Geometric Synthesis of Manipulators Using the Monte Carlo Method

1990 ◽  
Vol 112 (3) ◽  
pp. 450-452 ◽  
Author(s):  
J. Rastegar ◽  
B. Fardanesh
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Installation Space ◽  
Joint Motion ◽  
Task Space ◽  
Arbitrary Geometry ◽  
New Approach ◽  
Space Geometry ◽  
The Monte Carlo Method

A new approach to the solution of problems of geometric synthesis of manipulators is presented. It utilizes the Monte Carlo method, is general, and can be applied to any type of manipulator. Joint motion limitations can be included. The task space may have any arbitrary geometry. The allowable manipulator installation space is determined. Synthesis for a specified number of possible configurations (branches), and optimization of task space geometry are discussed.

Independent and Dependent Scattering for Semitransparent Media Containing Bubbles

2004 ◽  
Author(s):  
Jaona H. Randrianalisoa ◽  
Dominique Baillis
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Bubble Size ◽  
Volume Fraction ◽  
Geometric Optic ◽  
New Approach ◽  
The Monte Carlo Method ◽  
Spherical Bubbles ◽  
Optic Limit ◽  
Radiative Characteristics

The objective of this present work is to provide a new approach for the radiative characteristics computation of semitransparent media containing spherical bubbles. The bubble size is considred much larger than the wavelength. First, previous models from the literature based on the independent theory are reviewed and established in the Geometric optic limit. Second, a predictive model using the Monte Carlo method is developed. The results obtained from the independent theory models and the Monte Carlo approach are compared. In addition, by varying the bubbles volume fraction, we investigate the limit of validity of the independent theory in such medium.

Determination of Allowable Manipulator Link Shapes; and Task, Installation, and Obstacle Spaces Using the Monte Carlo Method

1993 ◽  
Vol 115 (3) ◽  
pp. 457-461 ◽  
Author(s):  
Q. Tu ◽  
J. Rastegar
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Shape Design ◽  
Task Space ◽  
Operating Environment ◽  
Design Task ◽  
The Monte Carlo Method ◽  
Manipulator Link ◽  
And Task

The Monte Carlo method is used to solve a number of manipulator link shape design, task space, and obstacle placement problems. The shape of links of manipulators that are to operate within geometrically specified enclosures are determined. Within the enclosure, one or several obstacles may be present. For a specified operating environment, the spaces within which a given manipulator may be installed in order to perform the required tasks are identified. For a given enclosure, the allowable task spaces, and regions within which obstacles may be placed are mapped. By defining weighted distributions for the task and/or obstacle spaces, weighted allowable link shapes, and task and obstacle spaces are determined. The information can be used for optimal link shape synthesis, and for optimal task, obstacle, and manipulator placement purposes. The developed methods are very simple, numeric in nature, and readily implemented on computer. Several examples are presented.

Determination of the Allowable Manipulator Link Shapes; and Task, Installation, and Obstacle Spaces Using the Monte Carlo Method

1989 ◽  
Author(s):  
J. Rastegar ◽  
Q. Tu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Shape Design ◽  
Task Space ◽  
Operating Environment ◽  
End Effector ◽  
The Monte Carlo Method ◽  
Manipulator Link ◽  
And Task

Abstract The Monte Carlo method is used to solve a number of problems in manipulator link shape design, and in task space and obstacle placement. The shape of links of manipulators that are to operate within geometrically specified enclosures are determined. Within the enclosure, one or several obstacles may be present. The end effector operates within the task space, and may be required to reach points in different regions with different orientations. For a specified operating environment (enclosure geometry and obstacles), the spaces within which a given manipulator may be installed in order to perform the required tasks are identified. For a given enclosure, task space, and position of the fixed joint of the manipulator, regions within which obstacles may be placed are mapped. The developed methods are very simple, numeric in nature, and readily implemented on computer. Several examples are presented.

