摘要
为了解决足球机器人单一传感器所提供的定位数据的精度及稳定性不足以满足控制系统要求的问题,本文利用BP神经网络算法的学习功能,将目标足球及机器人自身状态信息作为标定数据,将视觉、加速度计、电子罗盘等多个传感器信息作为网络输入,以神经网络输出辅助足球机器人对目标足球的捕捉。实验仿真结果表明,神经网络算法提高了对目标足球的定位以及机器人自定位的精度,起到了预期的效果。滤波平均相对误差优于传统的卡尔曼滤波。
The accuracy and stability of position data, which is offered by the unique visual sensor, can not satisfy the requirement of control system. The learning function of the BP neutral network is used to solve this problem. The state information of football and robot is treated as sample data, the visual sensor, accelerometer and compass are treated as the input of network, and the outputs of the neutral network assist soccer robot to track football. Simulation shows that the neutral network improves the accura- cy of the location for football and robot. The expected result is obtained. The average relative error is superior to the traditional Kalinan filter.
出处
《计算机与现代化》
2009年第8期112-115,共4页
Computer and Modernization
