The manipulator workspace mapping is an important problem in robotics and has attracted significant attention in the community. However, most of the pre-existing algorithms have expensive time complexity due to the reliance on sophisticated kinematic equations. To solve this problem, this paper introduces subspace learning (SL), a variant of subspace embedding, where a set of robot and scope parameters is mapped to the corresponding workspace by a deep neural network (DNN). Trained on a large dataset of around 6 × 104 samples obtained from a MATLAB®implementation of a classical method and sampling of designed uniform distributions, the experiments demonstrate that the embedding significantly reduces run-time from 5.23 × 103 s of traditional discretization method to 0.224 s, with high accuracies (average F-measure is 0.9665 with batch gradient descent and resilient backpropagation).1MATLAB® Scripts are available at https: github.com liaopeiyuan Subspace-Learning
In the literature, the development of a neural network for workspace generation has been already tested on both serial-link and parallel mechanisms, utilizing several model architectures and optimization methods @cite_20 @cite_24 . The feasibility of using an artificial neural network for workspace analysis is first proposed and investigated by @cite_19 , where a two-layer perceptron learns to generate the orientation workspace of a 6-3 SPM parallel mechanism based on a 3-tuple input. In a similar approach, @cite_21 constructs a three-layer deep network that takes the input joint angles of a 2-DOF parallel manipulator and gives the workspace of the mechanism in a particular Cartesian plane. Levenberg-Marquardt method is used for optimization, and the lowest mean squared error obtained by the algorithm is 0.026.
@cite_24 @cite_19 @cite_21 @cite_20
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Purpose – This paper aims to combine and further develop different mathematical models of the workspace representation of 6 degrees of freedom parallel mechanisms and to bring a new point of view to existing workspace analysis methods through using neural networks (NN).Design methodology approach – For the orientation workspace of the 6‐3 SPM, discretization method is used which is based on Euler angles and the NN algorithm is applied.Findings – The workspace analysis is carried out in the direction perpendicular to the moving platform which is the most workable direction of 6‐3 Stewart platform mechanisms and NN algorithm has decreased processing time.Originality value – The determination of the point, on that direction, at which the workspace is maximum, is outlined. It is the first time that the NN is used for classification of workspace of a parallel manipulator. The main purpose of the paper is to develop a neural network application destined to the workspace generation of a parallel mechanism, as an performant alternative to the workspace representation based on inverse kinematic model. The paper describes both algorithms. The initial testing was made for a parallel mechanism with two degrees of freedom that could be applied for the orientation of different systems like a TV satellite dish antennas, sun trackers, telescopes, cameras, radars etc. We present a new mechanical design for a compact three degrees-of-freedom joint mechanism. This joint design is ideal for use in robotic devices, especially hyper-redundant or snake-like robots. This joint exhibits an unprecedented range of motion: the origin of a coordinate frame attached to the output link of the joint can move on a spherical cone whose apex angle is 120° and this coordinate frame can be rotated around an axis passing through its origin and the center of the sphere. Moreover, the joint is optimized for strength, compactness and accuracy. These characteristics are verified by exploring the magnitude of the wrenches and errors that the joint can produce in its effective workspace. Finally, we present a mechatronic integration of four of our joints to construct a 12 degrees-of-freedom spatial hyper-redundant robot. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... The trajectory planning of redundant robots is an important area of research and efficient optimization algorithms are needed. The pseudoinverse control is not repeatable, causing drift in joint space which is undesirable for physical control. This paper presents a new technique that combines the closed-loop pseudoinverse method with genetic algorithms, leading to an optimization criterion for repeatable control of redundant manipulators, and avoiding the joint angle drift problem. Computer simulations performed based on redundant and hyper-redundant planar manipulators show that, when the end-effector traces a closed path in the workspace, the robot returns to its initial configuration. The solution is repeatable for a workspace with and without obstacles in the sense that, after executing several cycles, the initial and final states of the manipulator are very close. Concentric tube robots are catheter-sized continuum robots that are well suited for minimally invasive surgery inside confined body cavities. These robots are constructed from sets of precurved superelastic tubes and are capable of assuming complex 3-D curves. The family of 3-D curves that the robot can assume depends on the number, curvatures, lengths, and stiffnesses of the tubes in its tube set. The robot design problem involves solving for a tube set that will produce the family of curves necessary to perform a surgical procedure. At a minimum, these curves must enable the robot to smoothly extend into the body and to manipulate tools over the desired surgical workspace while respecting anatomical constraints. This paper introduces an optimization framework that utilizes procedure- or patient-specific image-based anatomical models along with surgical workspace requirements to generate robot tube set designs. The algorithm searches for designs that minimize robot length and curvature and for which all paths required for the procedure consist of stable robot configurations. Two mechanics-based kinematic models are used. Initial designs are sought using a model assuming torsional rigidity. These designs are then refined using a torsionally compliant model. The approach is illustrated with