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.

Monte Carlo method for the reduction of measurement errors in the material parameter estimation with cavities

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ronny Peter ◽  
Luca Bifano ◽  
Gerhard Fischerauer
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Measurement Errors ◽  
Input Data ◽  
Material Parameter ◽  
Cavity Resonator ◽  
Calculation Algorithm ◽  
The Monte Carlo Method ◽  
Electromagnetic Resonances

Abstract The quantitative determination of material parameter distributions in resonant cavities is a relatively new method for the real-time monitoring of chemical processes. For this purpose, electromagnetic resonances of the cavity resonator are used as input data for the reverse calculation (inversion). However, the reverse calculation algorithm is sensitive to disturbances of the input data, which produces measurement errors and tends to diverge, which leads to no measurement result at all. In this work a correction algorithm based on the Monte Carlo method is presented which ensures a convergent behavior of the reverse calculation algorithm.

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

scholarly journals Akurasi dalam Memprediksi Penetapan Besaran Anggaran Proposal Pendapatan dan Belanja Universitas Menggunakan Metode Monte Carlo

2020 ◽  
pp. 60-66
Author(s):  
Yogo Turnandes ◽  
Yuhandri Yunus
Keyword(s):  
Higher Education ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Community Service ◽  
The Monte Carlo Method ◽  
Average Accuracy ◽  
Service Activities ◽  
High Level ◽  
The University

The Institute for Research and Community Service at the University of Lancang Kuning has the mandate in research and service activities which are the two dharmas of the Tri Dharma of Higher Education. The purpose of this study is to predict the determination of the budget amount for the University Income and Expenditure Budget (APBU) proposal approved at LPPM Unilak for the following year. Thus, it will make it easier for the LPPM leadership to make decisions on the acceptance of APBU proposals that are approved quickly and optimally. The data used in this research is APBU research and service proposal data approved in 2018 to 2020 which is processed using the monte carlo method. The APBU proposal budget prediction will be carried out every year. Based on the results of tests that have been carried out with the monte carlo method, it is found that the system used to predict the amount of APBU proposal budget approved in 2019 with an average accuracy of 84% and in 2020 with an average accuracy of 73%. Then with a fairly high level of accuracy, the application of the Monte Carlo method is considered to be able to predict the amount of the APBU proposal budget that is approved by each faculty each year.

scholarly journals Research on Region Division of Large Workpiece Based on Dexterous Workspace of Robot

2021 ◽  
Vol 2095 (1) ◽  
pp. 012059
Author(s):  
Yonggui Zhang ◽  
Gan Zhang ◽  
Xin Xu ◽  
Qianqiu Zhao
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Coordinate System ◽  
Large Scale ◽  
Forward Kinematics ◽  
Geometric Features ◽  
Task Space ◽  
Region Division ◽  
And Task

Abstract In order to complete the processing of large-scale workpieces, a region division of large-scale workpieces based on robot dexterous workspace is studied. The linkage coordinate system of the robot is established by D-H method, and the forward kinematics equation of the robot is obtained; Monte Carlo method is used to analyze the workspace of the robot, and MATLAB is used to program to draw the workspace and task space of the robot; Taking an example part as the object, the feature of the part is studied, and the process of determining the task space area of the workpiece in the dexterous workspace of the robot is given, and the region of the workpiece is divided based on the size of the task space and the geometric features of the workpiece.

Accurate Determination of Silicon Inversion Layer Mobility by the Monte Carlo Method

1993 ◽  
pp. 481-484 ◽  
Author(s):  
F. Gámiz ◽  
J. A. López-Villanueva ◽  
J. Banqueri ◽  
J. A. Jiménez-Tejada ◽  
P. Cartujo
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Accurate Determination ◽  
Inversion Layer ◽  
The Monte Carlo Method
Sign in / Sign up

Export Citation Format

Share Document