摘要
针对传统 BP 神经网络在未知环境下机器人路径规划及避障算法中存在权值调节收敛速度慢、易陷入局部极小点、网络结构不稳定等问题，基于权值调节收敛速度与学习率之间的关系，通过引入调节因子及设置参数 k inc 和 k dec 对传统 BP 神经算法进行了改进，实现了学习率的大小动态调节，优化了权值的收敛．利用改进后 BP 神经网络算法，给出了一种新型机器人二级 BP 神经网络避障控制模型．仿真结果表明：该模型用于障碍物实时识别及机器人快速避障时有效可行，障碍物识别率达到80．5％～99．5％，避障路径趋近最优直线路径．
The conventional BP neural network has slow convergence of weight adjusting in robot obstacle avoidance algorithm.The relationship between weight vector and the convergence speed adjustment is investigated,the regulatory factor is introduced,and the k inc ,k dec parameters are set to implement dynamically adjusting the size of the vector to improve BP neural algorithm.A new two level BP neural network model is designed.This model can accurate identify the types of obstacles and fast achieve obstacle avoidance.The simulation results prove that the new network model and the improved algorithm can identify the type of obstacle by 80.5%～99.5%,obstacle avoidance path is closed to the optimal linear path.
出处
《西安工业大学学报》
CAS
2015年第8期678-682,共5页
Journal of Xi’an Technological University
基金
西安工业大学校长基金(XAGDXJJ1217)
关键词
BP
神经网络
避障控制模型
权值调节
学习率
BP neural network
obstacle avoidance control model
weight adjustment
training rate
