International Journal of Artificial Life Research最新文献

筛选
英文 中文
Topological Gaussian ARTs with Short-Term and Long-Term Memory for Map Building and Fuzzy Motion Planning 具有短期和长期记忆的拓扑高斯艺术用于地图构建和模糊运动规划
International Journal of Artificial Life Research Pub Date : 2016-07-01 DOI: 10.4018/IJALR.2016070104
Chin Wei Hong, L. C. Kiong, Kubota Naoyuki
{"title":"Topological Gaussian ARTs with Short-Term and Long-Term Memory for Map Building and Fuzzy Motion Planning","authors":"Chin Wei Hong, L. C. Kiong, Kubota Naoyuki","doi":"10.4018/IJALR.2016070104","DOIUrl":"https://doi.org/10.4018/IJALR.2016070104","url":null,"abstract":"This paper proposes a cognitive architecture for building a topological map incrementally inspired by beta oscillations during place cell learning in hippocampus. The proposed architecture consists of two layer: the short-term memory layer and the long-term memory layer. The short-term memory layer emulates the entorhinal and the ? is the orientation system; the long-term memory layer emulates the hippocampus. Nodes in the topological map represent place cells robot location, links connect nodes and store robot action i.e. adjacent angle between connected nodes. The proposed method is formed by multiple Gaussian Adaptive Resonance Theory to receive data from various sensors for the map building. It consists of input layer and memory layer. The input layer obtains sensor data and incrementally categorizes the acquired information as topological nodes temporarily short-term memory. In the long-term memory layer, the categorized information will be associated with robot actions to form the topological map long-term memory. The advantages of the proposed method are: 1 it is a cognitive model that does not require human defined information and advanced knowledge to implement in a natural environment; 2 it can generate the map by processing various sensors data simultaneously in continuous space that is important for real world implementation; and 3 it is an incremental and unsupervised learning approach. Thus, the authors combine their Topological Gaussian ARTs method TGARTs with fuzzy motion planning to constitute a basis for mobile robot navigation in environment with slightly changes. Finally, the proposed approach was verified with several simulations using standardized benchmark datasets and real robot implementation.","PeriodicalId":364692,"journal":{"name":"International Journal of Artificial Life Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123143241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信