasuga Bunkyo-ku Tokyo 112-8551 Japan DI3 University of Trieste Via Valerio 10 34123 Trieste Italy IFACE s.r.l. Via Valerio 10 34123 Trieste Italy

ISBN: (纸本)9781467317856

the aim of this paper is to describe a flexible and robust background management algorithm. In these years various techniques were proposed to segment the images from a video stream sequence, and detect interesting dynamic objects. Many works faced the problem to segment the image in indoor environment for human detection and intelligent room applications. In these works, both accuracy and efficiency depend on the background model they used. Specific high performances models suffer of some limitations in chaotic unstructured environments. Long video stream sequences changes in light condition, and object and human displacement. In those environments, dynamic to stable objects and humans can be absorbed in the background, and then become invisible to the system. In this work we propose an approach to combine low level and high level information to improve the background management and to solve unpredictable object dynamic problems. Experimental recall and precision results show improved performances with respect to popular background management algorithms. Finally, a real application is shown and discussed. © 2012 IEEE.

关键词： computer vision

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An intelligent Pipeline for Localization of Industrial Components in Robotic Manufacturing Applications 27

An Intelligent Pipeline for Localization of Industrial Compo...

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27th European conference on Artificial Intelligence, ECAI 2024

作者： Rawal, Parth Valencia, Daniel Hintze, Wolfgang Ottenbecker Damm 12 Stade21684 Germany Hamburg University of Technology TUHH Denickestraße 15 Hamburg21071 Germany

ISBN: (纸本)9781643685489

the rising skill shortage problem in Europe threatens the economic slowdown in the manufacturing sector. Approaches based on artificial intelligence can play a crucial role in bridging the shortage gap if they can be integrated into robot-assisted production to simplify repetitive manual tasks. Localizing components in a production cell is a familiar problem of robot-assisted production. robots are often taught trajectories manually, which requires expertise in robot programming. Some of the existing feature-based computer vision solutions can localize a component in 3D space. However, these solutions are not versatile enough to be integrated across different components and production cells. this paper proposes an AI-based solution in the form of a pipeline for the 6D localization of components that can be integrated into multiple industrial use cases. the pipeline encompasses flows for generating synthetic images of components from their CAD model, training deep neural networks to estimate component poses, and improving their accuracy for manufacturing applications. the performance of the pipeline has been validated for components in a production-related environment. the paper also demonstrates the versatility of the pipeline by deploying it for a robotic spray coating use case. Such AI skills can empower the skilled workforce on the shop floor so that they can focus on the overall manufacturing process. © 2024 the Authors.

关键词： Deep neural networks

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Expert self-learning controller for robot manipulator

Expert self-learning controller for robot manipulator

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IEEE conference on Decision and Control

作者： Z. Geng M. Jamshidi Department of Electrical Engineering and Computer Science George Washington University Washington D.C. DC USA CAD Laboratory of Systems / Robotics Department of ECE University of New Mexico Albuquerque NM USA

Real-time expert system techniques and applications to robot manipulator control systems are discussed. A novel type of intelligent controller structure, the expert learning controller prototype ELEC (expert learning controller), is developed for the trajectory tracking control in repeat operations. ELEC, acting as an intelligent real-time controller in a closed-loop system, can modify the control series in a human-expert-like manner using the experience of previous operations in order to force the system output to converge to the prespecified desired trajectory. ELEC does not require the knowledge of system models, so it can be used in a fairly wide range of control problems. A numerical example for a two-link robot manipulator is given which shows the satisfactory performance of ELEC.< >

关键词： Robot control Manipulators Control systems Real time systems intelligent robots Force control Expert systems intelligent structures Prototypes Trajectory

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Scenario-based simulation of intelligent driving functions using neural networks

Scenario-based simulation of intelligent driving functions u...

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International conference on Computing, Communication and Networking Technologies

作者： Parampreet Kaur Rajeev Sobti School of Computer Science Engineering Lovely Professional University Jalandhar India

the modern technological era is witnessing a significant advancement in AI related fields such as machine learning, mobile robots and autonomous vehicles. the success of such systems is immensely dependent on computer vision algorithms. the entirely software enabled intelligent vehicles will soon be hitting the roads in the coming decade. Such self-controlled mobile robots may still be vulnerable to accidents or crashes. therefore, the industry requires some state of the art techniques to substantiate the safety and protection of its passengers as well as other road users. Simulation based testing methods have been in use from a long time, but the newer smart vehicle innovations require better versions of simulation techniques. We thus provide an improved mechanism for simulation testing to validate safe navigation of cars in variety of traffic scenarios. Neural network approach integrated with agent based modelling is described in this paper.

关键词： Agent based modelling intelligent driving SAE autonomy levels Simulation ADAS

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