By Tine Lefebvre
ISBN-10: 3540280235
ISBN-13: 9783540280231
ISBN-10: 3540315047
ISBN-13: 9783540315049
This monograph makes a speciality of the right way to in achieving extra robotic autonomy by way of trustworthy processing talents. "Nonlinear Kalman Filtering for Force-Controlled robotic projects " discusses the most recent advancements within the parts of touch modeling, nonlinear parameter estimation and activity plan optimization for more advantageous estimation accuracy. Kalman filtering recommendations are utilized to spot the touch kingdom in accordance with strength sensing among a grasped item and the surroundings. the opportunity of this paintings is to be chanced on not just for commercial robotic operation in house, sub-sea or nuclear situations, but additionally for carrier robots working in unstructured environments co-habited via people the place self sufficient compliant projects require energetic sensing.
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Extra resources for Nonlinear Kalman Filtering for Force-Controlled Robot Tasks
Example text
Nonlinear Kalman Filtering, STAR 19, pp. 25–49, 2005. 2 Bayesian Versus Classical Statistics Bayesian Versus Classical Statistics The main difference between the Bayesian and the classical approach (also called frequentist approach) to statistics is the interpretation of the concept probability. , probability does not mean “randomness” but more generally as a subjective measure of uncertainty. , assume a coin tossing experiment. The possible outcome of the experiment belongs to the discrete set of values {head, tail}.
Active sensing focuses on the active search for system and measurement inputs such that the measurement data are “informative” and the posterior estimate will be “accurate”. , the executed CF sequence and the compliant motion trajectories in each CF, influence the accuracy of the geometrical parameter estimates. Active sensing consists in optimising this task plan in order to obtain accurate estimates. The formulation of the active sensing problem needs a measure of the accuracy/uncertainty of an estimate (also called “information measure”).
This approximation assumes that a consisˆ i exists which detects inconsistency as soon as tency test for a model M ˆ j , j = i. Examples of the data originates from one of the other models M such consistency tests for linear systems subject to Gaussian model uncertainty are the testing of the innovation4 process for zero mean, whiteness and a given covariance [188]; and the testing of the sum of a number of Normalised Innovation Squared values4, p. 36 (SNIS) [19, 268] to lie in a confidence interval.
Nonlinear Kalman Filtering for Force-Controlled Robot Tasks by Tine Lefebvre
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