Research On Key Technology Of Tomato Harvesting Robot

Fruit picking is the most time-consuming and laborious work during the agricultural production.It belongs to labor-intensive work,and urgently needs to realizethe automatic operation to improve its labor efficiency.Thus,some key technologies for tomato picking robot were systematically investigatedin this paper,such as chassis control method,picking robot arm,tomato automatic identification and positioning.A novel picking robot with the functions of automatic navigation,automatic identification and positioning to fruit target was finallydeveloped.The main contents and results were as follows:(1)The tomato picking robot system was overall designed.According to the picking requirements for the mature tomato,the basic structure and working principle of the tomato harvesting robot were analyzed;Using mechanical and electrical engineering design methods,sensor and detection technologies,automatic control and multi information fusion technology,the overall structure of the tomato harvesting robot system was designed,which consisted of four wheel type walking system,four-degree-of-freedom manipulator,binocular visual recognition and positioning system,laser automatic navigation system.(2)Using the modular design method and the embedded control system theory,the four-wheel independent steering platform was developed,and the kinematics analysis of the mobile platform was carried out.The kinematic model of the speed,angular velocity and deflection angle of tomato picking robot wasestablished.Especially,under the consideration of reasonable power allocation and control accuracy,a four-wheel independent steering control system was designed by using the modular robot system design method.Based on the low speed Ackerman steering principle,the robothad a function of four wheel independent steering.(3)The laser navigation control system was developed.The working principle and measurement error of laser sensor,sampled data fusion algorithm and navigation control method were analyzed.A method of data fusion based on improved UKF algorithm waspresented,and an automatic laser navigation control method based on adaptive PID algorithm was proposed.(4)Asmart tandem typeof four-degreeoffreedom manipulator was developed.Based on the analysis of tomato picking work space,the parameters of each joint were calculated and the specific sizes of each joints of robot arm weredetermined.Based on Monte Carlo method,the working space of tomato harvesting manipulator wasdetermined.On the established D-H coordinate system,the kinetic analysis,simulation analysis and inverse dynamics calculationof the tomato picking manipulator werecarried out.Based on the joint motion planning of the joint space,the electrical structure and the software control interface of the manipulator were designed.(5)The tomato automatic identification and positioning technology were studied.With the aid of testing and construction of tomato related biological characteristics,the technical way of recognizing tomato with color characteristics was determined.Based on the analysis of tomato fruit characteristics and image segmentation method,a Niblack algorithm based on adaptive adjustment correction coefficient was proposed to extract tomato target.The fruit elliptic template matchingmethod,the three-dimensional image reconstruction method and the method for picking path planning of tomato fruit were studied.(6)The individual and overall performance test for each function modules and the whole tomato picking robot system respectively.The individual performance tests included mobile platform corner control test,robot arm positioning accuracy test,bearing capacity test ofmechanical arm,error test ofmature tomato identification.The experimental results showed that the maximum absolute error of the moving wheel angle control was 0.10° during the rotation of 0~360°,and the corresponding standard deviation was not more than 0.03°.The control system was verystable and the corner control hada high precision.The maximum error of robot positioning accuracy was 2.5mm;the average error was 1.1mm,theload capacity of machine could reached to 3.5kg,and the recognition rate of binocular stereoscopic vision system was99%.When the recognition distance was less than 600 mm,the positioning error was less than 10 mm.The overall test focused on the laser navigation control system test and the tomato picking success rate test,whose resultindicated that the response speed of laser navigation control system was so fast that the yaw error was less than 8cm.The average time to pick a single tomato was within15 s,the picking success rate was more than 86.7%.