东南大学学报（自然科学版）2012，Vol.42Issue(4)：637-642,6.DOI:10.3969/j.issn.1001-0505.2012.04.012
基于自适应模糊神经网络的机器人路径规划方法
Path planning for mobile robot based on adaptive fuzzy neural network
摘要
Abstract
To solve the complex trap problems in the traditional reactive navigation and optimize navigation control with reduction of computational complexity, a navigation method that combines mobile robot navigation control based on adaptive fuzzy neural network and an improved virtual target path planning is proposed. First, a mobile robot controller based on the kinematic model combining the learning ability of neural network and the fuzzy reasoning of fuzzy control is designed, resulting in Takagi-Sugeno fuzzy system which is used as the reference model in local reaction control. The controller outputs disturbance angle for real-time adjustment of the direction of robot, and the mobile robot tends to the target without collision by means of the controller. Then, an improved virtual target method is applied to solve local trap problem. The robot may prefer the path to escape from the trap state. This approach can simplify the design difficulty, change the virtual target switching mode, and reduce a large number of complex calculations. The experimental results show that the proposed method can help the mobile robot navigate in unknown complex environments and approach the target without collisions and redundant paths, and the trajectory is smooth.关键词
自适应模糊神经网络/导航/陷阱问题/虚目标/路径规划Key words
adaptive fuzzy neural network/ navigation/ trap problem/ virtual target/ path planning分类
信息技术与安全科学引用本文复制引用
钱夔,宋爱国,章华涛,熊鹏文..基于自适应模糊神经网络的机器人路径规划方法[J].东南大学学报（自然科学版）,2012,42(4):637-642,6.基金项目
国家高技术研究发展计划(863计划)资助项目(2006AA04Z246)、教育部重大创新工程培育资金资助项目(708045). （863计划）
