The Inverse Solution Algorithm and Trajectory Error Analysis of Robotic Arm Based on MQACA-RBF Network

Xu Cheng,Ming Zhao

doi:10.1155/2020/7807952

Abstract

In order to improve the position accuracy and trajectory accuracy of a 6R robotic arm, a robot arm inverse solution algorithm based on the MQACA- (improved quantum ant colony-) RBF network is proposed. This algorithm establishes the prediction model through the neural network and uses the quantum ant colony algorithm to optimize the output weight. In order to solve the problem that the quantum ant colony algorithm has low convergence precision and easy to fall into the local optimal solution in the inverse solution algorithm of the multifreedom robotic arm, improved measures such as 2-opt local optimization and maximum minimum pheromone limit and variation are adopted. By comparing the simulation results of the 6R robotic arm simulation results and the simulation results based on ACA, QACA, and RBF neural networks on the position and motion trajectory of the space point, the advantages in precision are obvious. This proves the feasibility and effectiveness of the scheme.

Highlights

In modern manufacturing, the robotic arm is a more important electromechanical integration device
In order to adapt to different trajectory planning tasks, the robotic arm will be adjusted which is closely related to the diversification of production
Quantum Ant Colony Optimization Algorithm (QACA) and RBF neural networks are combined to complement each other’s advantages and make full use of the efficient optimization capability of the quantum ant colony, and improvement measures are proposed to form the inverse solution algorithm of the mechanical arm based on the MQACA-RBF network

Summary

Introduction

The robotic arm is a more important electromechanical integration device. QACA and RBF neural networks are combined to complement each other’s advantages and make full use of the efficient optimization capability of the quantum ant colony, and improvement measures are proposed to form the inverse solution algorithm of the mechanical arm based on the MQACA-RBF network. On the basis of the maximum minimum pheromone limit and the optimal path of each ant colony, the genetic algorithm (mutation operation) is used to reduce the local optimal solution problem in the search process of the quantum ant colony algorithm. Step 9: assuming that the optimal adaptive function value and the maximum number of iterations of the ant population have not changed too much, the optimal model parameters can be obtained, and enter step (11). Where z—parameters before normalization; zmin—minimum parameter; zmax—maximum parameter; and z′—normalized parameters

Simulation Experiment and Error Analysis of 6R Manipulator

Error Analysis of Motion Trajectory

Findings

Conclusion