Search results for "Roboti"

showing 10 items of 687 documents

Safety assurance of an industrial robotic control system using hardware/software co-verification

2022

As a general trend in industrial robotics, an increasing number of safety functions are being developed or re-engineered to be handled in software rather than by physical hardware such as safety relays or interlock circuits. This trend reinforces the importance of supplementing traditional, input-based testing and quality procedures which are widely used in industry today, with formal verification and model-checking methods. To this end, this paper focuses on a representative safety-critical system in an ABB industrial paint robot, namely the High-Voltage electrostatic Control system (HVC). The practical convergence of the high-voltage produced by the HVC, essential for safe operation, is f…

FOS: Computer and information sciencesFormal Languages and Automata Theory (cs.FL)Formal methodsVerificationComputer Science - Formal Languages and Automata Theory:Teknisk kybernetikk: 553 [VDP]VDP::Teknisk kybernetikk: 553RoboticsComputer Science - RoboticsVDP::Technical cybernetics: 553:Technical cybernetics: 553 [VDP]VerifikasjonFormelle metoderRobotikkRobotics (cs.RO)Software
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Depth-Adapted CNN for RGB-D cameras

2020

Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the photometric information on a fixed neighborhood without taking into account the geometric information. We tackle the problem of improving the classical RGB CNN methods by using the depth information provided by the RGB-D cameras. State-of-the-art approaches use depth as an additional channel or image (HHA) or pass from 2D CNN to 3D CNN. This paper proposes a novel and generic procedure to articulate both photometric and geometric information in CNN architecture. The depth data is represented as a 2D offset to adapt …

FOS: Computer and information sciencesOffset (computer science)Computer scienceComputer Vision and Pattern Recognition (cs.CV)Coordinate systemComputer Science::Neural and Evolutionary ComputationComputer Science - Computer Vision and Pattern RecognitionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyConvolutional neural network030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicine0202 electrical engineering electronic engineering information engineering[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Computer visionInvariant (mathematics)business.industry[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO]020207 software engineeringWeightingSpatial coherenceComputer Science::Computer Vision and Pattern RecognitionRGB color modelArtificial intelligencebusinessLinear filter
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Random Walk in a N-cube Without Hamiltonian Cycle to Chaotic Pseudorandom Number Generation: Theoretical and Practical Considerations

2017

Designing a pseudorandom number generator (PRNG) is a difficult and complex task. Many recent works have considered chaotic functions as the basis of built PRNGs: the quality of the output would indeed be an obvious consequence of some chaos properties. However, there is no direct reasoning that goes from chaotic functions to uniform distribution of the output. Moreover, embedding such kind of functions into a PRNG does not necessarily allow to get a chaotic output, which could be required for simulating some chaotic behaviors. In a previous work, some of the authors have proposed the idea of walking into a $\mathsf{N}$-cube where a balanced Hamiltonian cycle has been removed as the basis o…

FOS: Computer and information sciencesUniform distribution (continuous)Computer Science - Cryptography and SecurityComputer scienceHamiltonian CycleChaoticPseudorandom Numbers GeneratorFOS: Physical sciences02 engineering and technology[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]01 natural sciencesUpper and lower bounds[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computingsymbols.namesake[INFO.INFO-MC]Computer Science [cs]/Mobile Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]0202 electrical engineering electronic engineering information engineeringApplied mathematics[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]0101 mathematicsEngineering (miscellaneous)Pseudorandom number generatorChaotic IterationsBasis (linear algebra)Applied Mathematics020208 electrical & electronic engineering010102 general mathematicsRandom walkNonlinear Sciences - Chaotic DynamicsHamiltonian path[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationNonlinear Sciences::Chaotic Dynamics[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Modeling and SimulationRandom Walk[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]symbolsPseudo random number generator[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]Chaotic Dynamics (nlin.CD)[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Cryptography and Security (cs.CR)
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RGB-Event Fusion for Moving Object Detection in Autonomous Driving

2022

Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving. Despite plausible results of deep learning methods, most existing approaches are only frame-based and may fail to reach reasonable performance when dealing with dynamic traffic participants. Recent advances in sensor technologies, especially the Event camera, can naturally complement the conventional camera approach to better model moving objects. However, event-based works often adopt a pre-defined time window for event representation, and simply integrate it to estimate image intensities from events, neglecting much of the rich temporal information from the available asynchronous ev…

FOS: Computer and information sciences[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Computer Science - Robotics[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Computer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern Recognition[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Robotics (cs.RO)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks

2022

Monocular depth estimation has been a popular area of research for several years, especially since self-supervised networks have shown increasingly good results in bridging the gap with supervised and stereo methods. However, these approaches focus their interest on dense 3D reconstruction and sometimes on tiny details that are superfluous for autonomous navigation. In this paper, we propose to address this issue by estimating the navigation map under a quadtree representation. The objective is to create an adaptive depth map prediction that only extract details that are essential for the obstacle avoidance. Other 3D space which leaves large room for navigation will be provided with approxi…

FOS: Computer and information sciences[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Computer Vision and Pattern Recognition (cs.CV)[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO]Computer Science - Computer Vision and Pattern Recognition
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Good Old-Fashioned Artificial Consciousness and the Intermediate Level Fallacy

2018

Recently, there has been considerable interest and effort to the possibility to design and implement conscious robots, i.e., the chance that a robot may have subjective experiences. However, typical approaches as the global workspace, information integration, enaction, cognitive mechanisms, embodiment, i.e., the Good Old-Fashioned Artificial Consciousness, henceforth, GOFAC, share the same conceptual framework. In this paper, we discuss GOFAC's basic tenets and their implication for AI and Robotics. In particular, we point out the intermediate level fallacy as the central issue affecting GOFAC. Finally, we outline a possible alternative conceptual framework towards robot consciousness.

