基于NN改进PSO算法的机器人路径规划

A application of using Neural Network to improve the PSO in robot path planning

本文介绍了基于神经网络和微粒群优化算法的移动机器人动态避障路径规划算法。通过神经网络改进的微粒群算法,充分利用了神经网络的融合性和并行性来提高微粒群算法中适应度函数的准确性。通过神经网络描述机器人工作空间的动态环境约束并找到最优的适应度函数,在微粒群算法中使用该函数,求得微粒群算法最优无碰路径。

This paper presents a mobile robot obstacle avoidance dynamic path planning algorithm based on neural network and Particle Swarm Optimization （PSO）. Using neural network to improve the PSO algorithm, and make integration and parallel of neural net- work to enhance the accuracy of PSO algorithm’s fitness function. Optimization function for the extreme, points to determine the equations of motion, and finally to the point iterative path tends to set the optimal path planning, use this function in PSO algorithm, finally get PSO obtained optimal collision path.