Research on Convex Fiber Grating Tactile Sliding Sensor Based on Mechanical Fingers.

In order to solve the problem of flexible sliding tactile composite sensing in the actual grasp of intelligent robot fingers, this paper proposes a research on a convex fiber grating tactile sliding sensor based on mechanical fingers. Based on the sensing principle of fiber Bragg grating, 3D printing technology was used to encapsulate the FBG sensor array with elastic 50 A resin, a double-layer "hemispherical cuboid" distributed sensing unit was designed, and the FBG slippery tactile sensor was actually pasted on the surface of the mechanical finger for static and dynamic experiments. The experimental results show that the slippery tactile sensor designed in this paper has a good linear relationship with temperature and strain. The temperature sensitivities of the polymer-packaged FBGs are KT1 = 13.04 pm/°C and KT2 = 12.91 pm/°C, and they have a pressure sensitivity of 40.4 pm/N and 31.2 pm/N, respectively. The FBG sliding tactile sensor not only realizes the identification of the sliding signal generation point and the end point but also completes the classification and identification of sandpaper, cardboard, and polypropylene plastic, and it has a high degree of fit with the robot finger, which has certain application value for the intelligent robot sliding tactile signal perception.

Research on Finger Pressure Tactile Sensor with Square Hole Structure Based on Fiber Bragg Grating.

Aiming at the problems of lateral force interference and non-uniform strain of robot fingers in the process of pressure tactile sensing, a flexible tactile sensor with a square hole structure based on fiber Bragg grating (FBG) is proposed in this paper. Firstly, the optimal embedding depth of the FBG in the sensor matrix model was determined by finite element simulation. Secondly, according to the size of the finger knuckle and the simulation analysis based on the pressure tactile sensor element for the robot finger, the square hole structure was designed, and the overall dimensions of the sensing element and size of the square hole were determined. Thirdly, the FBG was embedded in the polydimethylsiloxane (PDMS) elastic matrix to make a sensor model, and the tactile sensor was fabricated. Finally, the FBG pressure tactile sensing system platform was built by using optical fiber sensing technology, and the experiment of the FBG tactile sensor was completed through the sensing system platform. Experimental results show that the tactile sensor designed in this paper has good repeatability and creep resistance. The sensitivity is 8.85 pm/N, and the resolution is 0.2 N. The loading sensitivity based on the robot finger is 27.3 pm/N, the goodness of fit is 0.996, and the average value of interference in the sensing process is 7.63%, which is lower than the solid structure sensor. These results verify that the sensor can effectively reduce the lateral force interference and solve the problem of non-uniform strain and has high fit with fingers, which has a certain application value for the research of robot pressure tactile intelligent perception.

Design and Experimental Research of Robot Finger Sliding Tactile Sensor Based on FBG.

Aiming at the problem of flexible sliding tactile sensing for the actual grasp of intelligent robot fingers, a double-layer sliding tactile sensor based on fiber Bragg grating (FBG) for robot fingers is proposed in this paper. Firstly, the optimal embedding depth range of FBG in the elastic matrix of polydimethylsiloxane (PDMS) was determined through finite element analysis and static detection experiments of finger tactile sensing. Secondly, the sensor structure is optimized and designed through the simulation and dynamic experiments of sliding sensing to determine the final array structure. Thirdly, the sensing array is actually pasted on the surface of the robot finger and the sensing characteristics testing platform is built to test and analyze the basic performance of the sliding tactile sensor. Then, the sensor array is actually attached to the finger surface of the robot and the sensing characteristics testing platform is built to experiment and analyze the basic performance of the sliding tactile sensor. Finally, a sliding tactile sensing experiment of robot finger grasping is conducted. The experimental results show that the sliding tactile sensor designed in this paper has good repeatability and creep resistance, with sensitivities of 12.4 pm/N, 11.6 pm/N, and 14.5 pm/N, respectively, and the overall deviation is controlled within 5 pm. Meanwhile, it can effectively sense the signals of the robot fingers during static contact and sliding. The sensor has a high degree of fit with the robot finger structure, and has certain application value for the perception of sliding tactile signals in the object grasping of intelligent robot objects.

Evaluation of S100A4 mRNA in EUS-FNA specimens for the assessment of chemosensitivity to gemcitabine from patients with unresectable pancreatic cancer.

BACKGROUND/AIMS: Gemcitabine (GEM) is the first-line chemotherapy in patients with unresectable pancreatic cancer. However, the clinical outcomes of this regimen are still unsatisfactory in prolonging survival. Resistant to GEM is one of the reasons for poor prognosis. Therefore, looking for molecular biomarkers to predict chemosensitivity to GEM is important for treatment in unresectable pancreatic cancer patients. The aim of this study was to analyze S100A4 mRNA in tissues of unresectable pancreatic cancer obtained by endoscopic ultrasound-guided fine-needle aspiration biopsy (EUS-FNA), and to determine the relation between S100A4 mRNA level and chemosensitivity to GEM. METHODS: The analysis was performed on samples from 36 patients with unresectable pancreatic cancer who were treated with gemcitabine alone. The patients were assigned to receive GEM at 1,000 mg/m(2)/wk for weeks 1 to 6, followed by 1 week rest, then for 4 weeks. mRNA was extracted for S100A4 mRNA assay from patients above by EUS-FNA before GEM-treatment. The 36 patients were divided into the following two groups. Patients with partial response and those with stable disease whose tumor markers decreased by 50% or more were classified as the effective group. The rest of patients were classified as the non effective group. The relationship between GEM efficacy and S100A4 mRNA expression was then examined by chi-squared test. RESULTS: S100A4 mRNA showed a significant correlation with GEM efficacy. Patients in the effective group had low S100A4 mRNA expression, whereas patients in non-effective group had high S100A4 mRNA expressions (P = 0.0059). CONCLUSION: S100A4 mRNA level analyzed in EUS-FNA samples is an important molecular biomarker for prediction of chemosensitivity to GEM in unresectable pancreatic cancer.