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Marginal probabilistic modeling of the delays in the sensory data transmission of networked telerobots.

Gago-Benítez A, Fernández-Madrigal JA, Cruz-Martín A - Sensors (Basel) (2014)

Bottom Line: This paper studies marginal probability distributions that, under mild assumptions, can be a good approximation of the real distribution of the delays without using knowledge of their dynamics, are efficient to compute, and need minor modifications on the networked robot.Since sequences of delays exhibit strong non-linearities in these networked applications, to satisfy the iid hypothesis required by the marginal approach we apply a change detection method.The results reported here indicate that some parametrical models explain well many more real scenarios when using this change detection method, while some non-parametrical distributions have a very good rate of successful modeling in the case that non-linearity detection is not possible and that we split the total delay into its three basic terms: server, network and client times.

View Article: PubMed Central - PubMed

Affiliation: Systems Engineering and Automation Department, University of Málaga, Campus Teatinos, Boulevard Luis Pasteur s/n, Málaga 29071, Spain. anagagobenitez@gmail.com.

ABSTRACT
Networked telerobots are remotely controlled through general purpose networks and components, which are highly heterogeneous and exhibit stochastic response times; however their correct teleoperation requires a timely flow of information from sensors to remote stations. In order to guarantee these time requirements, a good on-line probabilistic estimation of the sensory transmission delays is needed. In many modern applications this estimation must be computationally highly efficient, e.g., when the system includes a web-based client interface. This paper studies marginal probability distributions that, under mild assumptions, can be a good approximation of the real distribution of the delays without using knowledge of their dynamics, are efficient to compute, and need minor modifications on the networked robot. Since sequences of delays exhibit strong non-linearities in these networked applications, to satisfy the iid hypothesis required by the marginal approach we apply a change detection method. The results reported here indicate that some parametrical models explain well many more real scenarios when using this change detection method, while some non-parametrical distributions have a very good rate of successful modeling in the case that non-linearity detection is not possible and that we split the total delay into its three basic terms: server, network and client times.

No MeSH data available.


Related in: MedlinePlus

Autocorrelograms of the original scenarios of Figure 2 and of the visually purged scenarios once they are divided into the visual regimes marked there. Observe that in the cases when some lags go above/below the confidence limits, they do not exceed the 5% of the total count of lags (except for scenario #13).
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f4-sensors-14-02305: Autocorrelograms of the original scenarios of Figure 2 and of the visually purged scenarios once they are divided into the visual regimes marked there. Observe that in the cases when some lags go above/below the confidence limits, they do not exceed the 5% of the total count of lags (except for scenario #13).

Mentions: As explained in the introduction, it is a suitable approximation to assume statistical independence of sequential delay values in the stationary parts of the signal, which will allow us to apply the basic tools of the next sections and, in general, work with marginal distributions as suitable models of the data. To confirm this, we have analysed the correlation coefficients in the acquired data: the delays would be dependent if the autocorrelogram function (ACF) goes above the corresponding confidence limits; otherwise, our independence hypothesis cannot be rejected [35]. Figure 4 shows the ACFs of the scenarios, where it is clear the strong dependence of many of them when non-stationarity is not detected and handled, and also the ACF of the visually purged scenarios (eliminating the visually detected bursts and regimes marked in Figure 2). We can see how the ACF stays closer to zero in the latter, and it remains below the confidence limits in most cases. This supports our assumption of considering iid sequences of delays (independent and identically distributed) as long as they are separated in regimes and we can also detect bursts and isolated outliers. Of course, smooth variations on the underlying distribution—smooth trend changes—are still possible, but the results obtained with our abrupt detection method also applies in many cases that exhibit such trending.


Marginal probabilistic modeling of the delays in the sensory data transmission of networked telerobots.

Gago-Benítez A, Fernández-Madrigal JA, Cruz-Martín A - Sensors (Basel) (2014)

Autocorrelograms of the original scenarios of Figure 2 and of the visually purged scenarios once they are divided into the visual regimes marked there. Observe that in the cases when some lags go above/below the confidence limits, they do not exceed the 5% of the total count of lags (except for scenario #13).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958226&req=5

f4-sensors-14-02305: Autocorrelograms of the original scenarios of Figure 2 and of the visually purged scenarios once they are divided into the visual regimes marked there. Observe that in the cases when some lags go above/below the confidence limits, they do not exceed the 5% of the total count of lags (except for scenario #13).
Mentions: As explained in the introduction, it is a suitable approximation to assume statistical independence of sequential delay values in the stationary parts of the signal, which will allow us to apply the basic tools of the next sections and, in general, work with marginal distributions as suitable models of the data. To confirm this, we have analysed the correlation coefficients in the acquired data: the delays would be dependent if the autocorrelogram function (ACF) goes above the corresponding confidence limits; otherwise, our independence hypothesis cannot be rejected [35]. Figure 4 shows the ACFs of the scenarios, where it is clear the strong dependence of many of them when non-stationarity is not detected and handled, and also the ACF of the visually purged scenarios (eliminating the visually detected bursts and regimes marked in Figure 2). We can see how the ACF stays closer to zero in the latter, and it remains below the confidence limits in most cases. This supports our assumption of considering iid sequences of delays (independent and identically distributed) as long as they are separated in regimes and we can also detect bursts and isolated outliers. Of course, smooth variations on the underlying distribution—smooth trend changes—are still possible, but the results obtained with our abrupt detection method also applies in many cases that exhibit such trending.

Bottom Line: This paper studies marginal probability distributions that, under mild assumptions, can be a good approximation of the real distribution of the delays without using knowledge of their dynamics, are efficient to compute, and need minor modifications on the networked robot.Since sequences of delays exhibit strong non-linearities in these networked applications, to satisfy the iid hypothesis required by the marginal approach we apply a change detection method.The results reported here indicate that some parametrical models explain well many more real scenarios when using this change detection method, while some non-parametrical distributions have a very good rate of successful modeling in the case that non-linearity detection is not possible and that we split the total delay into its three basic terms: server, network and client times.

View Article: PubMed Central - PubMed

Affiliation: Systems Engineering and Automation Department, University of Málaga, Campus Teatinos, Boulevard Luis Pasteur s/n, Málaga 29071, Spain. anagagobenitez@gmail.com.

ABSTRACT
Networked telerobots are remotely controlled through general purpose networks and components, which are highly heterogeneous and exhibit stochastic response times; however their correct teleoperation requires a timely flow of information from sensors to remote stations. In order to guarantee these time requirements, a good on-line probabilistic estimation of the sensory transmission delays is needed. In many modern applications this estimation must be computationally highly efficient, e.g., when the system includes a web-based client interface. This paper studies marginal probability distributions that, under mild assumptions, can be a good approximation of the real distribution of the delays without using knowledge of their dynamics, are efficient to compute, and need minor modifications on the networked robot. Since sequences of delays exhibit strong non-linearities in these networked applications, to satisfy the iid hypothesis required by the marginal approach we apply a change detection method. The results reported here indicate that some parametrical models explain well many more real scenarios when using this change detection method, while some non-parametrical distributions have a very good rate of successful modeling in the case that non-linearity detection is not possible and that we split the total delay into its three basic terms: server, network and client times.

No MeSH data available.


Related in: MedlinePlus