The periodic QZ algorithm involved in the structure-preserving skew-Hamiltonian/Hamiltonian algorithm is investigated. These are key algorithms for many applications in diverse theoretical and practical domains such a...
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ISBN:
(纸本)9789897583803
The periodic QZ algorithm involved in the structure-preserving skew-Hamiltonian/Hamiltonian algorithm is investigated. These are key algorithms for many applications in diverse theoretical and practical domains such as periodic systems, (robust) optimal control, and characterization of dynamical systems. Although in use for several years, few examples of skew-Hamiltonian/Hamiltonian eigenproblems have been discovered for which the periodic QZ algorithm either did not converge or required too many iterations to reach the solution. This paper investigates this rare bad convergence behavior and proposes some modifications of the periodic QZ and skew-Hamiltonian/Hamiltonian solvers to avoid nonconvergence failures and improve the convergence speed. The results obtained on a generated set of one million skew-Hamiltonian/Hamiltonian eigenproblems of order 80 show no failures and a significant reduction (sometimes of over 240 times) of the number of iterations.
In this research, we focus on the 6D pose estimation of known objects from the RGB image. In contrast to state of the art methods, which are based on the end-to-end neural network training, we proposed a hybrid approa...
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ISBN:
(纸本)9789897583803
In this research, we focus on the 6D pose estimation of known objects from the RGB image. In contrast to state of the art methods, which are based on the end-to-end neural network training, we proposed a hybrid approach. We use separate deep neural networks to: detect the object on the image, estimate the center of the object, and estimate the translation and "in-place" rotation of the object. Then, we use geometrical relations on the image and the camera model to recover the full 6D object pose. As a result, we avoid the direct estimation of the object orientation defined in SO3 using a neural network. We propose the 4D-NET neural network to estimate translation and "in-place" rotation of the object. Finally, we show results on the images generated from the Pascal VOC and ShapeNet datasets.
This article introduces the concept of cell formation and scheduling in robotics coordinated Dynamic Cellular Manufacturing System (DCMS) for industry 4.0. Automated reconfigurability and flexibility in cellular Manuf...
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ISBN:
(纸本)9781728130583
This article introduces the concept of cell formation and scheduling in robotics coordinated Dynamic Cellular Manufacturing System (DCMS) for industry 4.0. Automated reconfigurability and flexibility in cellular Manufacturing are based on variations in product demands, deletion or addition of products and varying product mix. To design DCMS for Industry 4.0, this article proposes a conceptual model for an automated production environment in which no human factor involves receiving an order to its delivery. Computer Embedded System will analyze data from the manufacturing environment and make smart decisions to accept and complete the orders. The position and movement of all components will be signaled from the central ERP system. Cell formation will be automated by using Multi-Function robotics with self-transportation capabilities and Advanced Automatic Guided Vehicles (AGV) will be reconfigured and reschedule, whenever the new model added. Cell reformulation will provide a foundation for parts rescheduling. So, the DCMS will improve responsiveness with higher quality and lower cost.
The proceedings contain 170 papers. The topics discussed include: evolving systems and their automotive applications;fault training matrix for process monitoring based on structured residuals;a PGD-based method for ro...
ISBN:
(纸本)9789897583803
The proceedings contain 170 papers. The topics discussed include: evolving systems and their automotive applications;fault training matrix for process monitoring based on structured residuals;a PGD-based method for robot global path planning: a primer;generation of complex data for ai-based predictive maintenance research with a physical factory model;exploiting physical contacts for robustness improvement of a dot-painting mission by a micro air vehicle;on some open-ended challenges in model-based fault management for aerospace systems: a look backwards and forwards;camera and lidar cooperation for 3D feature extraction;balancing control of a self-driving bicycle;and singularity analysis for redundant manipulators of arbitrary kinematic structure.
The proceedings contain 170 papers. The topics discussed include: evolving systems and their automotive applications;fault training matrix for process monitoring based on structured residuals;a PGD-based method for ro...
ISBN:
(纸本)9789897583803
The proceedings contain 170 papers. The topics discussed include: evolving systems and their automotive applications;fault training matrix for process monitoring based on structured residuals;a PGD-based method for robot global path planning: a primer;generation of complex data for ai-based predictive maintenance research with a physical factory model;exploiting physical contacts for robustness improvement of a dot-painting mission by a micro air vehicle;on some open-ended challenges in model-based fault management for aerospace systems: a look backwards and forwards;camera and lidar cooperation for 3D feature extraction;balancing control of a self-driving bicycle;and singularity analysis for redundant manipulators of arbitrary kinematic structure.
Information technology plays an important role in the business world because it may affect the operations of the organization within the organization that also uses information technology to provide information. One o...
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In robot manipulator control, grasping different types of objects is an important task, but despite being a subject of many studies, there is still no universal approach. A humanoid robot arm end-effector has a signif...
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ISBN:
(纸本)9789897583803
In robot manipulator control, grasping different types of objects is an important task, but despite being a subject of many studies, there is still no universal approach. A humanoid robot arm end-effector has a significantly more complicated structure than the one of an industrial manipulator. It complicates a process of object grasping, but could possibly make it more robust and stable. A success of grasping strongly depends on a method of determining an object shape and a manipulator grasping procedure. Combining these factors turns object grasping by a humanoid into an interesting and versatile control problem. This paper presents a grasping algorithm for AR-601M humanoid arm with mimic joints in the hand that utilizes the simplicity of an antipodal grasp and satisfies force closure condition. The algorithm was tested in Gazebo simulation with sample objects that were modeled after selected household items.
Many advances and algorithms have been proposed to obtain complete Lyapunov functions for dynamical systems and to properly describe the chain-recurrent set, e.g. periodic orbits. Recently, a heuristic algorithm was p...
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ISBN:
(纸本)9789897583803
Many advances and algorithms have been proposed to obtain complete Lyapunov functions for dynamical systems and to properly describe the chain-recurrent set, e.g. periodic orbits. Recently, a heuristic algorithm was proposed to classify and reduce the over-estimation of different periodic orbits in the chain-recurrent set, provided they are circular. This was done to investigate the effect on further iterations of the algorithm to compute approximations to a complete Lyapunov function. In this paper, we propose an algorithm that classifies the different connected components of the chain-recurrent set for general systems, not restricted to (circular) periodic orbits. The algorithm is based on identifying clustering of points and is independent of the particular algorithm to construct the complete Lyapunov functions.
Estimating camera pose is a significant process, which assures the success of the 3D modeling performance. This research presents a camera pose estimation using convolutional neural network (CNN) to transfer learning ...
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ISBN:
(纸本)9781450388023
Estimating camera pose is a significant process, which assures the success of the 3D modeling performance. This research presents a camera pose estimation using convolutional neural network (CNN) to transfer learning from pre-trained deep learning VGG19 model in order to extract features from a single image using several datasets captured in indoor and outdoor environments with diverse perspectives and photographic styles. Due to the large dimensions of the extracted features, Latent Semantic Analysis (LSA) are introduced prior to the CNN input. Then, the CNN is trained to predict the camera views and translations. The prediction performance is measured in terms of average mean square errors and compared to the reference techniques. As a result, the regression estimation of the proposed CNN model outperforms the others with average 0.24 degrees rotation error and 0.26 m. translation errors.
This paper is devoted to the rotor angular velocity estimation of the permanent-magnet synchronous motor (PMSM). It is an actual problem, for example, in sensorless control. We consider a classical, two-phase model in...
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