基于BP神经网络的移动机器人避障设计及仿真

为了解决移动机器人在复杂环境中如何高效精确地躲避障碍物的问题，提出了一种基于BP神经网络的避障方法。建立了机器人的避障运动模型并设计了神经网络避障控制系统；分析了机器人在运动过程中与障碍物的位置关系，使用超声波传感器采集距离信息，进行BP神经网络输入、输出训练并采用Matlab工具进行仿真试验。结果表明，该方法可以高效精确地实现移动机器人的自主避障，运行相对稳定、轨迹连续平滑，达到了较为理想的避障效果。验证了方法的可行性和有效性，为移动机器人自主避障提供了一种新的控制方法。

Obstacle Avoidance Design and Simulation of Mobile Robot Based on BP Neural Network

In order to solve the problem of how to avoid obstacles efficiently and accurately for mobile robots in a complex environment,an obstacle avoidance method based on BP neural network is proposed.The obstacle avoidance motion model of the robot was established,and the neural network obstacle avoidance control system was designed.The position relationship between the robot and the obstacle during its movement was analyzed,the distance information was collected by ultrasonic sensor,the input and output training of the BP neural network was carried out,and the simulation test was carried out by Matlab.The results show that,this method can realize the autonomous obstacle avoidance of mobile robot effectively and accurately,with relatively stable operation and continuous and smooth trajectory,and a better obstacle avoidance effect is achieved.The feasibility and effectiveness of the method are verified,which provides a new control method for autonomous obstacle avoidance of mobile robot.