未知环境下基于模糊神经网络的机器人路径规划
Unknown Environment Based on Fuzzy Neural Network of Robot Path Planning
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摘要: 为提高移动机器人在未知环境下避障行为的成功率,通过对障碍物信息的输入,从控制输出数据中找出避障行为模式,生成相应的模糊逻辑控制规则,并把模糊控制算法引入到神经网络中,使得模糊控制器规则的在线精度和神经网络的学习速度均有较大的提高,使移动机器人具有较为迅速的反应能力,实现机器人连续、快速地避障并最终到达目标. 系统仿真证明了模糊神经网络在移动机器人路径选择中的智能性.Abstract: To enable the mobile robot in unknown environment obstacle-avoiding behavior of success, based on the information input of obstacles and from the control of output data to find a obstacle-avoiding behavior model, and To create the fuzzy logic rules, a fuzzy control algorithm is introduced to the neural network, allowing mobile robot more rapid response ability and to achieve a robot, and finally reach the target of obstacle avoidance. system simulation proves a fuzzy neural network in mobile robot path choice of intelligence.
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