scholarly journals Monte Carlo Methods and the Koksma-Hlawka Inequality

Mathematics ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 725
Author(s):  
Sergey Ermakov ◽  
Svetlana Leora
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Random Processes ◽  
Random Numbers ◽  
Average Order ◽  
New Approach ◽  
Average Value ◽  
Acceptable Accuracy ◽  
Cubature Formulas ◽  
The Monte Carlo Method

The solution of a wide class of applied problems can be represented as an integral over the trajectories of a random process. The process is usually modeled with the Monte Carlo method and the integral is estimated as the average value of a certain function on the trajectories of this process. Solving this problem with acceptable accuracy usually requires modeling a very large number of trajectories; therefore development of methods to improve the accuracy of such algorithms is extremely important. The paper discusses Monte Carlo method modifications that use some classical results of the theory of cubature formulas (quasi-random methods). A new approach to the derivation of the well known Koksma-Hlawka inequality is pointed out. It is shown that for high ( s > 5 ) dimensions of the integral, the asymptotic decrease of the error comparable to the asymptotic behavior of the Monte Carlo method, can be achieved only for a very large number of nodes N. It is shown that a special criterion can serve as a correct characteristic of the error decrease (average order of the error decrease). Using this criterion, it is possible to analyze the error for reasonable values of N and to compare various quasi-random sequences. Several numerical examples are given. Obtained results make it possible to formulate recommendations on the correct use of the quasi-random numbers when calculating integrals over the trajectories of random processes.

scholarly journals Simulation of Genetic Systems by Automatic Digital Computers

1958 ◽  
Vol 11 (4) ◽  
pp. 603 ◽  
Author(s):  
JSF Barker
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Stochastic Processes ◽  
Digital Computer ◽  
Gene Frequencies ◽  
New Approach ◽  
Electronic Digital Computer ◽  
Selection Processes ◽  
The Monte Carlo Method ◽  
Experimental Populations

A new approach to analysis of the effect of selection on gene frequencies is described. An electronic digital computer (the SILLIAC) is used to simulate the selection processes that operate in populations. The Monte Carlo method permits inclusion of stochastic processes so that results should simulate those in natural or experimental populations.

UGR CALCULATION BASED ON THE LUMINANCE SPATIAL-ANGULAR DISTRIBUTION

2020 ◽  
Vol 2020 (4) ◽  
pp. 25-32
Author(s):  
Viktor Zheltov ◽  
Viktor Chembaev
Keyword(s):  
Monte Carlo ◽  
Angular Distribution ◽  
Monte Carlo Method ◽  
Visual Task ◽  
New Method ◽  
Lighting System ◽  
Preliminary Assessment ◽  
System A ◽  
The Monte Carlo Method

The article has considered the calculation of the unified glare rating (UGR) based on the luminance spatial-angular distribution (LSAD). The method of local estimations of the Monte Carlo method is proposed as a method for modeling LSAD. On the basis of LSAD, it becomes possible to evaluate the quality of lighting by many criteria, including the generally accepted UGR. UGR allows preliminary assessment of the level of comfort for performing a visual task in a lighting system. A new method of "pixel-by-pixel" calculation of UGR based on LSAD is proposed.

PSHA software review based on the Monte Carlo method

2020 ◽  
pp. 14-24
Author(s):  
V.A. Mironov ◽  
S.A. Peretokin ◽  
K.V. Simonov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Seismic Hazard ◽  
Hazard Analysis ◽  
Software Review ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Test Calculation ◽  
Seismic Surveys ◽  
The Monte Carlo Method

The article is a continuation of the software research to perform probabilistic seismic hazard analysis (PSHA) as one of the main stages in engineering seismic surveys. The article provides an overview of modern software for PSHA based on the Monte Carlo method, describes in detail the work of foreign programs OpenQuake Engine and EqHaz. A test calculation of seismic hazard was carried out to compare the functionality of domestic and foreign software.

Sign in / Sign up

Export Citation Format

Share Document