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Geometry Based Object Pose Estimation And Grasp Detection For Industrial Robotic Random Picking Systems Equipped With A 3d Vision Sensor
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Book Synopsis Geometry-based Object Pose Estimation and Grasp Detection for Industrial Robotic Random Picking Systems Equipped with a 3D Vision Sensor by : 王喻民
Download or read book Geometry-based Object Pose Estimation and Grasp Detection for Industrial Robotic Random Picking Systems Equipped with a 3D Vision Sensor written by 王喻民 and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Touch Based Object Pose Estimation for Robotic Grasping by : Joao Bimbo
Download or read book Touch Based Object Pose Estimation for Robotic Grasping written by Joao Bimbo and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot grasping and manipulation require very accurate and timely knowledge of the manipulated object's shape and pose to successfully perform a desired task. One of the main reasons current systems fail to carry out complex tasks in a real, unstructured environment is their inability to accurately determine where in, the object the fingers are touching. Most systems use vision to detect the pose of an object, but the performance of this sensing modality deteriorates as soon as the robot grasps the object. When the robot hand contacts an object, it partially occludes it, which makes it difficult for vision systems to track the object's location. This thesis presents algorithms to use the robot's available tactile sensing to correct the visually determined pose of a grasped object. This method is extended to globally estimate the pose of the object even when no initial estimate is given. Two different tactile sensing strategies have been employed: single-point and distributed, and measurement models for these two strategies are presented. Different optimisation algorithms are developed and tested to minimise the output of these measurement models and find one or more poses that satisfy current tactile measurements. Results show that the method is able to successfully estimate the pose of a grasped object with high accuracy, even for objects with a high degree of geometric complexity. Other applications of the method are proposed, such as determining grasp stability or identifying the grasped object, as well as future research directions.
Book Synopsis 3D Object Pose Estimation in Industrial Context by : Giorgia Pitteri
Download or read book 3D Object Pose Estimation in Industrial Context written by Giorgia Pitteri and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 3D object detection and pose estimation are of primary importance for tasks such as robotic manipulation, augmented reality and they have been the focus of intense research in recent years. Methods relying on depth data acquired by depth cameras are robust. Unfortunately, active depth sensors are power hungry or sometimes it is not possible to use them. It is therefore often desirable to rely on color images. When training machine learning algorithms that aim at estimate object's 6D poses from images, many challenges arise, especially in industrial context that requires handling objects with symmetries and generalizing to unseen objects, i.e. objects never seen by the networks during training.In this thesis, we first analyse the link between the symmetries of a 3D object and its appearance in images. Our analysis explains why symmetrical objects can be a challenge when training machine learning algorithms to predict their 6D pose from images. We then propose an efficient and simple solution that relies on the normalization of the pose rotation. This approach is general and can be used with any 6D pose estimation algorithm.Then, we address the second main challenge: the generalization to unseen objects. Many recent methods for 6D pose estimation are robust and accurate but their success can be attributed to supervised Machine Learning approaches. For each new object, these methods have to be retrained on many different images of this object, which are not always available. Even if domain transfer methods allow for training such methods with synthetic images instead of real ones-at least to some extent-such training sessions take time, and it is highly desirable to avoid them in practice.We propose two methods to handle this problem. The first method relies only on the objects' geometries and focuses on objects with prominent corners, which covers a large number of industrial objects. We first learn to detect object corners of various shapes in images and also to predict their 3D poses, by using training images of a small set of objects. To detect a new object in a given image, we first identify its corners from its CAD model; we also detect the corners visible in the image and predict their 3D poses. We then introduce a RANSAC-like algorithm that robustly and efficiently detects and estimates the object's 3D pose by matching its corners on the CAD model with their detected counterparts in the image.The second method overcomes the limitations of the first one as it does not require objects to have specific corners and the offline selection of the corners on the CAD model. It combines Deep Learning and 3D geometry and relies on an embedding of the local 3D geometry to match the CAD models to the input images. For points at the surface of objects, this embedding can be computed directly from the CAD model; for image locations, we learn to predict it from the image itself. This establishes correspondences between 3D points on the CAD model and 2D locations of the input images. However, many of these correspondences are ambiguous as many points may have similar local geometries. We also show that we can use Mask-RCNN in a class-agnostic way to detect the new objects without retraining and thus drastically limit the number of possible correspondences. We can then robustly estimate a 3D pose from these discriminative correspondences using a RANSAC-like algorithm.
