Research State and Trend of Fruit Picking Robot Manipulator Structure

Starting from the characteristics of fruit picking,the characteristics of fruit picking robot manipulators and the research state at home and abroad are reviewed.The analysis summarizes the difficulties in fruit picking robotic arm research.Aiming at the configuration of the manipulator,the structure and characteristics of the manipulator with redundant degrees of freedom are introduced,and the feasibility of the redundant mechanism is demonstrated through the current research state of the manipulator.

Review of rigid fruit and vegetable picking robots

The important indicators to measure the goodness of rigid fruit and vegetable picking robot have two aspects,the first is the reasonable structural design of the end-effector,and the second is having a high precision positioning recognition method.Many researchers have done a lot of work in these two aspects,and a variety of end-effector structures and advanced position recognition methods are constantly appearing in people’s view.The working principle,structural characteristics,advantages and disadvantages of each end-effector are summarized to help us design better fruit and vegetable picking robot.The authors start from the rigid fruit and vegetable picking robot grasping methods,separation methods,and position recognition methods,firstly introduce three different grasping methods and the characteristics of the two separation methods,then introduce the under-driven picking robot developed on the basis of the traditional rigid picking robot,then explain the single special,multi-feature,and deep learning location position recognition methods currently used,and finally make a summary and outlook on the rigid fruit and vegetable picking robot from the structural development and position recognition methods.

Review of smart robots for fruit and vegetable picking in agriculture

The fruit and vegetable picking has posed a great challenge on the production and markets during the harvest season.Manual picking cannot fully meet the rapid requirements of each market,mainly due to the high labor-intensive and time-consuming tasks,even the aging and shortage of agricultural labor force in recent years.Alternatively,smart robotics can be an efficient solution to increase the planting areas for the markets in combination with changes in cultivation,preservation,and processing technology.However,some improvements still need to be performed on these picking robots.To document the progress in and current status of this field,this study performed a bibliometric analysis.This analysis evaluated the current performance characteristics of various fruit and vegetable picking robots for better prospects in the future.Five perspectives were proposed covering the robotic arms,end effectors,vision systems,picking environments,and picking performance for the large-scale mechanized production of fruits and vegetables in modern agriculture.The current problems of fruit and vegetable picking robots were summarized.Finally,the outlook of the fruit and vegetable picking robots prospected from four aspects:structured environment for fruit planting,the algorithm of recognition and positioning,picking efficiency,and cost-saving picking robots.This study comprehensively assesses the current research status,thus helping researchers steer their projects or locate potential collaborators.

Tracking and recognition algorithm for a robot harvesting oscillating apples

Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying process,tracking and recognition method combined with an affine transformation was proposed.The method can be divided into three steps.First,the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue(2R-G-B)color feature;after improving the binary image,the apples were recognized with a local parameter adaptive Hough circle transformation method,thus improving the accuracy of recognition and avoiding the long,time-consuming process and excessive fitted circles in traditional Hough circle transformation.The process and results were verified experimentally.Second,the Shi-Tomasi corners detected and extracted from the first frame image were tracked,and the corners with large positive and negative optical flow errors were removed.The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm(RANSAC)to correct the scale of the template image and predict the apple positions.Third,the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm.The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8%and 20%in the absence of template correction and apple position prediction,respectively.In comparison,the running time of our algorithm was 25 ms,and the tracking error was less than 4%.Therefore,test results indicate that speed and efficiency can be greatly improved by using our method,and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits.