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A Slope-Adaptive Navigation Approach for Ground Mobile Robots

  • China Agricultural University
  • Beijing University of Chemical Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The 2-dimensional cost map has been widely used for the navigation of ground mobile robot. Although it is effective when the ground is flat, it becomes clumsy and ineffective when the ground has some slopes, where such slopes are often misjudged as the forbidden area by the cost map. For this reason, we propose a slope-adaptive navigation approach based on multilayer cost map in this paper. Instead of taking the point cloud of slope as obstacles, we actively construct a multi-layer cost map that takes slope information into the map in the stage of building environment map. A slope detection algorithm is developed to switch the cost map during the robot navigation. The slope is then considered as a passable road, only with extra cost. In the case that the slope leads the robot to a new floor, we adopt the Aruco code to switch the map information, such that the navigation can still keep working. Both simulation and real-world experimental results demonstrate the high effectiveness of our proposed approach.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages610-615
Number of pages6
ISBN (Electronic)9781665452588
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Prague, Czech Republic
Duration: 9 Oct 202212 Oct 2022

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2022-October
ISSN (Print)1062-922X

Conference

Conference2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022
Country/TerritoryCzech Republic
CityPrague
Period9/10/2212/10/22

Keywords

  • layered cost map
  • navigation
  • target detection

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