Exploration of Spiral Structure Motion Based on Genetic Algorithm and Feedforward Neural Networks

The aim of this paper is to explore the motion process of the spiral structure and propose an optimization model based on genetic algorithm and feed-forward neural network. The study firstly establishes a mathematical model of the spiral structure to describe the motion trajectory of the head of the spiral structure, and also derives the velocity of each node body to simulate the motion behavior of the spiral structure. Secondly, the collision risk between the knuckles is further analyzed, and the termination moment of the disk-in is determined by using the Euclidean distance constraint. In addition, this paper optimizes the minimum pitch of the spiral structure by genetic algorithm in order to enter the turnaround space smoothly. Finally, for the optimization of the turnaround path, a feed-forward neural network was used for iterative training, which successfully shortened the length of the turnaround curve. The results of this study not only provide theoretical support for the design and application of spiral structures, but also open up a new direction for motion optimization research in related fields.