Book Synopsis Manufacturing Systems: Theory and Practice by : George Chryssolouris
Download or read book Manufacturing Systems: Theory and Practice written by George Chryssolouris and published by Springer Science & Business Media. This book was released on 2006-02-28 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: Overviews manufacturing systems from the ground up, following the same concept as in the first edition. Delves into the fundamental building blocks of manufacturing systems: manufacturing processes and equipment. Discusses all topics from the viewpoint of four fundamental manufacturing attributes: cost, rate, flexibility and quality.
Book Synopsis Towards Robust Object Detection and Pose Estimation As a Service for Manufacturing Lndustries by : Martin Rudorfer
Download or read book Towards Robust Object Detection and Pose Estimation As a Service for Manufacturing Lndustries written by Martin Rudorfer and published by Fraunhofer Verlag. This book was released on 2021-11-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The trend towards high-mix and low-volume production demands flexible and reconfigurable control for assembly systems. In less-structured environments, object detection and pose estimation is a key capability to enable industrial robotics applications such as grasping, handling and assembling. The integration and interconnectivity of such automation functions is fostered by Industry 4.0 through the adoption of service-based ecosystems. The main objective of this thesis is to create a service-based framework for object detection and pose estimation in manufacturing environments. This could be a viable alternative to traditional machine vision systems such as smart cameras and embedded PCs, which are challenged by the high diversity and fast-paced progress in the field of object detection and pose estimation. We approach this problem in three steps: First, by designing a service-based framework that allows to handle all methods uniformly. Second, by examining the integration of three exemplary object detection and pose estimation methods, and third, by characterizing the strengths and weaknesses of the proposed solution compared to traditional machine vision systems.
Book Synopsis Aerial Manipulation by : Matko Orsag
Download or read book Aerial Manipulation written by Matko Orsag and published by Springer. This book was released on 2017-09-19 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is a thorough treatment of the rapidly growing area of aerial manipulation. It details all the design steps required for the modeling and control of unmanned aerial vehicles (UAV) equipped with robotic manipulators. Starting with the physical basics of rigid-body kinematics, the book gives an in-depth presentation of local and global coordinates, together with the representation of orientation and motion in fixed- and moving-coordinate systems. Coverage of the kinematics and dynamics of unmanned aerial vehicles is developed in a succession of popular UAV configurations for multirotor systems. Such an arrangement, supported by frequent examples and end-of-chapter exercises, leads the reader from simple to more complex UAV configurations. Propulsion-system aerodynamics, essential in UAV design, is analyzed through blade-element and momentum theories, analysis which is followed by a description of drag and ground-aerodynamic effects. The central part of the book is dedicated to aerial-manipulator kinematics, dynamics, and control. Based on foundations laid in the opening chapters, this portion of the book is a structured presentation of Newton–Euler dynamic modeling that results in forward and backward equations in both fixed- and moving-coordinate systems. The Lagrange–Euler approach is applied to expand the model further, providing formalisms to model the variable moment of inertia later used to analyze the dynamics of aerial manipulators in contact with the environment. Using knowledge from sensor data, insights are presented into the ways in which linear, robust, and adaptive control techniques can be applied in aerial manipulation so as to tackle the real-world problems faced by scholars and engineers in the design and implementation of aerial robotics systems. The book is completed by path and trajectory planning with vision-based examples for tracking and manipulation.
Book Synopsis Robot Manipulators by : Agustin Jimenez
Download or read book Robot Manipulators written by Agustin Jimenez and published by BoD – Books on Demand. This book was released on 2010-03-01 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent research advances in robot manipulators. It offers a complete survey to the kinematic and dynamic modelling, simulation, computer vision, software engineering, optimization and design of control algorithms applied for robotic systems. It is devoted for a large scale of applications, such as manufacturing, manipulation, medicine and automation. Several control methods are included such as optimal, adaptive, robust, force, fuzzy and neural network control strategies. The trajectory planning is discussed in details for point-to-point and path motions control. The results in obtained in this book are expected to be of great interest for researchers, engineers, scientists and students, in engineering studies and industrial sectors related to robot modelling, design, control, and application. The book also details theoretical, mathematical and practical requirements for mathematicians and control engineers. It surveys recent techniques in modelling, computer simulation and implementation of advanced and intelligent controllers.
