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 63 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 63 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 selfcitation 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 ...