An Approach for Examining Alternatives Attributable to Locational Uncertainty

2014 ◽  
Vol 41 (1) ◽  
pp. 93-109 ◽  
Author(s):  
Alan T Murray ◽  
Ran Wei ◽  
Tony H Grubesic
Keyword(s):  
Locational Uncertainty

scholarly journals Characteristics of locational uncertainty marginal price for correlated uncertainties of variable renewable generation and demands

2021 ◽  
Vol 282 ◽  
pp. 116064
Author(s):  
Xin Fang ◽  
Hantao Cui ◽  
Ershun Du ◽  
Fangxing Li ◽  
Chongqing Kang
Keyword(s):  
Renewable Generation ◽  
Marginal Price ◽  
Locational Uncertainty

scholarly journals Locational uncertainty in georeferencing public health datasets

2001 ◽  
Vol 11 (4) ◽  
pp. 329-334 ◽  
Author(s):  
STEVE M DEARWENT ◽  
ROBERT R JACOBS ◽  
JOHN B HALBERT
Keyword(s):  
Public Health ◽  
Locational Uncertainty

scholarly journals Tolerance Volume Due to Joint Variable Errors in Robots

1989 ◽  
Vol 111 (4) ◽  
pp. 597-604 ◽  
Author(s):  
Woo-Jong Lee ◽  
T. C. Woo
Keyword(s):  
Confidence Level ◽  
Degrees Of Freedom ◽  
Probabilistic Approach ◽  
Automatic Generation ◽  
Linear Mapping ◽  
Precision Engineering ◽  
Probability Of Error ◽  
Robot Configuration ◽  
Joint Variable ◽  
Locational Uncertainty

The locational uncertainty of a manipulator is largely due to the errors of the joint variables. But these errors cannot be easily compensated for because they are dependent on the operation (i.e., robot-configuration). Motivated by the need to conduct precision engineering and the intellectual curiosity of geometric uncertainty, the probabilistic tolerance volume due to joint errors is investigated. By defining the locational uncertainty in Cartesian space as a tolerance volume, the investigation focuses on the automatic generation of the tolerance volume from a given confidence level. For this purpose, the linear mapping form Δq space to Δd space through Jacobian matrix is analyzed probabilistically. Probabilistic approach is advantageous since the tolerance volume by the deterministic approach is found to be unnecessarily large. With the assumption of normality of joint variables, this paper begins with the computation of the confidence level for a given tolerance volume. A fast analytic procedure, which gives a considerable time-reduction compared to the commonly used Monte-Carlo simulation, is presented. Based on the monotonic relation between confidence level and tolerance volume, the procedure is used to generate the tolerance volume covering the desired confidence level. The scheme is tested with the six degrees-of-freedom Stanford manipulator and shows a significant (more than 5 times) reduction in the size of the tolerance volume with a 0.3 percent probability of error.

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