scholarly journals Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3274
Author(s):  
Jesus Huerta-Chua ◽  
Gerardo Diaz-Arango ◽  
Hector Vazquez-Leal ◽  
Javier Flores-Mendez ◽  
Mario Moreno-Moreno ◽  
...  
Keyword(s):  
Configuration Space ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Robotic Manipulators ◽  
Industrial Sector ◽  
Industrial Applications ◽  
Study Results ◽  
Six Dof ◽  
Multiple Query ◽  
Grid Based

The applicability of the path planning strategy to robotic manipulators has been an exciting topic for researchers in the last few decades due to the large demand in the industrial sector and its enormous potential development for space, surgical, and pharmaceutical applications. The automation of high-degree-of-freedom (DOF) manipulator robots is a challenging task due to the high redundancy in the end-effector position. Additionally, in the presence of obstacles in the workspace, the task becomes even more complicated. Therefore, for decades, the most common method of integrating a manipulator in an industrial automated process has been the demonstration technique through human operator intervention. Although it is a simple strategy, some drawbacks must be considered: first, the path’s success, length, and execution time depend on operator experience; second, for a structured environment with few objects, the planning task is easy. However, for most typical industrial applications, the environments contain many obstacles, which poses challenges for planning a collision-free trajectory. In this paper, a multiple-query method capable of obtaining collision-free paths for high DOF manipulators with multiple surrounding obstacles is presented. The proposed method is inspired by the resistive grid-based planner method (RGBPM). Furthermore, several improvements are implemented to solve complex planning problems that cannot be handled by the original formulation. The most important features of the proposed planner are as follows: (1) the easy implementation of robotic manipulators with multiple degrees of freedom, (2) the ability to handle dozens of obstacles in the environment, (3) compatibility with various obstacle representations using mathematical models, (4) a new recycling of a previous simulation strategy to convert the RGBPM into a multiple-query planner, and (5) the capacity to handle large sparse matrices representing the configuration space. A numerical simulation was carried out to validate the proposed planning method’s effectiveness for manipulators with three, five, and six DOFs on environments with dozens of surrounding obstacles. The case study results show the applicability of the proposed novel strategy in quickly computing new collision-free paths using the first execution data. Each new query requires less than 0.2 s for a 3 DOF manipulator in a configuration space free-modeled by a 7291 × 7291 sparse matrix and less than 30 s for five and six DOF manipulators in a configuration space free-modeled by 313,958 × 313,958 and 204,087 × 204,087 sparse matrices, respectively. Finally, a simulation was conducted to validate the proposed multiple-query RGBPM planner’s efficacy in finding feasible paths without collision using a six-DOF manipulator (KUKA LBR iiwa 14R820) in a complex environment with dozens of surrounding obstacles.

scholarly journals Amesos2 and Belos: Direct and Iterative Solvers for Large Sparse Linear Systems

2012 ◽  
Vol 20 (3) ◽  
pp. 241-255 ◽  
Author(s):  
Eric Bavier ◽  
Mark Hoemmen ◽  
Sivasankaran Rajamanickam ◽  
Heidi Thornquist
Keyword(s):  
Linear Systems ◽  
Iterative Methods ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Direct Methods ◽  
Iterative Solvers ◽  
Software Project ◽  
Sparse Linear Systems ◽  
Scalar Type ◽  
Mixed Precision

Solvers for large sparse linear systems come in two categories: direct and iterative. Amesos2, a package in the Trilinos software project, provides direct methods, and Belos, another Trilinos package, provides iterative methods. Amesos2 offers a common interface to many different sparse matrix factorization codes, and can handle any implementation of sparse matrices and vectors, via an easy-to-extend C++ traits interface. It can also factor matrices whose entries have arbitrary “Scalar” type, enabling extended-precision and mixed-precision algorithms. Belos includes many different iterative methods for solving large sparse linear systems and least-squares problems. Unlike competing iterative solver libraries, Belos completely decouples the algorithms from the implementations of the underlying linear algebra objects. This lets Belos exploit the latest hardware without changes to the code. Belos favors algorithms that solve higher-level problems, such as multiple simultaneous linear systems and sequences of related linear systems, faster than standard algorithms. The package also supports extended-precision and mixed-precision algorithms. Together, Amesos2 and Belos form a complete suite of sparse linear solvers.

