Robotic Ball Catching with an Eye-in-Hand Single-Camera System

ADS40 is the first commercial airborne pushbroom sensor system produced by LH Systems, which offers on a single camera system the possibility to acquire both 3-line panchromatic and multispectral images in up to 10 channels. The geometric configuration and characteristics of the sensor enable the development of a matching algorithm, based on a multi-image philosophy, incorporating novel strategies, primarily aiming at producing reliable DSMs/DTMs. Existing algorithms are geared towards frame imagery and do not make explicit use of the special characteristics of the sensor; thus, most of them show a low success rate, requiring additional manual editing. Multi-image constraints make use of quasi-epipolar lines, since each line has its own position and attitude, to restrict search space. A multi-patch approach, utilized in the algorithm, strengthens the identification of mismatches and can be justified since larger patches aim at reliability of a coarse solution and the smaller ones at accuracy. Additional quality measures, computed at run time, are combined in the stage of the quality control in order to assign each matched point to a possible pre-defined error class (e.g. occlusion, multiple solutions). The evaluation of the matching performance is done through a qualitative and quantitative analysis using manually measured mass points from a block of frame aerial images with the same ground sampling distance (0.22 m) as ADS40 data.

Current vision systems are designed to perform in normal weather condition. However, no one can escape from severe weather conditions. Bad weather reduces scene contrast and visibility, which results in degradation in the performance of various computer vision algorithms such as object tracking, segmentation and recognition. Thus, current vision systems must include some mechanisms that enable them to perform up to the mark in bad weather conditions such as rain and fog. Rain causes the spatial and temporal intensity variations in images or video frames. These intensity changes are due to the random distribution and high velocities of the raindrops. Fog causes low contrast and whiteness in the image and leads to a shift in the color. This book has studied rain and fog from the perspective of vision. The book has two main goals: 1) removal of rain from videos captured by a moving and static camera, 2) removal of the fog from images and videos captured by a moving single uncalibrated camera system. The book begins with a literature survey. Pros and cons of the selected prior art algorithms are described, and a general framework for the development of an efficient rain removal algorithm is explored. Temporal and spatiotemporal properties of rain pixels are analyzed and using these properties, two rain removal algorithms for the videos captured by a static camera are developed. For the removal of rain, temporal and spatiotemporal algorithms require fewer numbers of consecutive frames which reduces buffer size and delay. These algorithms do not assume the shape, size and velocity of raindrops which make it robust to different rain conditions (i.e., heavy rain, light rain and moderate rain). In a practical situation, there is no ground truth available for rain video. Thus, no reference quality metric is very useful in measuring the efficacy of the rain removal algorithms. Temporal variance and spatiotemporal variance are presented in this book as no reference quality metrics. An efficient rain removal algorithm using meteorological properties of rain is developed. The relation among the orientation of the raindrops, wind velocity and terminal velocity is established. This relation is used in the estimation of shape-based features of the raindrop. Meteorological property-based features helped to discriminate the rain and non-rain pixels. Most of the prior art algorithms are designed for the videos captured by a static camera. The use of global motion compensation with all rain removal algorithms designed for videos captured by static camera results in better accuracy for videos captured by moving camera. Qualitative and quantitative results confirm that probabilistic temporal, spatiotemporal and meteorological algorithms outperformed other prior art algorithms in terms of the perceptual quality, buffer size, execution delay and system cost. The work presented in this book can find wide application in entertainment industries, transportation, tracking and consumer electronics. Table of Contents: Acknowledgments / Introduction / Analysis of Rain / Dataset and Performance Metrics / Important Rain Detection Algorithms / Probabilistic Approach for Detection and Removal of Rain / Impact of Camera Motion on Detection of Rain / Meteorological Approach for Detection and Removal of Rain from Videos / Conclusion and Scope of Future Work / Bibliography / Authors' Biographies

Quantum associative memory with linear and non-linear algorithms for the diagnosis of some tropical diseases

Many researches related to Future Internet are ongoing to overcome the limitations of current Internet such as security, mobility, QoS, content explosion, etc. In order to successfully research innovative architectures about Future Internet, it is very important for us to go on researching various technologies for opened testbeds as well as architectural researches, protocols and services. Recently, researches to dynamically experiment functions about data plane and control plane are being progressed in ETRI. In this paper, we present FIRSTProNET, which is a unified control framework to control virtualized programmable platforms including FIRST@ATCA of ETRI.

Advancements in central control for IFMIF-DONES: Integrating CODAC, MPS, and SCS into a unified control framework

Motor channelling is a concept to provide navigation and sensory feedback to operators in master-slave surgical setups. It is beneficial since the introduction of robotic surgery creates a physical separation between the surgeon and patient anatomy. Active Constraints/Virtual Fixtures are proposed which integrate Guidance and Forbidden Region Constraints into a unified control framework. The developed approach provides guidance and safe manipulation to improve precision and reduce the risk of inadvertent tissue damage. Online three-degree-of-freedom motion prediction and compensation of the target anatomy is performed to complement the master constraints. The presented Active Constraints concept is applied to two clinical scenarios; surface scanning for in situ medical imaging and vessel manipulation in cardiac surgery. The proposed motor channelling control strategy is implemented on the da Vinci Surgical System using the da Vinci Research Kit (dVRK) and its effectiveness is demonstrated through a detailed user study.

