Forward kinematics of body posture perception using an improved BP neural network based on a quantum genetic algorithm

Abstract

Abstract Forward kinematics analysis of body posture perception is the basis for studying other performance of 6-degree of freedom parallel robot. Because forward kinematics involves many sets of nonlinear equations, it is usually difficult to solve. In this paper, an improved BP neural network (BPNN) based on a quantum genetic algorithm (GA) is designed to solve the forward kinematics problem. Additionally, we use the characteristics of easy calculation of inverse kinematics to generate a dataset for training and testing. Finally, through a large number of experiments, we show that the improved strategy of BPNN by quantum GA is effective, and the accuracy of the model we designed is high enough to solve the forward kinematics of body posture perception.