Autonomous Cross-Floor Navigation System for a ROS-Based Modular Service Robot

Wen Hsin Wang, Yi Hsing Chien, Hsin Han Chiang, Wei Yen Wang, Chen Chien Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, we present an autonomous cross-floor navigation system including mapping, localization, path planning, and scene recognition based on robot operating system (ROS) architecture. The Gmapping algorithm is utilized to build a 2D map with a laser range-finder, and AMCL algorithm is utilized in the robot localization. Moreover, an improved A∗ algorithm is proposed to prevent robot from getting too close to the wall. Because our robot needs to navigate in the multi-floor environment, a decision system using deep convolutional neural network (DCNN) is also designed to recognize the current floor and the associated map can be download to the robot system. By training with the scene images of the featured location in each floor, the robot can recognize the current floor and then complete the navigation task. Finally, real test of our robot is conducted to demonstrate the feasibility of the proposed method.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728128160
DOIs
Publication statusPublished - 2019 Jul
Event18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 - Kobe, Japan
Duration: 2019 Jul 72019 Jul 10

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2019-July
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
Country/TerritoryJapan
CityKobe
Period2019/07/072019/07/10

Keywords

  • Cross-floor navigation system
  • Deep convolutional neural network (DCNN)
  • Modular service robot
  • ROS architecture

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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