Book Synopsis Monocular Model-based 3D Tracking of Rigid Objects by : Vincent Lepetit
Download or read book Monocular Model-based 3D Tracking of Rigid Objects written by Vincent Lepetit and published by Now Publishers Inc. This book was released on 2005 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monocular Model-Based 3D Tracking of Rigid Objects reviews the different techniques and approaches that have been developed by industry and research.
Book Synopsis Action for Perception by : Kanzhi Wu
Download or read book Action for Perception written by Kanzhi Wu and published by . This book was released on 2017 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object recognition and localisation are indispensable competency for service robots in everyday environments like offices and kitchens. Presence of similar objects that can only be differentiated from a small part of the surface together with clutter that leads to occlusions make it impossible to detect target objects accurately and reliably from a single observation. When the sensor observing the environment is mounted on a mobile platform, object detection and pose estimation can be facilitated by observing the environment from a series of different viewpoints. Computing Active perception strategies, with the aim of finding optimal actions to enhance object recognition and pose estimation performance is the focus of this thesis. This thesis consists of two main parts: In the first part, it focuses on object detection and pose estimation from a single frame of observation. Using an RGB-D sensor, we propose a modular 3D textured object detection and pose estimation framework which can recognise object under cluttered environment by taking advantage of the geometric information provided from the sensor. To handle less-textured objects and objects under severe illumination conditions, we propose a novel RGB-D feature which is robust to illumination, scale, rotation and viewpoint variations, and provides reliable feature matching results under challenging conditions. The proposed feature is validated for multiple applications including object detection and point cloud alignment. Parts of the above approaches are integrated with existing work to produce a practical and effective perception module for a warehouse automation task. The designed perception system can detect objects of different types and estimate their poses robustly thus guaranteeing a reliable object grasping and manipulation performances. In the second part of the thesis, we investigate the problem of active object detection and pose estimation from two perspectives: with and without considering the uncertainties in the motion model and the observation model. First, we propose a model-driven active object recognition and pose estimation system via exploiting the feature association probability under scale and viewpoint variations. By explicitly modelling the feature association, the proposed system can predict future information more accurately thus laying the foundation of a successful active Next-Best-View planning system even with a naive greedy search technique. We also present a probabilistic framework which handles motion and observation uncertainties in the active object detection and pose estimation problem. We present an optimisation framework which computes the optimal control at each step, using an objective function which incorporates uncertainties in state estimation, feature coverage for better recognition confidence and control consumption. The proposed framework can handle various issues such as object initialisation, collision avoidance, occlusion and changing the object hypothesis. Validations based on a simulation environment are also presented.
Book Synopsis An Object Recognition and Pose Estimation Library for Intelligent Industrial Automation by : Adam David Allevato
Download or read book An Object Recognition and Pose Estimation Library for Intelligent Industrial Automation written by Adam David Allevato and published by . This book was released on 2016 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: The nuclear-industrial complex is a field characterized by hazardous environments and stringent worker health regulations. Automation is one of the best ways to improve worker health, but many of the work-intensive tasks in the nuclear industry are difficult to automate using rigid industrial manipulators, which are often treated as glorified assembly lines. This thesis presents the idea of intelligent industrial automation, or IIA, as a way to implement automation in diverse and uncertain environments, and shows that robust computer vision is a key technology in achieving deployable IIA. Furthermore, with recent advances in the field of computer vision, including machine-learning based techniques, the time is better than ever for groups such as the Department of Energy (DOE) to implement computer vision and IIA in their processes. A modular software framework for object recognition and pose estimation (ORP) is developed and incorporated into three laboratory demonstrations, each of which represents a different capability relevant to DOE. By using well-proven computer vision techniques and libraries, ORP enables robust task completion in domains that would have previously been impossible without human supervision or custom mechanical designs (such as task-specific fixtures). A vision-enabled manipulation system is shown to reliably pick and place small weapon detonator components 98% of the time, making it an ideal candidate for machine tending. A remote inspection and inventory system shows the ability to visually detect the position of nuclear material storage canisters with a standard deviation under 1 mm, allowing it to detect cans that have been moved or tampered with. Finally, using vision, an automated glovebox mixed-waste sorting system is able to sort small objects, which begin in a random configuration, into three containers based on their color (a surrogate for radiation signature) with 94.6% accuracy. All three demonstrations proceed autonomously, suggesting that implementing IIA can result in significant improvements in worker safety and productivity at DOE complex sites.
