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检索条件"任意字段=Conference on Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques"

共 375 条记录，以下是351-360 订阅

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An Adaptive Focal Connectivity Algorithm for Multifocus Fusion

An Adaptive Focal Connectivity Algorithm for Multifocus Fusi...

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conference on computer vision and Pattern Recognition (CVPR)

作者： Harishwaran Hariharan Andreas Koschan Mongi Abidi Imaging Robotics and Intelligent Systems Laboratory University of Tennessee Knoxville TN USA

Multifocus fusion is the process of fusing focal information from a set of input images into one all-in-focus image. Here, a versatile multifocus fusion algorithm is presented for application-independent fusion. A focally connected region is a region or a set of regions in an input image that falls under the depth of field of the imaging system. Such regions are segmented adaptively under the predicate of focal connectivity and fused by partition synthesis. The fused image has information from all focal planes, while maintaining the visual verisimilitude of the scene. In order to validate the fusion performance of our method, we have compared our results with those of tiling and multiscale fusion techniques. In addition to performing a seamless fusion of the focally connected regions, our method out performs the competing methods regarding overall sharpness in all our experiments. Several illustrative examples of multifocus fusion are shown and objective comparisons are provided.

关键词： Layout Image segmentation Focusing Lenses Inspection Microscopy intelligent robots Partitioning algorithms Geometry intelligent systems

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Designing and making the intelligence assistant robot and controlling it by the fuzzy procedure

Designing and making the intelligence assistant robot and co...

引用

International conference on Electronics, computer and Computation (ICECCO)

作者： Iman Zabbah Shima Foolad Bahram Chaharaqran Reza Mazlooman Department of computer Islamic Azad University Iran Department of computer Semnan University Semnan Iran Department of computer Maragheh Sanati University Maragheh Iran National Iranian Oil Products Distribution Company Iran

Mobile robots are those with the capability of moving in an environment. The controller of these robots needs to have the ability to do intelligent behaviours in an uncertain environment. Today fuzzy control of mobile robots in non-structured environments is increasing. Many represented algorithms for mobile robot navigation don't manifest appropriate performance in dynamic environments. This article aims at designing and making an intelligence robot. This robot is able to detect a specified target and track it. Velocity and steering of this robot is controlled by the fuzzy logic. Robot velocity is adjusted according to its distance from the target. This article is expressed the features of this mobile robot including different mechanical and electronic infrastructures, fuzzy navigation and vision controller. Navigation system of this robot makes its fuzzy decisions only based on the machine vision techniques and unlike many robots, it doesn't depend on sensor. It is concluded that robot with fuzzy navigation have 60% time saving than the non-fuzzy. The results obtained indicate its appropriate performance in order to navigate a target-oriented mobile robot in an undetermined and dynamic environment.

关键词： Mobile robots Robot kinematics Robot sensing systems Navigation Wheels DC motors

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Auditory and visual integration based localization and tracking of humans in daily-life environments

Auditory and visual integration based localization and track...

引用

2007 IEEE/RSJ International conference on intelligent robots and Systems

作者： Hyun-Don Kim Kazunori Komatani Tetsuya Ogata Hiroshi G. Okuno Speech Media Processing Group Kyoto University Kyoto Japan

The purpose of this research is to develop techniques that enable robots to choose and track a desired person for interaction in daily-life environments. Therefore, localizing multiple moving sounds and human faces is necessary so that robots can locate a desired person. For sound source localization, we used a cross-power spectrum phase analysis (CSP) method and showed that CSP can localize sound sources only using two microphones and does not need impulse response data. An expectation-maximization (EM) algorithm was shown to enable a robot to cope with multiple moving sound sources. For face localization, we developed a method that can reliably detect several faces using the skin color classification obtained by using the EM algorithm. To deal with a change in color state according to illumination condition and various skin colors, the robot can obtain new skin color features of faces detected by OpenCV, an open vision library, for detecting human faces. Finally, we developed a probability based method to integrate auditory and visual information and to produce a reliable tracking path in real time. Furthermore, the developed system chose and tracked people while dealing with various background noises that are considered loud, even in the daily-life environments.

关键词： Humans Face detection Skin Change detection algorithms Microphones Lighting Robot vision systems computer vision Libraries Background noise

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Autonomous mental development in high dimensional state and action spaces

Autonomous mental development in high dimensional state and ...

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International Joint conference on Neural Networks (IJCNN)

作者： A. Joshi Juyang Weng Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA Department of Computer Science and Engineering Michigan State University East Lansing MI USA

Autonomous mental development (AMD) of robots opened a new paradigm for developing machine intelligence, using neural network type of techniques and it fundamentally changed the way an intelligent machine is developed from manual to autonomous. The work presented is a part of SAIL (self-organizing autonomous incremental learner) project which deals with autonomous development of entire humanoid robot with vision, audition, manipulation and locomotion. The major issue addressed is the challenge of high dimensional action space (5 to 10) in addition to the high dimensional context state space (hundreds to thousands and beyond), typically required by an AMD machine. This is the first work that studies a high dimensional (numeric) action space in conjunction with a high dimensional perception (context state) space, under the AMID mode. Two new learning algorithms, Direct Update on Direction Cosines (DUDC) and High-Dimensional Conjugate Gradient Search (HCGS), are developed, implemented and tested. The convergence properties of both the algorithms and their targeted applications are discussed. Autonomous learning of speech production under reinforcement learning is studied as an example.

关键词： Autonomous mental development intelligent robots Orbital robotics Machine intelligence Neural networks intelligent networks Learning systems Manuals Humanoid robots State-space methods

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Towards vision-Based Impedance Control for the Contact Inspection of Unknown Generically-Shaped Surfaces with a Fully-Actuated UAV

Towards Vision-Based Impedance Control for the Contact Inspe...

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2020 IEEE/RSJ International conference on intelligent robots and Systems (IROS)

作者： Ramy Rashad Davide Bicego Ran Jiao Santiago Sanchez-Escalonilla Stefano Stramigioli Robotics and Mechatronics group University of Twente Enschede The Netherlands School of Mechanical Engineering and Automation Beihang University Beijing China Robotics and Mechatronics group University of Twente and ITMO University Saint Petersburg Russia

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

The integration of computer vision techniques for the accomplishment of autonomous interaction tasks represents a challenging research direction in the context of aerial robotics. In this paper, we consider the problem of contact-based inspection of a textured target of unknown geometry and pose. Exploiting state of the art techniques in computer graphics, tuned and improved for the task at hand, we designed a framework for the projection of a desired trajectory for the robot end-effector on a generically-shaped surface to be inspected. Combining these results with previous work on energy-based interaction control, we are laying the basis of what we call vision-based impedance control paradigm. To demonstrate the feasibility and the effectiveness of our methodology, we present the results of both realistic ROS/Gazebo simulations and preliminary experiments with a fully-actuated hexarotor interacting with heterogeneous curved surfaces whose geometric description is not available a priori, provided that enough visual features on the target are naturally or artificially available to allow the integration of localization and mapping algorithms.

关键词： Surface impedance Inspection End effectors Trajectory Surface texture Impedance Task analysis

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Reexamining Indoor Mobile robots from a Cognitive Perspective

Reexamining Indoor Mobile Robots from a Cognitive Perspectiv...

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Robotics and computer vision (ICRCV), International conference on

作者： Fei Song Jinyu Li Fengzhen Tang Yandong Tang Bailu Si State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China School of Systems Science Beijing Normal University Beijing China

The field of traditional mobile robot navigation has undergone a gradual transformation, evolving into a standardized and procedural research domain. Through a fresh cognitive perspective on this navigation process, a bridge emerges between robotic researchers and cognitive neuroscientists, thereby fostering the growth of interdisciplinary exploration. This article meticulously examines three pivotal phases of robot navigation: information acquisition, simultaneous localization and mapping (SLAM), and path planning. These phases are intricately linked to pertinent cognitive research. Furthermore, a comprehensive survey of existing biomimetic and neuromorphic algorithms is presented, highlighting their applications in augmenting robot navigation capabilities. Ultimately, this article charts a visionary course for the future of robot navigation, envisioning its potential in facilitating more intelligent and efficient navigation techniques.

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3D feature based mapping towards mobile robots' enhanced performance in rescue missions

3D feature based mapping towards mobile robots' enhanced per...

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2009 IEEE/RSJ International conference on intelligent robots and Systems

作者： Paloma de la Puente Diego Rodriguez-Losada Alberto Valero Fernando Matia Intelligent Control Group Universidad Politecnica de Madrid Madrid Spain

This paper presents a feature based 3D mapping approach with regard to obtaining compact models of semi-structured environments such as partially destroyed buildings where mobile robots are to carry out rescue activities. To gather the 3D data, we use a laser scanner, employing a nodding data acquisition system mounted on both real and simulated robots. Our segmentation algorithm comes up from the integration of computer vision techniques, allowing for a fast separation of points corresponding to different, not necessarily planar, surfaces. The subsequent extraction of geometrical features out of each region's points is done by means of least-squares fitting. A Maximum Incremental Probability algorithm formulated upon the Extended Kalman Filter provides 6D localization and produces a map of planar patches with a convex-hull based representation. Scenarios from the Unified System for Automation and Robot Simulation (USARSim), including world models from past RoboCup Rescue editions' arenas, have been utilized to conduct some of our experiments.

关键词： Mobile robots Robotics and automation intelligent robots Laser modes Feature extraction Solid modeling Iterative algorithms Iterative closest point algorithm Simultaneous localization and mapping Noise measurement

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Visual and Inertial Multi-Rate Data Fusion for Motion Estimation via Pareto-Optimization

Visual and Inertial Multi-Rate Data Fusion for Motion Estima...

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IEEE/RSJ International conference on intelligent robots and Systems

作者： Giuseppe Loianno Vincenzo Lippiello Carlo Fischione Bruno Siciliano PRISMA Lab Department of Electrical Engineering and Information Technology University of Naples Federico II via Claudio 21 80125 Naples Italy ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

Motion estimation is an open research field in control and robotic applications. Sensor fusion algorithms are generally used to achieve an accurate estimation of the vehicle motion by combining heterogeneous sensors measurements with different statistical characteristics. In this paper, a new method that combines measurements provided by an inertial sensor and a vision system is presented. Compared to classical model-based techniques, the method relies on a Pareto optimization that trades off the statistical properties of the measurements. The proposed technique is evaluated with simulations in terms of computational requirements and estimation accuracy with respect to a classical Kalman filter approach. It is shown that the proposed method gives an improved estimation accuracy at the cost of a slightly increased computational complexity.

关键词： Motion Estimation via Pareto-Optimization control robotic applications motion estimation Pareto optimisation Inertial Visualization Standing Orders Data fusion data integration Kalman filters computer vision complexity classes

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Feature detection and map building using ranging sensors

Feature detection and map building using ranging sensors

引用

International conference on intelligent Transportation

作者： M. Kulich P. Stepan L. Preucil The Gerstner Laboratory for Intelligent Decision Making and Control Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

This paper describes two methods for feature extraction. The goal is to interpret the data obtained from sonar measurements of the robot's environment as geometric map primitives. The extracted primitives can be used to update the robot position and the global map. This is the process how the robot can explore an unknown environment. The first algorithm gathers sonar measurements into sensor-based map and then using computer-vision techniques and morphological operations extracts geometric description of detected obstacles. The second method is started whenever the border tracking algorithm has been activated. This method uses raw sonar data as the input while obstacle border is robustly detected by the border tracking process. The algorithms have been verified with an experimental mobile robot at the Czech Technical University.

关键词： computer vision Robot sensing systems Sonar measurements Mobile robots Sonar detection Sensor fusion intelligent sensors Data mining Robustness Laboratories

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Development of computer vision Based Aluminium Scrap Sorting Using Real Time Metal Scrap Dataset

Development of Computer Vision Based Aluminium Scrap Sorting...

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Sustainable Expert Systems (ICSES), International conference on

作者： Adhithya S J. Kathirvelan School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The aluminium manufacturing industries face numerous challenges when it comes to sorting metal scraps, particularly when scraps are small in size. Traditional methods like flotation and gravity separation are inefficient, energy-intensive, and often fail when dealing with size variations. Recent advancements in computer vision and deep learning algorithms, including intelligent robotic systems, offer promising solutions. However, challenges such as accurate detection in complex environments, varying lighting conditions, and overlapping objects still persist. Newer techniques like YOLO (You Only Look Once) for real-time object detection, and Transfer Learning to improve model accuracy with smaller datasets, have shown potential. This research aims to overcome these challenges by developing an AI-powered, computer vision-based sorting system that can identify and classify aluminium scraps by color and shape at high speed. The authors developed a custom dataset and trained models using CNN architectures such as DenseNet-161, ResNet-152, and VGG 19. By employing ensemble techniques, this research achieved an 75% of accuracy.

关键词： Deep learning YOLO computer vision Accuracy Service robots Computational modeling Aluminum Real-time systems Recycling Sorting

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