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. 2012 Aug;39(10):9602-9611.
doi: 10.1016/j.eswa.2012.02.145.

Automated Recognition of Robotic Manipulation Failures in High-throughput Biodosimetry Tool

Youhua Chen  1 , Hongliang Wang, Jian Zhang, Guy Garty, Nabil Simaan, Y Lawrence Yao, David J Brenner
Affiliations

Automated Recognition of Robotic Manipulation Failures in High-throughput Biodosimetry Tool

Youhua Chen et al. Expert Syst Appl. 2012 Aug.

Abstract

A completely automated, high-throughput biodosimetry workstation has been developed by the Center for Minimally Invasive Radiation Biodosimetry at Columbia University over the past few years. To process patients' blood samples safely and reliably presents a significant challenge in the development of this biodosimetry tool. In this paper, automated failure recognition methods of robotic manipulation of capillary tubes based on a torque/force sensor are described. The characteristic features of sampled raw signals are extracted through data preprocessing. The twelve-dimensional (12D) feature space is projected onto a two-dimensional (2D) feature plane by the optimized Principal Component Analysis (PCA) and Fisher Discrimination Analysis (FDA) feature extraction functions. For the three-class manipulation failure problem in the cell harvesting module, FDA yields better separability index than that of PCA and produces well separated classes. Three classification methods, Support Vector Machine (SVM), Fisher Linear Discrimination (FLD) and Quadratic Discrimination Analysis (QDA), are employed for real-time recognition. Considering the trade-off between error rate and computation cost, SVM achieves the best overall performance.

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Figures

Figure 1
Figure 1
Layout of the rapid biodosimetry tool.
Figure 2
Figure 2
Prototype and processing sequence of the cell harvesting module (➀: pick up a capillary; ➁: detect separation band, read barcode, cut the capillary; ➂: dispense lymphocytes; ➃: dispose the empty capillary).
Figure 3
Figure 3
Force/torque sensor mounting and its Cartesian coordinates.
Figure 4
Figure 4
Failure modes on manipulating capillaries (A: failure because of last capillary not disposed (class1); B: failure because of small position misalignment (class2); C: failure because of big position misalignment (class3)).
Figure 5
Figure 5
Sampled raw force/torque data from the force/torque sensor in the Cartesian coordinates (force: Fx, Fy, Fz; torque: Tx, Ty, Tz).
Figure 6
Figure 6
Recognition procedure of robotic manipulation failures.
Figure 7
Figure 7
Time responses of Fz and Tx of the three failure modes.
Figure 8
Figure 8
Comparison of the feature extraction results of the different original statistical features (A: 12D statistical feature space; B: 8D statistical feature space; C: 6D statistical feature space; D: 4D statistical feature space). Their separability index ( J=Sb∗/Sw∗).
Figure 9
Figure 9
Comparison of the feature extraction methods between PCA (A) and FDA (B). Their separability index ( J=Sb∗/Sw∗).
Figure 10
Figure 10
PCA is not always best for pattern recognition. Projection on PCA direction makes the two classes coincide. While, projection on FDA direction keeps the classes separated.
Figure 11
Figure 11
Gaussian probability distribution of the three failure classes (A: normalized 2D probability density function, B: multivariable distribution ellipses in the 2D feature plane).
Figure 12
Figure 12
Classification for the testing data (A: SVM classifier, B: fisher linear classifier, C: quadratic classifier).
See this image and copyright information in PMC

References

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