| Citrus is one of the most important economic crops in China,and citrus harvesting is seasonal,labour-intensive and labour-intensive.Therefore,it is of great significance to develop citrus picking robots to improve operational efficiency,reduce the labour intensity of agricultural workers and improve the level of intensive production of citrus.The key technologies of citrus picking robot end-effector include end-effector picking mode and method,machine vision fruit identification and positioning,picking posture control and route planning,which all directly affect the picking performance of citrus picking robot.In this thesis,a three-finger end-effector is developed based on the actual picking situation of citrus fruits,and the visual algorithm of the picking robot is optimized and the motion of the manipulator is planned.Finally,the indoor and field performance tests of the whole machine of the picking robot are carried out.The details of the study and the conclusions drawn are as follows.(1)The physical parameters of citrus fruits and fruit stalks were measured,and the mechanical characteristics of citrus fruits and fruit stalks were tested to provide a theoretical basis for the picking method and structural parameters of the citrus picking end-effector.The test results showed that the clamping force on the citrus fruit during harvesting should be less than 11.03 N.The test results showed that the average coefficient of static sliding friction between the citrus skin and the clamping finger under dry and wet conditions were The cutting mechanical characteristics of citrus fruit stalks were investigated on a self-built citrus stalk cutting test rig,and the relationship between the diameter of citrus fruit stalks,blade cutting speed,blade cutting clearance,blade sliding angle and peak cutting force was investigated by a single-factor test with the peak cutting force of citrus fruit stalks as the target value.The optimum combination of cutting parameters was obtained as blade cutting speed 140 mm/min,blade cutting clearance 1.5 mm and blade sliding angle 20°.In addition,the main factors affecting the peak cutting force were the blade cutting speed and the blade cutting clearance,and the secondary factors were the blade sliding angle.(2)The structure design of the citrus picking end-effector is based on the growth characteristics of citrus fruit.A three-finger end-effector was designed to fix the citrus fruit first and then pick it through the cutting method.A finite element analysis of the end-effector was first carried out to simulate fruit clamping and fruit stalk cutting to verify the picking effectiveness of the end-effector.The end-effector picking performance tests were then carried out.The test results showed that the end-effector was well adapted to various forms of citrus fruit and different angles of fruit stalks,and that the best picking performance was achieved at a stepper motor speed of 250 r/min,an end-effector speed of 160 mm/min and a picking angle of 0°.In addition,it was found that the factors affecting the picking performance of the endeffector were,in descending order,the end-effector speed,the stepper motor speed and the picking angle.(3)A citrus picking robot vision system was built.By modifying the Darknet-53 network structure of the YOLOv3 algorithm and using a multi-scale detection module,the ImprovedYOLOv3 model for fast recognition of citrus fruits in complex environments was obtained after image training.Using the Improved-YOLOv3 model for citrus fruit detection and recognition,more feature information can be extracted.The detection results show that the Improved-YOLOv3 model has good detection capability(detection rate,accuracy,mapping,detection speed)for the target fruit with high detection accuracy.In addition,a comparison with different algorithms shows that the Improved-YOLOv3 algorithm has strong robustness,high detection accuracy and short training time,and is able to identify citrus in complex environments.The ZED binocular depth camera was used to photograph the citrus fruit,and the depth information of the citrus fruit was derived.The ZED binocular depth camera was calibrated to complete the conversion of the 3D coordinates of the binocular camera to achieve accurate recognition and localization of the citrus fruit.(4)A six-degree-of-freedom picking robot arm platform was built.The coordinate transformation matrix of each linkage of the picking robot arm and the forward and inverse kinematic equations of the kinematic matrix of the picking robot arm were obtained by establishing the D-H coordinate system.The kinematic analysis of the picking robot arm was carried out using ADAMS kinetic simulation software,resulting in a working space of approximately 1.56 m3,which can meet the requirements for picking the whole fruit tree.The arm was simulated using the MATLAB robotics toolbox to obtain the trajectory of the endeffector and the velocity and acceleration of each joint.The motion parameters of each joint of the picking robot were analysed to find that the motion planning of the arm was suitable for citrus fruit picking.The robot arm and the end-effector control program were written in the ROS system,and the actual test showed that the path planning of the robot arm can meet the citrus picking well.(5)Overall performance tests of the picking robot were carried out.Through the assembly of the citrus picking robot vision system,picking robot arm,end-effector and control system to form the total integration of the picking robot,indoor tests on the comprehensive performance of the picking robot were conducted.The test results showed that the citrus picking robot had a picking success rate of 96% and a positioning success rate of 98% for the citrus fruit.In addition,the average time taken by the citrus picking robot to identify citrus fruits was 4.825 ms;the average movement time of the citrus picking robot arm was 5.337 s;the average picking time of the end-effector was 4.633 s.The time taken for the whole process of single fruit picking was 9.970 s.The picking performance was good,and all key systems worked well together during the picking operation of the picking robot,and the operation was relatively smooth,which could effectively complete the tasks of each part.The ZED binocular depth camera was able to locate the citrus fruit in real time.Field trials have shown that the success rate of citrus fruit identification is 94% and the success rate of picking is 86%.The results are comparable to those of current harvesting robots of the same type.Overall,the developed citrus picking robot is able to meet the natural environment of fruit picking. |
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