Book Synopsis Scene Reconstruction for Simulated Grasp Search in Structured Clutter by : Runlin Guo
Download or read book Scene Reconstruction for Simulated Grasp Search in Structured Clutter written by Runlin Guo and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotic grasping in a complex environment is one of the fundamental challenges for home-assistant robots. Complex environment grasping has been extensively studied in industrial bin-picking scenarios, where reliably grasping objects from unorganized heaps is challenging due to sensor noise, obstructions, and occlusions. However, bin picking is still relatively easier than grasping common household objects from a structured clutter in a home environment because the robot cannot knock over neighboring objects during the grasping motion. Recently, there have been several attempts to tackle the grasping-in-structured-clutter problem. In our experiments, we found these methods either hard to adapt to our simulated environment without extra tuning or generate too few stable grasps to successfully grasp the objects. The overviews and detailed analyses of these existing grasping approaches will appear in the first half of this thesis. In the second half of this thesis, we investigate the idea of using a physical simulator as an intermediate step to generate a grasp trajectory proposal. At a high level, we propose a two-step approach to solve the grasping-in-structured-clutter problem. First, we collect RGB-D observations to reconstruct the environment in a physical simulator via 9 degree-of-freedom (DoF) category-level object pose estimation, CAD model matching, and physical support refinement. Then, we perform antipodal grasp sampling, collision-free motion planning, and grasp execution in the simulator and directly transfer the robot arm's motion trajectory to the original environment. To generate a 9-DoF category-level object pose estimate, we extend a state-of-the-art 6-DoF instance-level object pose estimation network. In our experiments, we found the 9-DoF pose estimation network can reach performance comparable to the state-of-the-art on a category-level object pose estimation dataset. Relying on only the top-down view of the environment, we reconstructed the environment using the proposed two-step approach and evaluated the grasp transfer success. The results show further room for improvements in the model matching process. Future directions and some ideas will be discussed towards the end of this thesis. We hope the work of scene reconstruction for simulated grasp search and trajectory transfer will help future research of robotics manipulation in complex environments.
Book Synopsis Touch Based Object Pose Estimation for Robotic Grasping by : Joao Maria Bimbo
Download or read book Touch Based Object Pose Estimation for Robotic Grasping written by Joao Maria Bimbo and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Three-dimensional Hand Tracking and Surface-geometry Measurement for a Robot-vision System by : Chris Yu-Liang Liu
Download or read book Three-dimensional Hand Tracking and Surface-geometry Measurement for a Robot-vision System written by Chris Yu-Liang Liu and published by . This book was released on 2008 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tracking of human motion and object identification and recognition are important in many applications including motion capture for human-machine interaction systems. This research is part of a global project to enable a service robot to recognize new objects and perform different object-related tasks based on task guidance and demonstration provided by a general user. This research consists of the calibration and testing of two vision systems which are part of a robot-vision system.
Book Synopsis Vision-based Robotic Grasping from Real-time 6-DoF Deep Object Pose Estimation to Deep Grasp Estimation for Parallel Grippers by : Nico Leuze
Download or read book Vision-based Robotic Grasping from Real-time 6-DoF Deep Object Pose Estimation to Deep Grasp Estimation for Parallel Grippers written by Nico Leuze and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Shape Primitive-based Grasping Strategy Using Visual Object Recognition in Confined, Hazardous Environments by : Cheryl Lynn Brabec
Download or read book A Shape Primitive-based Grasping Strategy Using Visual Object Recognition in Confined, Hazardous Environments written by Cheryl Lynn Brabec and published by . This book was released on 2013 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grasping can be a complicated process for robotics due to the replication of human fine motor skills and typically high degrees of freedom in robotic hands. Robotic hands that are underactuated provide a method by which grasps can be executed without the onerous task of calculating every fingertip placement. The general shape configuration modes available to underactuated hands lend themselves well to an approach of grasping by shape primitives, and especially so when applied to gloveboxes in the nuclear domain due to the finite number of objects anticipated and the safe assumption that objects in the set are rigid. Thus, the object set found in a glovebox can be categorized as a small set of primitives such as cylinders, cubes, and bowls/hemispheres, etc. These same assumptions can also be leveraged for reliable identification and pose estimation within a glovebox. This effort develops and simulates a simple, but robust and effective grasp planning algorithm for a 7DOF industrial robot and three fingered dexterous, but underactuated robotic hand. The proposed grasping algorithm creates a grasp by generating a vector to the object from the base of the robot and manipulating that vector to be in a suitable starting location for a grasp. The grasp preshapes are selected to match shape primitives and are built-in to the Robotiq gripper used for algorithm demonstration purposes. If a grasp is found to be unsuitable via an inverse kinematics solution check, the algorithm procedurally generates additional grasps to try based on object geometry until a solution can be found or all possibilities are exhausted. The algorithm was tested and found capable of generating valid grasps for visually identified objects, and can recalculate grasps if one is found to be incompatible with the current kinematics of the robotic arm.
