Spatial abstraction for autonomous robot navigation

被引：3

作者：

Epstein, Susan L. ^{[1
,2
]}

Aroor, Anoop ^{[2
]}

Evanusa, Matthew ^{[1
]}

Sklar, Elizabeth I. ^{[3
,4
]}

Parsons, Simon ^{[3
]}

机构：

[1] CUNY, Hunter Coll, Dept Comp Sci, New York, NY 10021 USA

[2] CUNY, Grad Ctr, Dept Comp Sci, New York, NY USA

[3] Univ Liverpool, Dept Comp Sci, Liverpool, Merseyside, England

[4] Kings Coll London, Dept Comp Sci, London WC2R 2LS, England

来源：

COGNITIVE PROCESSING | 2015年 / 16卷

基金：

美国国家科学基金会;

关键词：

Spatial model; Spatial abstractions; Qualitative reasoning; Robot navigation;

D O I：

10.1007/s10339-015-0713-x

中图分类号：

B84 [心理学];

学科分类号：

04 ; 0402 ;

摘要：

Optimal navigation for a simulated robot relies on a detailed map and explicit path planning, an approach problematic for real-world robots that are subject to noise and error. This paper reports on autonomous robots that rely on local spatial perception, learning, and common-sense rationales instead. Despite realistic actuator error, learned spatial abstractions form a model that supports effective travel.

引用

页码：S215 / S219

页数：5

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