clinically relevant examples from neurosurgery and intracardiac surgery. A new and efficient kinematic position and velocity solution scheme for spatial hyper-redundant manipulators is presented. The manipulator's arm has discrete links and universal joints. Backbone curve concepts and a modal approach are used to resolve the manipulator's redundancy. The effects of the mode shapes and the slope of the backbone curve at the starting point on the workspace are studied. It is shown that the usage of conventional mode shapes limits the workspace of the hyper-redundant arm. By introducing new mode shapes, an improved workspace is obtained. A simple and efficient recursive fitting method is introduced to avoid complications involved with solving systems of nonlinear algebraic equations. This method also guarantees the existence of solutions for the inverse kinematic problem at the velocity level. Velocity properties of the backbone curve are investigated and the inverse velocity propagation is solved for the spatial hyper-redundant arm. The velocity propagation scheme is recursive and is efficiently applicable to any number of links. Backpropagation algorithm is indispensable for the training of feedforward neural networks. It requires propagating error gradients sequentially from the output layer all the way back to the input layer. The backward locking in backpropagation algorithm constrains us from updating network layers in parallel and fully leveraging the computing resources. Recently, several algorithms have been proposed for breaking the backward locking. However, their performances degrade seriously when networks are deep. In this paper, we propose decoupled parallel backpropagation algorithm for deep learning optimization with convergence guarantee. Firstly, we decouple the backpropagation algorithm using delayed gradients, and show that the backward locking is removed when we split the networks into multiple modules. Then, we utilize decoupled parallel backpropagation in two stochastic methods and prove that our method guarantees convergence to critical points for the non-convex problem. Finally, we perform experiments for training deep convolutional neural networks on benchmark datasets. The experimental results not only confirm our theoretical analysis, but also demonstrate that the proposed method can achieve significant speedup without loss of accuracy. Abstract In this paper, the dynamic parameters, both inertial and frictional, of a 3-DOF RPS parallel manipulator are identified considering two important issues: the physical feasibility of the identified inertial parameters and the use of nonlinear friction models in the identification process in order to model the friction phenomenon at robot joints. The dynamic model of the parallel manipulator is obtained starting from the Gibbs–Appell equations of motion along with the Gauss principle of Least Action, and these equations of motion are rewritten in a their linear form with respect to the inertial parameters of the mechanical system. At this point, in accordance with the friction model considered, either linear or nonlinear, two types of dynamic models are dealt with: the totally and the partially linear with respect to the parameters to be identified. In order to solve the identification problem when nonlinear friction models are included, a nonlinear constrained optimization problem will be formulated and solved, instead of the Least Square Method, which is valid only for linear identification problems. It must be mentioned that the above-mentioned optimization problem will include the physical feasibility of the identified parameters in its formulation. The proposed procedure will be verified against a virtual parallel manipulator and finally, experimental identification processes are carried out over an actual parallel manipulator and a comparison is made between the LSM and the optimization process in the case of linear friction models, and between the linear and nonlinear friction models in the optimization process. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. In this paper we present a framework that allows a human and a robot to perform simultaneous manipulation tasks safely in close proximity. The proposed framework is based on early prediction of the human's motion. The prediction system, which builds on previous work in the area of gesture recognition, generates a prediction of human workspace occupancy by computing the swept volume of learned human motion trajectories. The motion planner then plans robot trajectories that minimize a penetration cost in the human workspace occupancy while interleaving planning and execution. Multiple plans are computed in parallel, one for each robot task available at the current time, and the trajectory with the least cost is selected for execution. We test our framework in simulation using recorded human motions and a simulated PR2 robot. Our results show that our framework enables the robot to avoid the human while still accomplishing the robot's task, even in cases where the initial prediction of the human's motion is incorrect. We also show that taking into account the predicted human workspace occupancy in the robot's motion planner leads to safer and more efficient interactions between the user and the robot than only considering the human's current configuration. The utility of a mobile manipulator largely depends on its kinematic structure and mounting point on the robot body. The reachable workspace of the robot can be obtained offline and modeled as a discretized map called Reachability map. A Capability map is obtained by including some quality measure for the local dexterity of the manipulator, which helps to identify good and bad regions for manipulation. Once the maps are obtained based on forward or inverse kinematic methods, they can be used for numerous analysis tasks such as robot kinematics and workspace quality assessment, robot mounting point analysis or redundancy and failure analysis. This paper covers basic aspects of the Reachability and Capability map generation and storage, and shows particular applications of the maps for space robotics. We present a scalable robot motion planning algorithm for reach-avoid problems. We assume a discrete-time, linear model of the robot dynamics and a workspace