Abstract: According to the issue of dynamic path plan of mobile robot in unknown environments from the start to the destination with obstacle avoid，a systemic neural-fuzzy control algorithm is proposed.Effective fuzzy logic control is designed to do the input fuzzification，fuzzy reasoning rule base，output defuzzification.The simplified structure of neural network handling the fuzzy control based on rule base and the corresponding simplified network parameters set are also designed.Train the network using QPSO，solve the“dead cycle” problem in U-shaped obstacle through the storage and management strategy of state variable of robot.Experimental results show that under the control of the proposed systemic algorithm，mobile robot can moving toward the target，avoiding all kinds of obstacles，dynamically planning reasonable path，and not getting into the dead cycle.

Key words: fuzzy neural-fuzzy control, dynamic path plan, Quantum Particle Swarm Optimization（QPSO）, state variable

摘要： 针对机器人在未知、复杂环境下从源到目标之间，避开各种类型的障碍的问题，设计了系统的神经-模糊控制算法进行动态路径规划：设计了合理的模糊推理体系，实现输入模糊化、模糊推理规则库、输出去模糊化控制；根据规则库设计神经网络结构，简化网络结构和参数；采用QPSO算法训练网络；状态变量的存储和管理策略，解决了“U”型障碍物内的死循环路径问题。实验结果表明，在以上算法的控制下，机器人能够朝着目标，规划产生合理的路径，不会陷入死循环。

关键词: 神经-模糊控制, 动态路径规划, 量子化粒子群优化算法, 状态变量