The Growing Curvilinear Component Analysis (GCCA) neural network

This paper presents a single camera and laser system dedicated to the realtime estimation of attitude and altitude for unmanned aerial vehicles (UAV) under low illumination conditions to dark environments. The fisheye camera allows to cover a large field of view (FOV). The approach, close to structured light systems, uses the geometrical information obtained by the projection of a laser circle onto the ground plane and perceived by the camera. We propose some experiments based on simulated data and real sequences. The results show good agreement with the ground truth values from the commercial sensors in terms of its accuracy and correctness. The results also prove its suitability for autonomous take-off and landing as well as for the case of low altitude manoeuvre in dark, GPS signal deficient unknown environments with no prebuilt map. It also provides room for additional payload to be used for different applications due to it being inexpensive and use of light weight micro-camera and laser system.

Toward Intelligent Digital Twins for PV Systems: A Unified Framework for Control, Forecasting, and Grid Integration

We take a well log in petroleum exploration and development as an example of the big data mining, and adopt three regression and two classification algorithms: the multiple regression analysis (MRA), the error back-propagation neural network (BPNN), the regression of support vector machine ( R -SVM), the classification of support vector machine ( C -SVM), and the Bayesian successive discrimination (BAYSD). It is well known that MRA, BPNN and R -SVM are regression algorithms while C -SVM and BAYSD are classification algorithms, and only MRA is linear algorithm whereas the other four algorithms are nonlinear algorithms. From this case study, we can draw the following five major conclusions: a) Since C -SVM is the best classifier, it is employed as a data cleaning tool. b) Since MRA is a linear algorithm, its total mean absolute relative residual R * (%) can express the nonlinearity of studied problem. For this case study, R * (%)=52.14 showing the nonlinearity of the studied problem is strong. c) Since both MRA and BAYD can establish the order of dependence between a dependent variable and independent variables, each of MRA and BAYD could serve as a pioneering dimension-reduction tool in data mining. In the case study, since the nonlinearity of the studied problem is strong, the nonlinear algorithm BAYSD can serve as a pioneering dimension-reduction tool, but the linear algorithm MRA cannot. d) Since the nonlinearity of the case study is strong, BPNN and R -SVM are not applicable though they are nonlinear algorithms, whereas other two nonlinear algorithms C -SVM and BAYSD are applicable, indicating the nonlinear ability of C -SVM and BAYSD is higher than that of BPNN and R -SVM. e) Comparing the two applicable algorithms C -SVM and BAYSD for this case study, it is seen that R * (%) of C -SVM is less than that of BAYSD; BAYSD can serve as a pioneering dimension-reduction tool, but C -SVM cannot; it is easy to code the BAYSD program whereas it is very complicated to code the C -SVM program, so BAYSD is a good software for this case study when C -SVM is not available. Key words : Big data mining; Well log; Data cleaning; Dimension-reduction; Regression; Classification

Integral admittance shaping: A unified framework for active exoskeleton control

We quantify the accuracy of various simulators compared to a real world robotic reaching and interaction task. Simulators are used in robotics to design solutions for real world hardware without the need for physical access. The `reality gap' prevents solutions developed or learnt in simulation from performing well, or at all, when transferred to real-world hardware. Making use of a Kinova robotic manipulator and a motion capture system, we record a ground truth enabling comparisons with various simulators, and present quantitative data for various manipulation-oriented robotic tasks. We show the relative strengths and weaknesses of numerous contemporary simulators, highlighting areas of significant discrepancy, and assisting researchers in the field in their selection of appropriate simulators for their use cases.

To improve the accuracy of signal recognition in delay-based optical reservoir computing (RC) systems, this paper proposes the use of nonlinear algorithms at the output layer to replace traditional linear algorithms for training and testing datasets and apply them to the identification of frequency-modulated continuous wave (FMCW) LiDAR signals. This marks the inaugural use of the system for the identification of FMCW LiDAR signals. We elaborate on the fundamental principles of a delay-based optical RC system using an optical-injected distributed feedback laser (DFB) laser and discriminate four FMCW LiDAR signals through this setup. In the output layer, three distinct training algorithms—namely linear regression, support vector machine (SVM), and random forest—were employed to train the optical reservoir. Upon analyzing the experimental results, it was found that regardless of the size of the dataset, the recognition accuracy of the two nonlinear training algorithms was superior to that of the linear regression algorithm. Among the two nonlinear algorithms, the Random Forest algorithm had a higher recognition accuracy than SVM when the sample size was relatively small.

In robotic assembly processes, detecting target pose is indispensable for robot manipulation algorithms. Such a tedious work hinders the further application of robot manipulation algorithms to real industrial processes. In this paper, a vision-based position/impedance control framework is proposed. Therein, the objective pose is firstly obtained by a motion capture system, where a kinematic equation is solved to Figure out the target assembly pose. Next the robot arm motion is approximated by an impedance model, in this way, the position/impedance control law of the robot is derived, which drives the robot to perform a compliant assembly manipulation. Finally, a memory stick assembly experiment is conducted on a 6-DOF robot arm equipped with a motion capture system and a force-torque sensor. Experiment shows the efficiency of the vision-based position/impedance hybrid controller in terms of autonomous positioning and compliant assembly. Due to its convenience and generality, the present controller can be expected to apply to more general industrial scenarios.

We propose a probabilistic filtering method which fuses joint measurements with depth images to yield a precise, real-time estimate of the end-effector pose in the camera frame. This avoids the need for frame transformations when using it in combination with visual object tracking methods. Precision is achieved by modeling and correcting biases in the joint measurements as well as inaccuracies in the robot model, such as poor extrinsic camera calibration. We make our method computationally efficient through a principled combination of Kalman filtering of the joint measurements and asynchronous depth-image updates based on the Coordinate Particle Filter. We quantitatively evaluate our approach on a dataset recorded from a real robotic platform, annotated with ground truth from a motion capture system. We show that our method is robust and accurate even under challenging conditions such as fast motion, significant and long-term occlusions, and time-varying biases. We release the dataset along with open-source code of our method to allow quantitative comparison with alternative approaches.