Research and implementation of intelligent vehicle path planning based on four-layer neural network

Abstract

In order to solve the problem of path planning and roadblock avoidance in the process of smart car walking, this paper proposes a path planning algorithm based on neural network to realize the planning of the collision path of smart car in unknown environment. Since the optimal path planning of the smart car is to automatically find a collision-free path from the initial state to the target state when it moves in an environment with obstacles. The algorithm is a neural network algorithm using a four-layer network structure and the energy function is designed as an evaluation function of the network. By looking for the extremum of the energy function, the AGV smart car adjusts the trolley according to the trend of the path point set. Move to complete the path planning task. MATLAB simulation can be used to verify the accuracy of the algorithm. The results of the obstacle avoidance planning algorithm, a smooth path and speed in the ideal state. It provides a certain reference for mobile robots, unmanned vehicles and other applications in cargo transportation, intelligent driving and military applications.