Book Synopsis Estimation and Inference for Grasping and Manipulation Tasks Using Vision and Kinesthetic Sensors by : Paul Hebert
Download or read book Estimation and Inference for Grasping and Manipulation Tasks Using Vision and Kinesthetic Sensors written by Paul Hebert and published by . This book was released on 2013 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a novel framework for state estimation in the context of robotic grasping and manipulation. The overall estimation approach is based on fusing various visual cues for manipulator tracking, namely appearance and feature-based, shape-based, and silhouette-based visual cues. Similarly, a framework is developed to fuse the above visual cues, but also kinesthetic cues such as force-torque and tactile measurements, for in-hand object pose estimation. The cues are extracted from multiple sensor modalities and are fused in a variety of Kalman filters. A hybrid estimator is developed to estimate both a continuous state (robot and object states) and discrete states, called contact modes, which specify how each finger contacts a particular object surface. A static multiple model estimator is used to compute and maintain this mode probability. The thesis also develops an estimation framework for estimating model parameters associated with object grasping. Dual and joint state-parameter estimation is explored for parameter estimation of a grasped object's mass and center of mass. Experimental results demonstrate simultaneous object localization and center of mass estimation. Dual-arm estimation is developed for two arm robotic manipulation tasks. Two types of filters are explored; the first is an augmented filter that contains both arms in the state vector while the second runs two filters in parallel, one for each arm. These two frameworks and their performance is compared in a dual-arm task of removing a wheel from a hub. This thesis also presents a new method for action selection involving touch. This next best touch method selects an available action for interacting with an object that will gain the most information. The algorithm employs information theory to compute an information gain metric that is based on a probabilistic belief suitable for the task. An estimation framework is used to maintain this belief over time. Kinesthetic measurements such as contact and tactile measurements are used to update the state belief after every interactive action. Simulation and experimental results are demonstrated using next best touch for object localization, specifically a door handle on a door. The next best touch theory is extended for model parameter determination. Since many objects within a particular object category share the same rough shape, principle component analysis may be used to parametrize the object mesh models. These parameters can be estimated using the action selection technique that selects the touching action which best both localizes and estimates these parameters. Simulation results are then presented involving localizing and determining a parameter of a screwdriver. Lastly, the next best touch theory is further extended to model classes. Instead of estimating parameters, object class determination is incorporated into the information gain metric calculation. The best touching action is selected in order to best discern between the possible model classes. Simulation results are presented to validate the theory.
Book Synopsis Grasping in Robotics by : Giuseppe Carbone
Download or read book Grasping in Robotics written by Giuseppe Carbone and published by Springer Science & Business Media. This book was released on 2012-11-15 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grasping in Robotics contains original contributions in the field of grasping in robotics with a broad multidisciplinary approach. This gives the possibility of addressing all the major issues related to robotized grasping, including milestones in grasping through the centuries, mechanical design issues, control issues, modelling achievements and issues, formulations and software for simulation purposes, sensors and vision integration, applications in industrial field and non-conventional applications (including service robotics and agriculture). The contributors to this book are experts in their own diverse and wide ranging fields. This multidisciplinary approach can help make Grasping in Robotics of interest to a very wide audience. In particular, it can be a useful reference book for researchers, students and users in the wide field of grasping in robotics from many different disciplines including mechanical design, hardware design, control design, user interfaces, modelling, simulation, sensors and humanoid robotics. It could even be adopted as a reference textbook in specific PhD courses.