described by a set of obstacles and a target region, where both the obstacles and the region are polyhedra. Our goal is to construct a trajectory, and the associated control strategy, that steers the robot from its initial point to the target while avoiding obstacles. Differently from previous approaches, based on the discretization of the continuous state space or uniform discretization of the workspace, our approach, inspired by the lazy satisfiability modulo theory paradigm, decomposes the planning problem into smaller subproblems, which can be efficiently solved using specialized solvers. At each iteration, we use a coarse, obstacle-based discretization of the workspace to obtain candidate high-level, discrete plans that solve a set of Boolean constraints, while completely abstracting the low-level continuous dynamics. The feasibility of the proposed plans is then checked via a convex program, under constraints on both the system dynamics and the control inputs, and new candidate plans are generated until a feasible one is found. To achieve scalability, we show how to generate succinct explanations for the infeasibility of a discrete plan by exploiting a relaxation of the convex program that allows detecting the earliest possible occurrence of an infeasible transition between workspace regions. Simulation results show that our algorithm favorably compares with state-of-the-art techniques and scales well for complex systems, including robot dynamics with up to 50 continuous states. Going deeper and wider in neural architectures improves their accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need to change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, Liveness Analysis, Unified Tensor Pool, and Cost-Aware Recomputation; together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in these memory-saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has 104 basic network layers on a 12GB K40c. What is it that makes movement around obstacles hard? The answer seems clear: obstacles contort the geometry of the workspace and make it difficult to leverage what we consider easy and intuitive straight-line Cartesian geometry. But is Cartesian motion actually easy? It's certainly well-understood and has numerous applications. But beneath the details of linear algebra and pseudoinverses, lies a non-trivial Riemannian metric driving the solution. Cartesian motion is easy only because the pseudoinverse, our powerhouse tool, correctly represents how Euclidean workspace geometry pulls back into the configuration space. In light of that observation, it reasons that motion through a field of obstacles could be just as easy as long as we correctly account for how those obstacles warp the geometry of the space. This paper explores extending our geometric model of the robot beyond the notion of a Cartesian workspace space to fully model and leverage how geometry changes in the presence of obstacles. Intuitively, impenetrable obstacles form topological holes and geodesics curve around them accordingly. We formalize this intuition and develop a general motion optimization framework called Riemannian Motion Optimization (RieMO) to efficiently find motions using our geometric models. Our experiments demonstrate that, for many problems, obstacle avoidance can be much more natural when placed within the right geometric context. We describe an integrated system for personal workspace monitoring based around an RGB-D sensor. The approach is egocentric, facilitating full flexibility, and operates in real-time, providing object detection and recognition, and 3D trajectory estimation whilst the user undertakes tasks in the workspace. A prototype on-body system developed in the context of work-flow analysis for industrial manipulation and assembly tasks is described. The system is evaluated on two tasks with multiple users, and results indicate that the method is effective, giving good accuracy performance. Contrast is a crucial factor in visual information processing. It is desired for a visual system - irrespective of being biological or artificial - to "perceive" the world robustly under large potential changes in illumination. In this work, we studied the responses of deep neural networks (DNN) to identical images at different levels of contrast. We analysed the activation of kernels in the convolutional layers of eight prominent networks with distinct architectures (e.g. VGG and Inception). The results of our experiments indicate that those networks with a higher tolerance to alteration of contrast have more than one convolutional layer prior to the first max-pooling operator. It appears that the last convolutional layer before the first max-pooling acts as a mitigator of contrast variation in input images. In our investigation, interestingly, we observed many similarities between the mechanisms of these DNNs and biological visual systems. These comparisons allow us to understand more profoundly the underlying mechanisms of a visual system that is grounded on the basis of "data-analysis". Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles. Abstract : This paper presents a general theoretical framework for ensemble methods of constructing significantly improved regression estimates. Given a population of regression estimators, the authors construct a hybrid estimator that is as good or better in the mean square error sense than any estimator in the population. They argue that the ensemble method presented has several properties: (1) it efficiently uses all the networks of a population -- none of the networks need to be discarded; (2) it efficiently uses all of the available data for training without over-fitting; (3) it inherently performs regularization by smoothing in functional space, which helps to avoid over-fitting; (4) it utilizes local minima to construct improved estimates whereas other neural network algorithms are hindered by local minima; (5) it is ideally suited for parallel computation; (6) it leads to a very useful and natural measure of the number of distinct estimators in a population; and (7) the optimal parameters of the ensemble estimator are given in closed form. Experimental results show that the ensemble method dramatically improves neural network performance on difficult real-world optical character recognition tasks.