Tea Picking Robotic Arm Based On Grey Wolf Optimization Algorithm Path Planning Research

Due to the low picking efficiency and high labor cost,the traditional manual tea picking has failed to meet today’s development needs.Therefore,finding an efficient and accurate tea picking method to replace the traditional manual work has become an urgent need for the development of tea industry.The result of the path planning of the picking robot directly affects the efficiency of the picking operation,which is one of the key technologies of the famous tea picking robot.In this thesis,we take tea shoots as the research object,and take tea picking path planning as the research content,based on Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Grey Wolf Optimizer(GWO)and the improved Grey Wolf Optimization algorithm for the global path planning of tea shoots under different distribution scenarios.The main research contents are as follows:(1)Establish the kinematic model of the robotic arm by using the D-H method,carry out the analysis of the model positive and negative kinematics,and provide the basis for the control of the picking robotic arm and the planning research of the picking path.(2)To establish a three-dimensional picking point model with random distribution,uniform distribution and Long-type distribution of buds and leaves under natural scenes,and to plan the picking path using genetic algorithm,particle swarm algorithm and gray wolf optimization algorithm.The experimental results show that the gray wolf optimization algorithm is shorter in the optimal path as well as the average path compared with the other two,but there is a slow convergence speed.By introducing chaotic initialization,population interaction mechanism and adaptive inertia weight mechanism to improve the gray wolf optimization algorithm to improve the convergence speed and the accuracy of finding the best,the optimized gray wolf optimization algorithm shortens about 4.7% in the optimal path length and about 25.4% in the number of iterations.(3)Experimental system design for tea shoot picking.Based on the XCR5 robotic arm platform,eye-to-hand was selected to realize the hand-eye calibration of the robotic arm,and the depth camera was used as the vision system design to complete the construction of the picking experimental system.The feasibility of the tea picking robot is verified through picking experiments,and the path planning experiments of the picking robotic arm are implemented on the picking system.The experimental results show that a better picking path can improve the picking efficiency to a certain extent and verify the effectiveness of the path planning algorithm in this thesis.