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首页> 外文期刊>IEEE Transactions on Neural Networks >Prune-Able Fuzzy ART Neural Architecture for Robot Map Learning and Navigation in Dynamic Environments
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Prune-Able Fuzzy ART Neural Architecture for Robot Map Learning and Navigation in Dynamic Environments

机译：适用于动态环境中机器人地图学习和导航的准修剪模糊ART神经体系结构

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Mobile robots must be able to build their own maps to navigate in unknown worlds. Expanding a previously proposed method based on the fuzzy ART neural architecture (FARTNA), this paper introduces a new online method for learning maps of unknown dynamic worlds. For this purpose the new Prune-able fuzzy adaptive resonance theory neural architecture (PAFARTNA) is introduced. It extends the FARTNA self-organizing neural network with novel mechanisms that provide important dynamic adaptation capabilities. Relevant PAFARTNA properties are formulated and demonstrated. A method is proposed for the perception of object removals, and then integrated with PAFARTNA. The proposed methods are integrated into a navigation architecture. With the new navigation architecture the mobile robot is able to navigate in changing worlds, and a degree of optimality is maintained, associated to a shortest path planning approach implemented in real-time over the underlying global world model. Experimental results obtained with a Nomad 200 robot are presented demonstrating the feasibility and effectiveness of the proposed methods.
机译：移动机器人必须能够构建自己的地图，以便在未知的世界中导航。扩展了先前提出的基于模糊ART神经体系结构（FARTNA）的方法，本文引入了一种新的在线方法来学习未知动态世界的地图。为此，引入了新的可修剪模糊自适应共振理论神经体系结构（PAFARTNA）。它通过提供重要动态适应功能的新颖机制扩展了FARTNA自组织神经网络。制定并证明了PAFARTNA的相关性能。提出了一种感知物体去除的方法，然后将其与PAFARTNA集成。所提出的方法被集成到导航架构中。借助新的导航架构，移动机器人能够在不断变化的世界中导航，并保持一定程度的最优性，这与在底层全球世界模型上实时实施的最短路径规划方法相关。提出了用Nomad 200机器人获得的实验结果，证明了所提出方法的可行性和有效性。

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