Optimal Robotic Assembly Planning Using Dijkstra’s Algorithm

1994 ◽  
Author(s):  
Sam Anand ◽  
Mohamed Sabri
Keyword(s):  
Configuration Space ◽  
Material Handling ◽  
Search Algorithm ◽  
Robotic Manipulators ◽  
Robotic Assembly ◽  
Graph Search ◽  
Assembly Planning ◽  
Robotic Welding ◽  
Fast Collision ◽  
Space Approach

Abstract Robots play an important role in the modern factory and are used in a manufacturing cell for several functions such as assembly, material handling, robotic welding, etc. One of the principal problems faced by robots while performing their tasks is the presence of obstacles such as fixtures, tools, and objects in the robot workspace. Such objects could result in a collision with one of the arms of the robots. Fast collision-free motion planning algorithms are therefore necessary for robotic manipulators to operate in a wide variety of changing environments. The configuration space approach is one of the widely used methods for collision-free robotic path planning. This paper presents a novel graph-based method of searching the configuration space for a collision-free path in a robotic assembly operation. Dijkstra’s graph search algorithm is used for optimizing the joint displacements of the robot while performing the assembly task. The methodology is illustrated using a simple robotic assembly planning task.

scholarly journals Application-based fault tolerance techniques for sparse matrix solvers

2017 ◽  
Vol 32 (5) ◽  
pp. 627-640
Author(s):  
Simon McIntosh–Smith ◽  
Rob Hunt ◽  
James Price ◽  
Alex Warwick Vesztrocy
Keyword(s):  
Fault Tolerance ◽  
High Performance Computing ◽  
High Performance ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Error Correcting Codes ◽  
Computing Systems ◽  
Hardware Costs ◽  
Extreme Scale ◽  
Performance Computing

High-performance computing systems continue to increase in size in the quest for ever higher performance. The resulting increased electronic component count, coupled with the decrease in feature sizes of the silicon manufacturing processes used to build these components, may result in future exascale systems being more susceptible to soft errors caused by cosmic radiation than in current high-performance computing systems. Through the use of techniques such as hardware-based error-correcting codes and checkpoint-restart, many of these faults can be mitigated at the cost of increased hardware overhead, run-time, and energy consumption that can be as much as 10–20%. Some predictions expect these overheads to continue to grow over time. For extreme scale systems, these overheads will represent megawatts of power consumption and millions of dollars of additional hardware costs, which could potentially be avoided with more sophisticated fault-tolerance techniques. In this paper we present new software-based fault tolerance techniques that can be applied to one of the most important classes of software in high-performance computing: iterative sparse matrix solvers. Our new techniques enables us to exploit knowledge of the structure of sparse matrices in such a way as to improve the performance, energy efficiency, and fault tolerance of the overall solution.

scholarly journals Current perspective on production and applications of microbial cellulases: a review

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nisha Bhardwaj ◽  
Bikash Kumar ◽  
Komal Agrawal ◽  
Pradeep Verma
Keyword(s):  
Critical Factor ◽  
Industrial Sector ◽  
Industrial Applications ◽  
Cellulolytic Enzymes ◽  
Promising Tool ◽  
The Status ◽  
Key Enzyme ◽  
Increasing Demand ◽  
Immobilization Techniques ◽  
Insight Into

AbstractThe potential of cellulolytic enzymes has been widely studied and explored for bioconversion processes and plays a key role in various industrial applications. Cellulase, a key enzyme for cellulose-rich waste feedstock-based biorefinery, has increasing demand in various industries, e.g., paper and pulp, juice clarification, etc. Also, there has been constant progress in developing new strategies to enhance its production, such as the application of waste feedstock as the substrate for the production of individual or enzyme cocktails, process parameters control, and genetic manipulations for enzyme production with enhanced yield, efficiency, and specificity. Further, an insight into immobilization techniques has also been presented for improved reusability of cellulase, a critical factor that controls the cost of the enzyme at an industrial scale. In addition, the review also gives an insight into the status of the significant application of cellulase in the industrial sector, with its techno-economic analysis for future applications. The present review gives a complete overview of current perspectives on the production of microbial cellulases as a promising tool to develop a sustainable and greener concept for industrial applications.

A kinematic structure-based classification and compact kinematic equations for six-dof industrial robotic manipulators

2001 ◽  
Vol 36 (7) ◽  
pp. 817-832 ◽  
Author(s):  
Tuna Balkan ◽  
M. Kemal Özgören ◽  
M.A. Sahir Arıkan ◽  
H. Murat Baykurt
Keyword(s):  
Robotic Manipulators ◽  
Kinematic Structure ◽  
Six Dof

Machine Learning to Design an Auto-tuning System for the Best Compressed Format Detection for Parallel Sparse Computations

2021 ◽  
Author(s):  
Olfa Hamdi-Larbi ◽  
Ichrak Mehrez ◽  
Thomas Dufaud
Keyword(s):  
Machine Learning ◽  
Numerical Method ◽  
High Performance ◽  
Programming Model ◽  
Learning Algorithm ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Matrix Compression ◽  
Target Architecture ◽  
Parallel Programming Model

Many applications in scientific computing process very large sparse matrices on parallel architectures. The presented work in this paper is a part of a project where our general aim is to develop an auto-tuner system for the selection of the best matrix compression format in the context of high-performance computing. The target smart system can automatically select the best compression format for a given sparse matrix, a numerical method processing this matrix, a parallel programming model and a target architecture. Hence, this paper describes the design and implementation of the proposed concept. We consider a case study consisting of a numerical method reduced to the sparse matrix vector product (SpMV), some compression formats, the data parallel as a programming model and, a distributed multi-core platform as a target architecture. This study allows extracting a set of important novel metrics and parameters which are relative to the considered programming model. Our metrics are used as input to a machine-learning algorithm to predict the best matrix compression format. An experimental study targeting a distributed multi-core platform and processing random and real-world matrices shows that our system can improve in average up to 7% the accuracy of the machine learning.