Fallacyartificial consciousnessComputer sciencemedia_common.quotation_subjectlcsh:Mechanical engineering and machinerymachine consciousnessArtificial consciousness050105 experimental psychologylcsh:QA75.5-76.95Enactivism03 medical and health sciences0302 clinical medicineArtificial IntelligenceHypothesis and Theory0501 psychology and cognitive scienceslcsh:TJ1-1570media_commonrobot consciousness; machine consciousness; artificial consciousness; synthetic phenomenology; robot self-awarenessrobot consciousneartificial consciousneCognitive scienceSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniRobotics and AIIntegrated information theory05 social sciencesHard problem of consciousnessComputer Science Applicationsrobot self-awarenessConceptual frameworkRobotlcsh:Electronic computers. Computer scienceConsciousnessrobot consciousnesssynthetic phenomenologymachine consciousne030217 neurology & neurosurgeryFrontiers in Robotics and AI
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Control of a PTZ camera in a hybrid vision system

2014

In this paper, we propose a new approach to steer a PTZ camera in the direction of a detected object visible from another fixed camera equipped with a fisheye lens. This heterogeneous association of two cameras having different characteristics is called a hybrid stereo-vision system. The presented method employs epipolar geometry in a smart way in order to reduce the range of search of the desired region of interest. Furthermore, we proposed a target recognition method designed to cope with the illumination problems, the distortion of the omnidirectional image and the inherent dissimilarity of resolution and color responses between both cameras. Experimental results with synthetic and real …

Fisheye cameratarget detection[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO][ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]PTZhybrid vision system
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Robotic assistance for industrial sanding with a smooth approach to the surface and boundary constraints

2021

[EN] Surface treatment operations, such as sanding, deburring, finishing, grinding, polishing, etc. are progressively becoming more automated using robotic systems. However, previous research in this field used a completely automatic operation of the robot system or considered a low degree of human-robot interaction. Therefore, to overcome this issue, this work develops a truly synergistic cooperation between the human operator and the robot system to get the best from both. In particular, in the application developed in this work the human operator provides flexibility, guiding the tool of the robot system to treat arbitrary regions of the workpiece surface; while the robot system provides…

Flexibility (engineering)021103 operations researchGeneral Computer ScienceOrientation (computer vision)Computer scienceWork (physics)0211 other engineering and technologiesGeneral EngineeringPolishingControl engineering02 engineering and technologyField (computer science)INGENIERIA DE SISTEMAS Y AUTOMATICAGrindingSmooth approachHuman-robot cooperationBoundary constraints0202 electrical engineering electronic engineering information engineeringRobot020201 artificial intelligence & image processingEnginyeria de sistemesRobotic armRobots
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Smart camera design for intensive embedded computing

2005

Computer-assisted vision plays an important role in our society, in various fields such as personal and goods safety, industrial production, telecommunications, robotics, etc. However, technical developments are still rare and slowed down by various factors linked to sensor cost, lack of system flexibility, difficulty of rapidly developing complex and robust applications, and lack of interaction among these systems themselves, or with their environment. This paper describes our proposal for a smart camera with real-time video processing capabilities. A CMOS sensor, processor and, reconfigurable unit associated in the same chip will allow scalability, flexibility, and high performance.

Flexibility (engineering)CMOS sensorbusiness.industryComputer scienceIndustrial productionRoboticsVideo processingEmbedded systemSignal ProcessingScalabilityComputer Vision and Pattern RecognitionSmart cameraArtificial intelligenceElectrical and Electronic EngineeringImage sensorbusinessReal-Time Imaging
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Deep Learning for Classifying Physical Activities from Accelerometer Data

2021

Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify the physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the proposed models on two phy…

Fysisk aktivitetComputer scienceVDP::Informasjons- og kommunikasjonsteknologi: 550physical activityAccelerometercomputer.software_genresensorsBiochemistryMedical careRNNAnalytical Chemistry:Information and communication technology: 550 [VDP]Accelerometer dataAccelerometryartificial_intelligence_roboticsInstrumentationArtificial neural networkhealthAtomic and Molecular Physics and Opticsmachine learningclassificationHealthFeedforward neural network:Informasjons- og kommunikasjonsteknologi: 550 [VDP]Physical activityTP1-1185Movement activityMachine learningHelseFeed-forward neural networksVDP::Information and communication technology: 550ArticleFysisk aktiviteterMachine learningHumansAccelerometer dataElectrical and Electronic EngineeringExercisebusiness.industryPhysical activitySensorsDeep learningChemical technologydeep learningDeep learningfeed-forward neural networkRecurrent neural networkPhysical activitiesDiabetes Mellitus Type 2Recurrent neural networksaccelerometer dataUCIrecurrent neural networkNeural Networks ComputerArtificial intelligenceClassificationsbusinesscomputerDNN
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