基于自生长网络的可行运动区域知识提取

Chaoliang Zhong, Shirong Liu, Xuena Qiu
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引用次数: 1

摘要

环境地图认知包括地图知识的提取和理解两个方面。针对智能机器人的环境理解问题,提出了一种基于自生长网络的移动机器人可行运动区域提取方法。该方法利用生长神经气体(Growing Neural Gas, GNG)算法的生长特性,通过增加拓扑网络的新节点,抽象出对周围环境的整体知识,构建出机器人易于理解、自主规划和决策的环境图。仿真和物理实验验证了该方法的可行性和有效性。
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Self-growing network based extraction of feasible motion region's knowledge
Environmental map cognition includes two issues on the map knowledge extraction and comprehension. For the environmental comprehension of intelligent robot, an extraction method of the feasible motion area for mobile robot is proposed based on a self-growing network. Using the growth characteristics of Growing Neural Gas (GNG) algorithm, this method can abstracts the holistic knowledge of the surrounding environment by adding new node of topology network and builds an environmental map in which robot can easily understand, autonomously plan and make a strategic decision. The simulations and physical experiments verify the feasibility and effectiveness of the proposed method.
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