Applications

2004 ◽  
Author(s):  
Wesley Petersen ◽  
Peter Arbenz
Keyword(s):  
Linear Algebra ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Simple Procedure ◽  
Complex Numbers ◽  
Multiprocessor Computer ◽  
Basic Linear Algebra Subprograms ◽  
The Matrix ◽  
Shared Memory Multiprocessor ◽  
Real Complex

Linear algebra is often the kernel of most numerical computations. It deals with vectors and matrices and simple operations like addition and multiplication on these objects. Vectors are one-dimensional arrays of say n real or complex numbers x0, x1, . . . , xn−1. We denote such a vector by x and think of it as a column vector, On a sequential computer, these numbers occupy n consecutive memory locations. This is also true, at least conceptually, on a shared memory multiprocessor computer. On distributed memory multicomputers, the primary issue is how to distribute vectors on the memory of the processors involved in the computation. Matrices are two-dimensional arrays of the form The n · m real (complex) matrix elements aij are stored in n · m (respectively 2 · n ·m if complex datatype is available) consecutive memory locations. This is achieved by either stacking the columns on top of each other or by appending row after row. The former is called column-major, the latter row-major order. The actual procedure depends on the programming language. In Fortran, matrices are stored in column-major order, in C in row-major order. There is no principal difference, but for writing efficient programs one has to respect how matrices are laid out. To be consistent with the libraries that we will use that are mostly written in Fortran, we will explicitly program in column-major order. Thus, the matrix element aij of the m×n matrix A is located i+j · m memory locations after a00. Therefore, in our C codes we will write a[i+j*m]. Notice that there is no such simple procedure for determining the memory location of an element of a sparse matrix. In Section 2.3, we outline data descriptors to handle sparse matrices. In this and later chapters we deal with one of the simplest operations one wants to do with vectors and matrices: the so-called saxpy operation (2.3). In Tables 2.1 and 2.2 are listed some of the acronyms and conventions for the basic linear algebra subprograms discussed in this book.

scholarly journals Electrospinning of PCL-Based Blends: Processing Optimization for Their Scalable Production

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3853
Author(s):  
Marina P. Arrieta ◽  
Adrián Leonés Gil ◽  
Maysa Yusef ◽  
José M. Kenny ◽  
Laura Peponi
Keyword(s):  
Thermal Stability ◽  
Mechanical Response ◽  
Bulk Material ◽  
Processing Parameters ◽  
Industrial Sector ◽  
Industrial Applications ◽  
Elongation At Break ◽  
Processing Optimization ◽  
Scalable Production

In this work poly(ε-caprolactone) (PCL) based electrospun mats were prepared by blending PCL with microcrystalline cellulose (MCC) and poly(3-hydroxybutyrate) (PHB). The electrospinning processing parameters were firstly optimized with the aim to obtain scalable PCL-based electrospun mats to be used in the industrial sector. Neat PCL as well as PCL-MCC and PCL-PHB based mats in different proportions (99:1; 95:5; 90:10) were prepared. A complete morphological, thermal and mechanical characterization of the developed materials was carried out. Scanning electron microscopy (SEM) observations showed that the addition of PHB to the PCL matrix considerably reduced the formation of beads. Both the addition of MCC and PHB reduced the thermal stability of PCL, but obtained materials with enough thermal stability for the intended use. The electrospun PCL fibers show greatly reduced flexibility with respect to the PCL bulk material, however when PCL is blended with PHB their stretchability is increased, changing their elongation at break from 35% to 70% when 10 wt% of PHB is blended with PCL. However, the mechanical response of the different blends increases with respect to the neat electrospun PCL, offering the possibility to modulate their properties according to the required industrial applications.

Random banded and sparse matrices

2018 ◽  
Author(s):  
Roland Speicher
Keyword(s):  
Statistical Mechanics ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Bethe Lattice ◽  
Dual Representation ◽  
Band Matrix ◽  
Sparse Random Matrices ◽  
Matrix Ensembles ◽  
Definition Of ◽  
Local Spacing

This article discusses some mathematical results and conjectures about random band matrix ensembles (RBM) and sparse matrix ensembles. Spectral problems of RBM and sparse matrices can be expressed in terms of supersymmetric (SUSY) statistical mechanics that provides a dual representation for disordered quantum systems. This representation offers important insights into nonperturbative aspects of the spectrum and eigenfunctions of RBM. The article first presents the definition of RBM ensembles before considering the density of states, the behaviour of eigenvectors, and eigenvalue statistics for RBM and sparse random matrices. In particular, it highlights the relations with random Schrödinger (RS) and the role of the dimension of the lattice. It also describes the connection between RBM and statistical mechanics, the spectral theory of large random sparse matrices, conjectures and theorems about eigenvectors and local spacing statistics, and the RS operator on the Cayley tree or Bethe lattice.

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