• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • About the ART Lab
  • Research
  • Publications
  • People
  • Contact Us
  • News
  • NRI 3.0: Swarm-Enabled Smart Agriculture

Adaptive Robotics & Technology Lab

Texas A&M University College of Engineering

Directional RSS-Based Localization for Multi-Robot Applications

Donghwa Jeong and Kiju Lee

The 12th WSEAS International Conference on Signal Processing, Robotics, and Automation

February 2013

This paper presents new techniques for simultaneous localization of multiple mobile robots using di- rectional received signal strength (RSS). The RSS data is collected from the XBee wireless module with ZigBee technology. Directional RSS was achieved by a corner reflector antenna installed on the top of each mobile robot. The design parameters of the antenna were chosen by experiments and physical constraints of the robots. To map the RSS data to physical distance values, we first filtered the data using the well-known RANdom Sample Consen- sus (RANSAC) technique and then applied the least square regression method to further remove a large amount of outliers. In addition, real-time multi-robot localization and path planning are achieved by using an online statis- tical filter. The algorithm first identifies well-conditioned RSS values and sets a dynamic step size for trajectory generation. The online statistical filter was experimentally evaluated by comparing with two other algorithms, i.e., the Kalman and particle filters, and showed better performance than the other filters in terms of processing time. Preliminary evaluation on the directivity achieved by the corner reflector revealed that the mean orientation error of −4.01◦.

Recent Posts

  • Small and rural towns’ perception of autonomous vehicles: insights from a survey in Texas
  • Adaptive Centroidal Voronoi Tessellation With Agent Dropout and Reinsertion for Multi-Agent Non-Convex Area Coverage
  • Integrated system architecture with mixed-reality user interface for virtual-physical hybrid swarm simulations
  • ARMoR: Amphibious Robot for Mobility in Real-World Applications
  • Computerized Block Games for Automated Cognitive Assessment: Development and Evaluation Study

© 2016–2025 Adaptive Robotics & Technology Lab Log in

Texas A&M Engineering Experiment Station Logo
  • State of Texas
  • Open Records
  • Risk, Fraud & Misconduct Hotline
  • Statewide Search
  • Site Links & Policies
  • Accommodations
  • Environmental Health, Safety & Security
  • Employment