Full Content is available to subscribers

Subscribe/Learn More  >

Robot Localization Using Fuzzy Logic

[+] Author Affiliations
Avinash G. Dharne, Suhada Jayasuriya

Texas A&M University

Paper No. IMECE2005-81052, pp. 971-976; 6 pages
  • ASME 2005 International Mechanical Engineering Congress and Exposition
  • Dynamic Systems and Control, Parts A and B
  • Orlando, Florida, USA, November 5 – 11, 2005
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 0-7918-4216-9 | eISBN: 0-7918-3769-6
  • Copyright © 2005 by ASME


Robot Localization is an issue of vital importance for the functioning of autonomous mobile robots. Location information, allows a robot to navigate complex environments and perform local tasks successfully. In mobile sensor networks, this information facilitates important functions like topology control, collision avoidance and development and security of routing protocols. This issue can be divided into the problems of global position estimation, and once that is achieved, of local position tracking. To tackle these, two distinct methods have been used in the past. One is the use of specialized hardware and another is the use of probabilistic Bayesian estimation methods. This paper proposes the use of Fuzzy Logic to tackle this problem. Fuzzy Logic allows us to do away with strict probabilistic rules and to set up heuristic fuzzy rules. It also reduces computation time. A grid-based map is used to describe the environment of the robot and the robot’s confidence in it’s position at each grid-point is determined using sensor measurements. In case the robot is receiving information from multiple sensors, this paper demonstrates the robustness of the scheme to inaccurate sensor information or robot confidence within practical limits. This paper also applies the fuzzy rules to track the robot’s position as it moves. In order to reduce computational cost, this paper proposes limiting the computation of confidences to significant grid-points only.

Copyright © 2005 by ASME
Topics: Robots , Fuzzy logic



Interactive Graphics


Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In