The Accuracy & Repeatability of Industrial Robots Research Paper

The accuracy of industrial robots and their repeatability plays an important part in the contemporary production process, as robots replace manual labor and perform multiple tasks. At the same time, there are risks associated with possible errors made by industrial robots. This is why the improvement of their accuracy is needed. The study focuses on the improvement of the accuracy and repeatability of an industrial robot by using Saturated Design as a part of sequential Experimental Design.

Background of the Design of Experiments

The design of experiments may vary but the ultimate goal of such design is to create conditions and implement experiments in terms of the study on the ground of scientific principles and with the high level of reliability and validity of the experiment. In the past, researchers needed to conduct experiments to make their findings. This trend is still relevant because experiments comprise an integral part of the scientific progress (Myers, et al., 2010). This is why researchers set, design and implement experiments to prove their ideas and to draw the scientific evidence of the righteousness of their scientific assumptions or hypotheses. In such a way, the design of experiments was traditionally oriented on obtaining variation of the information related to the subject of the study.

The Design of Experiment

Traditionally, the Design of Experiment was used to describe variations of the information under certain conditions that are hypothesized to reflect the variation. Researchers used experiments to make their findings and to prove their scientific hypothesis. Ideally, the Design of Experiment is the design of the study that allows the implementation of the experiment that proves the hypothesis made by the researcher (Walpole, et al., 2007). There are different designs of experiment that may be conducted and researchers choose the design of experiment that matches goals of their study the best. In such a way, they obtain reliable and accurate information about the problem or subject they study.

The characteristics of the experiment

The study that involves the experiment has to comply with several key principles which reveal the essence of the experiment and are essential characteristics of the experiment. The study dedicated to the improvement of accuracy and repeatability of an industrial robot by using Saturated Design as a part of sequential Experimental Design should also comply with those principles (Bisgaard, 2008). First, comparison is very important for the experiment because the experiment involves the comparison between samples of information involved in the study, for example, the performance of industrial robots and their accuracy. The comparison helps to determine possible differences revealed in the course of the study and measure the accuracy and repeatability of industrial robots on the ground of changes tested in the course of the experiment.

Another distinct feature of the experiment is randomization. Randomization is very important to avoid the biased and subjective attitude toward the study and interpretation of results of the study. Randomization helps to select the information for the experiment objectively (Ottoboni, 1991). Therefore, randomization helps to avoid biases in the study. Statistical replication is another characteristic of the experiment because experiments have to deal with measurement of uncertainty. Statistical replication helps the researcher to determine the uncertainty and obtain more accurate results of the study. In addition, the experiment has such characteristic as blocking, when experimental units are blocked in groups for the better management and achievement of accurate results of the experiment. For example, new industrial robots may be compared to old ones to measure the difference in their accuracy and repeatability.


Bisgaard, S (2008) “Must a Process be in Statistical Control before Conducting Designed Experiments?”, Quality Engineering, ASQ, 20 (2), pp 143 – 176

Myers, R.H., et al. (2010). Generalized linear models : with applications in engineering and the sciences (2 ed.). Hoboken, N.J: Wiley.

Ottoboni, M. A. (1991). The dose makes the poison : a plain-language guide to toxicology (2nd ed.). New York, N.Y: Van Nostrand Reinhold.

Walpole, R.E., et al. (2007). Probability & statistics for engineers & scientists (8 ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

The terms offer and acceptance. (2016, May 17). Retrieved from

[Accessed: February 4, 2023]

"The terms offer and acceptance.", 17 May 2016.

[Accessed: February 4, 2023] (2016) The terms offer and acceptance [Online].
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[Accessed: February 4, 2023]

"The terms offer and acceptance.", 17 May 2016

[Accessed: February 4, 2023]

"The terms offer and acceptance.", 17 May 2016

[Accessed: February 4, 2023]

"The terms offer and acceptance.", 17 May 2016

[Accessed: February 4, 2023]

"The terms offer and acceptance.", 17 May 2016

[Accessed: February 4, 2023]
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