Evolutionary computing based mobile robot localization

被引：43

作者：

Kwok, N. M. ^{[1
]}

Liu, D. K. ^{[1
]}

Dissanayake, G. ^{[1
]}

机构：

[1] Univ Technol Sydney, Fac Engn, ARC Ctr Excellence Autonomous Syst CAS, Broadway, NSW 2007, Australia

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2006年 / 19卷 / 08期

基金：

澳大利亚研究理事会;

关键词：

evolutionary computing; mobile robot; localization;

D O I：

10.1016/j.engappai.2006.01.020

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 [计算机科学与技术];

摘要：

Evolutionary computing techniques, including genetic algorithms (GA), particle swarm optimization (PSO) and ants system (AS) are applied to the localization problem of a mobile robot. Salient features of robot localization are that the system is partially dynamic and information for fitness evaluation is incomplete and corrupted by noise. In this research, variations to the above three evolutionary computing techniques are proposed to tackle the specific dynamic and noisy system. Their performances are compared based on simulation and experiment results and the feasibility of the proposed approach to mobile robot localization is demonstrated. (c) 2006 Elsevier Ltd. All rights reserved.

引用

页码：857 / 868

页数：12

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