织机车间直捻机筒纱抓取机械臂逆运动学求解方法研究OA

In the textile workshop,the automatic replacement of cylindrical yarn packages on the twisting ma-chine is achieved by using a collaborative robotic arm instead of human workers.It can reduce the labor intensi-ty of workers and improve the production efficiency of winding yarn packages.In the process of modeling and analyzing the robot's motion,the solution to inverse kinematics is a critical aspect of robot kinematics.Tradition-al methods for solving the inverse kinematics of robots suffer from issues such as slow computation speed,com-plex solving processes,and poor stability of results.Utilizing the traditional Back Propagation Neural Network(BPNN)for solving also tends to get stuck in local minima.To address these challenges,this paper proposes a method for solving the inverse kinematics of a robotic arm based on the Particle Swarm Optimization Algorithm(PSO)optimized Backpropagation(BP)neural network.Through multiple iterations using the PSO algorithm,the weights and thresholds of the BP neural network are optimized,preventing it from getting stuck in local min-ima and enhancing its global search capabilities.The robot's kinematic model is established using the Denavit-Hartenberg(D-H)method,and the end-effector pose is obtained by solving joint angles through the robot's for-ward kinematic equations.The results obtained serve as a dataset,and the model parameters of the neural net-work are determined through multiple iterations using a learning algorithm,followed by performance testing.Experimental results indicate that the PSO-BP neural network converges faster compared to the traditional BP neural network.The model exhibits high precision in solving the inverse kinematics of the material handling ro-bot,meeting the requirements for yarn-grabbing operations in the textile workshop.