Low-Cost Indoor Location Management for Robots Using IR Leds and an IR Camera

Abstract

Many applications in wireless sensor networks can benefit from position information. However, existing accurate solutions for indoor environments are costly. Radio-Frequency (RF)-based approaches are not suitable for some indoor environments such as factory floors where heavy machinery can cause interference. We propose a low-cost and simple location management system using infrared (IR) leds and the Wii Remote Controller (WRC) which has an IR camera. The proposed solution is motivated by the need to find the location of a mobile robot used for data collection in a wireless sensor network. In the proposed schemes, the WRC is placed vertically on the mobile robot pointing upward and IR leds are placed irregularly on the ceiling. The mobile robot determines its position using the relative positions of the IR leds detected by the WRC. The WRC senses a few IR leds at a time, and they are differentiated using the irregularity among them. We analyze the problem theoretically and show that there exist limitations for covering large areas. We also discuss how to overcome these limitations. For small coverage areas, we provide optimal solutions using linear programming. The proposed scheme uses the resources efficiently and can cover a large area using a single WRC and multiple IR leds. We have simulation results including nonvertical placements of the WRC. The proposed scheme is easy to implement and requires minimal bandwidth for location management.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 01, 2014
Source ID
10.1145/2536713

Entities

People

  • Ali Şaman Tosun
  • Baris Tas
  • Nihat Altiparmak

Organizations

  • Army Research Office
  • University of Louisville
  • University of Texas at San Antonio

Tags

Fields of Study

  • Computer science

Readers

  • Military Science
  • Robotics and Automation.
  • Spectroscopy.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy