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3D vision for Precision Dairy Farming

IFAC-PapersOnLine 2019年第30期52卷 312-317页

作者： Niall O’ Mahony Sean Campbell Anderson Carvalho Lenka Krpalkova Daniel Riordan Joseph Walsh IMaR Research Centre Institute of Technology Tralee Tralee Co. Kerry Ireland

3D vision systems will play an important role in next-generation dairy farming due to the sensing capabilities they provide in the automation of animal husbandry tasks such as the monitoring, herding, feeding, milking and bedding of animals. This paper will review 3D computer vision systems and techniques that are and may be implemented in Precision Dairy Farming. This review will include evaluations of the applicability of Time of Flight and Streoscopic vision systems to agricultural applications as well as a breakdown of the categories of computer vision algorithms which are being explored in a variety of use cases. These use cases range from robotic platforms such as milking robots and autonomous vehicles which must interact closely and safely with animals to intelligent systems which can identify dairy cattle and detect deviations in health indicators such as Body Condition Score and Locomotion Score. Upon analysis of each use case, it is apparent that systems which can operate in unconstrained environments and adapt to variations in herd characteristics, weather conditions, farmyard layout and different scenarios in animal-robot interaction are required. Considering this requirement, this paper proposes the application of techniques arising from the emerging field of research in Artificial Intelligence that is Geometric Deep Learning.

关键词： Automation Robotics in Agriculture Sensing Automation in Animal Farming

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Heterogeneous Multi-Robot Localization in Unknown 3D Space

Heterogeneous Multi-Robot Localization in Unknown 3D Space

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2006 IEEE/RSJ International Conference on intelligent robots and Systems

作者： Yi Feng Zhigang Zhu Jizhong Xiao Department of Computer Science CUNY NY USA Department of Computer Science City College of New York NY USA

This paper presents a self-localization strategy for a team of heterogeneous mobile robots including ground mobile robots of various sizes and wall-climbing robots. These robots are equipped with various visual sensors, such as miniature webcams, omnidirectional cameras, and PTZ cameras. As the core of this work, a formation of four-robot team is constructed to operate in a 3D space, e.g., moving on ground, climbing on walls and clinging to ceilings. The four robots could dynamically localize themselves asynchronously by employing cooperative vision techniques. Three of them on the ground mutually view each other and determine their relative poses with 6 degrees of freedom (DOFs). A wall-climbing robot, which significantly extends the work space of the robot team to 3D, is at a vantage point (e.g., on the ceiling) such that it can see all the three teammates, thus determining its own location and orientation. The four-robot formation theory and algorithms are presented, and experimental results with both simulated and real image data are provided to demonstrate the feasibility of this formation. Two 3D localization and control strategies are designed for applications such as search and rescue and surveillance in 3D urban environments where robots must be deployed in a full 3D space

关键词： Orbital robotics Robot kinematics Mobile robots Robot sensing systems Robot vision systems Cameras Service robots intelligent robots computer science Cities and towns

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Unified approach to control point detection and stereo disparity computation

Unified approach to control point detection and stereo dispa...

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intelligent robots and computer vision XV: algorithms, techniques,Active vision, and Materials Handling

作者： Poelzleitner, Wolfgang Casasent, David P. Carnegie Mellon Univ. and Joanneum Research Cent. Graz Austria Austria

ISBN: (纸本)0819423068

We consider two problems: first, the problem of detection of objects in images of 3D planetary terrain;second, the task of finding corresponding points for stereo matching of this type of images. We propose an approach that is simultaneously applicable to both problem areas. The approach uses a bank of filters based on different 2D Gabor functions. By detection we mean locating multiple classes of targets with distortions present and in a cluttered background. It is desirable to minimize false alarms due to clutter, image noise, and the presence of other objects. In the scenario of stereo matching, the pixel location where we search for the corresponding point is the target, while all ambiguous matches are non-targets. In this work, we use Gabor filter banks in two versions. First, for fast detection of targets, the single filter outputs of the bank are fused by linear combination. Second, for stereo matching, the outputs of the filters form a feature vector used to find the best match. We refer to both types of filters as a macro Gabor filter. In the linear combination case, the filter bank forms a single filter. This filter is correlated with an input image, followed by local maximum detection, and thresholding to yield the finally detected targets. The new aspects are: combining real and imaginary parts of GFs into one filter using centered on off-center GFs, separately optimizing the fusion coefficients of the GFs by controlling the shape of the correlation outputs of each filter alone, and the application to two different scenarios.

关键词： Binocular vision

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On the complexity of computing equilibria for nonsymmetric analog networks

On the complexity of computing equilibria for nonsymmetric a...

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International Joint Conference on Neural Networks (IJCNN)

作者： D.A. Miller S.W. Sucker Computer Vision and Robotics Laboratory Research Center of Intelligent Machines McGill University Montreal Canada Comput. Vision & Robotics Lab. McGill Univ. Montreal Que. Canada

The authors ask what is the complexity of computing equilibria for physically realizable analog networks like those of J.J. Hopfield (1984) and T.J. Sejnowski (1981) with arbitrary connectivity. It is shown that, if the amplifiers are piecewise-linear, then such networks are instances of a game-theoretic model known as polymatrix games. Equilibria for the latter may be computed by vertex pivoting algorithms similar to the simplex method for linear programming, which are in practice of low order polynomial complexity. These algorithms appear to be the only ones both guaranteed to work and which are polynomial in practice. These results suggest that networks with few nonstable equilibria would be computationally attractive.< >

关键词： Analog computers computer networks Polynomials Biology computing NP-hard problem computer vision Physics computing Piecewise linear techniques intelligent robots Robot vision systems

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intelligent robot trends and predictions for the new millennium

Proceedings of SPIE - The International Society for Optical ...

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3837卷 14-25页

作者： Hall, Ernest L. Mundhenk, Nathan

ISBN: (纸本)0819434302

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The current use of these machines in outer space, medicine, hazardous materials, defense applications and industry is being pursued with vigor but little funding. In factory automation such robotics machines can improve productivity, increase product quality and improve competitiveness. The computer and the robot have both been developed during recent times. The intelligent robot combines both technologies and requires a thorough understanding and knowledge of mechatronics. In honor of the new millennium, this paper will present a discussion of futuristic trends and predictions. However, in keeping with technical tradition, a new technique for `Follow the Leader' will also be presented in the hope of it becoming a new, useful and non-obvious technique. Today's robotic machines are faster, cheaper, more repeatable, more reliable and safer. The knowledge base of inverse kinematic and dynamic solutions and intelligent controls is increasing. More attention is being given by industry to robots, vision and motion controls. New areas of usage are emerging for service robots, remote manipulators and automated guided vehicles. Economically, the robotics industry now has more than a billion-dollar market in the U.S. and is growing. Feasibility studies show decreasing costs for robots and unaudited healthy rates of return for a variety of robotic applications. However, the road from inspiration to successful application can be long and difficult, often taking decades to achieve a new product. A greater emphasis on mechatronics is needed in our universities. Certainly, more cooperation between government, industry and universities is needed to speed the development of intelligent robots that will benefit industry and society.

关键词： intelligent robots

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A Novel computer vision Algorithm for Enhancing Image Recognition and Classification

A Novel Computer Vision Algorithm for Enhancing Image Recogn...

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IEEE International Conference on Power, intelligent Computing and Systems (ICPICS)

作者： Herun Zhong School of Arts and Design Hubei University of Technology Wuhan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

computer vision focuses on optimizing computers to understand and interpret visual data from photos or movies, while image recognition specializes in detecting and categorizing objects or patterns in photographs. Technological advancements have revolutionized the field of computer science and found applications in various fields such as robotics, healthcare, security, and entertainment. This article offers a comprehensive overview of the novel approaches of computer vision algorithms widely used in enhancing image recognition and classification. The discipline deals with extracting, analyzing, and understanding information from images or videos by developing algorithms that enable robots to see and comprehend visual information similar to humans. Image recognition primarily emphasizes automatic identification and categorization of objects or patterns in pictures. According to findings, significant developments have been made due to large datasets availability along with improvements in deep learning techniques leveraging enhanced processor power. Convolutional neural networks are now the dominant approach for image recognition problems within deep learning field attaining consistently high-performance levels across several benchmarks.

关键词： Deep learning computer vision Visualization Image recognition Benchmark testing Visual systems Classification algorithms Object recognition Convolutional neural networks Videos

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Real-time Path Planning for intelligent robots System Based on vision Information

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IFAC Proceedings Volumes 1999年第2期32卷 861-866页

作者： Qingchun Meng Changjiu Zhou K. Jagannathan K.S. Leong Hongbo Ji Department of Electronics & Communication Engineering of Singapore Polytechnic 139651 Tel: 65 7721440 Fax: 65 7721974. Singapore Computer Science Dept. of Yantai University 264005 Yantai China State Key Lab. of Intelligent Technology & Systems of Tsinghua University 100084 Beijing China

Real-time path planning is a key subject in the research of a Multi-robot system, such as in soccer robot system, as in such system there are several robots to coordinate to complete one task and its workspace is time-varying. There exist unpredictable changes in such workspace. These changes will make the robots path planning tremendously hard. This paper introduce the study on the real-time planning of a multi-robot system, such as soccer robots system. In such systems, as there exist multiple mobile robots, robots path planning is highly required of real-time and of high path planning quality. In this paper, the definitions of Active Obstacle and Dominating Robot are put forward. Genetic-planning and Fuzzy-correction are developed as our path planning techniques. The concept of Sector Vital Value is proposed for Genetic-planning and Fuzzy-correction. Block-Inserting strategy is employed to further improve the system's real-time performance in the workspace modelization. It takes only 2% of workspace modeling time required by Grid-modeling technique. After taking the proposed measures, the system's real-time performance is improved a lot. The initial simulation shows that our planning strategy can meet real-time demand.

关键词： Multi-agent System Path Planning Fuzzy Logic Genetic algorithms

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Joint probabilistic techniques for tracking objects using multiple visual cues

Joint probabilistic techniques for tracking objects using mu...

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IEEE International Workshop on intelligent robots and Systems (IROS)

作者： C. Rasmussen G.D. Hager Center for Computational Vision & Control Yale University New Haven CT USA

robots relying on vision as a primary sensor frequently need to track common objects such as people, cars, and tools in order to successfully perform autonomous navigation or grasping tasks. These objects may comprise many visual parts and attributes, yet image-based tracking algorithms are often keyed to only one of a target's identifying characteristics. In this paper, we present a framework for sharing information among disparate state estimation processes operating on the same underlying visual object. Well-known techniques for joint probabilistic data association are adapted to yield increased robustness when multiple trackers attuned to different visual cues are deployed simultaneously. We also formulate a measure of tracker confidence, based on distinctiveness and occlusion probability, which permits the deactivation of trackers before erroneous state estimates adversely affect the ensemble. We will discuss experiments using color-region- and snake-based tracking in tandem that demonstrate the efficacy of this approach.

关键词： Target tracking Head State estimation Filters computer vision Robustness Switches Technological innovation Current measurement Computed tomography

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Reducing Computation Requirements for Autonomous Mobile robots on Low Powered Embedded Systems

Reducing Computation Requirements for Autonomous Mobile Robo...

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Modeling, Simulation & intelligent Computing (MoSICom), International Conference on

作者： Abhinav Pathak Aditya Singhal Adit Tewari Aadish Jain V. Kalaichelvi Department of Computer Science Birla Institute of Technology and Science Pilani Dubai UAE Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Dubai UAE Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani

Fully autonomous mobile robots have the potential to revolutionize various industries, from warehouse management to hospital logistics and last-mile deliveries. However, a significant obstacle to achieving reliable autonomy lies in the high computational and energy requirements. In response to this challenge, our paper introduces two innovative algorithms: the Pure Image Segmentation Approach (PISA) and the UNet Based Approach to Semantic Segmentation (UBASS). PISA leverages classical computer vision techniques, offering a fresh perspective on solving crucial tasks such as object detection, object avoidance, and lane detection. In contrast, UBASS harnesses the power of deep learning algorithms for semantic segmentation, unlocking new capabilities in robot perception. Our experiments showcase the effectiveness of these algorithms, demonstrating their accuracy and computational efficiency. Notably, PISA and UBASS outperform or match traditional techniques, including End-to-End Deep Learning and Canny Edge Detection, in terms of both task performance and resource utilization. This research contributes to the advancement of autonomous mobile robotics by offering practical and efficient solutions for navigation and perception challenges. By combining classic and contemporary approaches, we aim to inspire further research in the field, ultimately paving the way for more accessible and dependable autonomous mobile robots.

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Spatio-temporal Fourier transforms for visual discrimination

Spatio-temporal Fourier transforms for visual discrimination

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IEEE Convention of the Electrical and ELectronic Engineers in Israel

作者： N. Mirchina Z. Zacharia Electrical and Electronics Department Center for Technological Education Holon Holon Israel

ISBN: (纸本)0780358422

intelligent pattern recognition techniques have become important in the fields of computer and robot vision. Our interest focuses on detection of dynamical variables which allows robotic visual units to define object evolution from 2D images. We describe a computational algorithm which predicts the real scene images using spatial Fourier frequency transforms multiplied by transforms of dynamic model integro-differential equations. The theoretical results have been simulated by computer numerical image processing and compared to expected real scenes.

关键词： Fourier transforms intelligent robots Layout Computational intelligence Pattern recognition computer vision Robot vision systems Object detection Prediction algorithms Frequency

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