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Abstract
A study was conducted to evaluate the surface roughness levels of fruits and vegetables by a novel tactile sensor. Firstly, the effective areas of the sensor were determined through the mechanical analysis with the ANSYS software, and the sensitive elements of polyvinylidene fluoride piezoelectric films and strain gauges were randomly arranged in these areas. When the sensor contacted with the surfaces of the fruits and vegetables, the signals produced by the sensitive elements were output and tactile features were obtained. Secondly, the D-score criterion was applied to evaluate the contribution of every tactile feature component in expressing the surface roughness levels. According to the value of D-score, the strategies of the sequential forward selection and equential forward floating selection were used to guide the optimization of feature components selection. Back propagation neural network model was applied to evaluate the performance of the optimal features. Finally, the experimental results revealed that the identification accuracy of the algorithm was up to 93.737%, which demonstrated that the optimal feature subsets possessed fewer dimensions while maintaining a high performance in expressing the surface roughness characteristics of the fruits and vegetables. The results also provided a basis for the optimized design of the tactile sensor.
Highlights
The application of agricultural robots in picking, sorting, grafting, and other tasks is becoming more and more extensive.[1,2,3] End effectors of the robots are the key to determine the quality of the work
The best subset of features wasn’t the same, there was intersection between two optimal subsets. These feature components were all from polyvinylidene fluoride (PVDF)-sensitive elements, it is can be seen that the effectiveness of PVDF-sensitive elements was better than strain gauges in detecting the surface roughness characteristics of the fruits and vegetables
A tactile sensor was produced by randomly arranging the sensitive elements, which can simplify the sensor design and make it adapt to different grasping tasks
Summary
The application of agricultural robots in picking, sorting, grafting, and other tasks is becoming more and more extensive.[1,2,3] End effectors of the robots are the key to determine the quality of the work. Takamuku et al.[16] used the PVDF piezoelectric film and strain gauge to designed robotic tactile fingers and detected the surface roughness characteristics by touching way. By controlling the 2-dimensional motion of the finger-shaped sensor along the object surface, the output charge of PVDF film changes with the small height/depth variation of surface texture. These surface roughness detection were carried out in different ways, the structure and layout of the tactile sensor were a little complex. The focus of our research is to design a tactile sensor with the PVDF piezoelectric films and strain gauges in a simple way to estimate the surface roughness of fruits and vegetables. A group of signals of the PVDF piezoelectric films and strain gauges for each sample are shown in the Figure 6
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