For fruit picking robot, it is an essential prerequisite for achieving fruit picking using machine vision technology to accurately identify the fruits growing in the natural environment. This article presents a vision system of fruit picking robot to perform fruit location and three-dimensional model reconstruction. Firstly, combining the features of color and shape of fruit to reconstruct the actual contour of overlapped and sheltered fruits. Secondly, the least square method was used to reconstruct the three-dimensional model of each fruit according to the spatial coordinates corresponding to image location. Finally, fruit picking experiments in the laboratory environment are used to verify the feasibility of the proposed vision system. Three parameters including Segmentation Error, Intersection Over Union, and False Negative Rate are used to evaluate the performance of the algorithm. The average Segmentation Error, Intersection Over Union, and False Negative Rate of the fruit location algorithm based on geometry were 6.36%, 87.9%, and 6.72%, respectively. The experimental results showed that the average computation time of the algorithm is 3.2 s and the reconstructed three-dimensional model matched the size and position of fruits in the actual scene. The research results can be applied to the vision system of fruit picking robot.

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The design, fabrication, and testing of soft sensors that measure elastomer curvature and mechanical finger bending are described in this study. The base of the soft sensors is polydimethylsiloxane (PDMS), which is a translucent elastomer. The main body of the soft sensors consists of three layers of silicone rubber plate, and the sensing element is a microchannel filled with galliumindium-tin (Ga-In-Sn) alloy, which is embedded in the elastomer. First, the working principle of soft sensors is investigated, and their structure is designed. Second, the relationship between curvature and resistance is determined. Third, several sensors with different specifications are built in accordance with the structural design. Experiments show that the sensors exhibit high accuracy when the curvature changes within a certain range. Lastly, the soft sensors are applied to the measurement of mechanical finger bending. Experiments show that soft curvature sensors can effectively reflect mechanical finger bending and can be used to measure the bending of mechanical fingers with high sensitivity within a certain working range.

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