A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

Location-aware services are one of the key elements of modern intelligent applications. Numerous real-world applications such as factory automation, indoor delivery, and even search and rescue scenarios require autonomous robots to have the ability to navigate in an unknown environment and reach mobile targets with minimal or no prior infrastructure deployment. This research investigates and proposes a novel approach of dynamic target localisation using a single RF emitter, which will be used as the basis of allowing autonomous robots to navigate towards and reach a target. Through the use of multiple directional antennae, Received Signal Strength (RSS) is compared to determine the most probable direction of the targeted emitter, which is combined with the distance estimates to improve the localisation performance. The accuracy of the position estimate is further improved using a particle filter to mitigate the fluctuating nature of real-time RSS data. Based on the direction information, a motion control algorithm is proposed, using Simultaneous Localisation and Mapping (SLAM) and A* path planning to enable navigation through unknown complex environments. A number of navigation scenarios were developed in the context of factory automation applications to demonstrate and evaluate the functionality and performance of the proposed system.

Authors

  • Tyrone Sherwin
    Department of Electrical and Computer Engineering, The University of Auckland, Auckland 1023, New Zealand. tshe835@aucklanduni.ac.nz.
  • Mikala Easte
    Department of Electrical and Computer Engineering, The University of Auckland, Auckland 1023, New Zealand. mikalaeaste@gmail.com.
  • Andrew Tzer-Yeu Chen
    Department of Electrical and Computer Engineering, The University of Auckland, Auckland 1023, New Zealand. andrew.chen@auckland.ac.nz.
  • Kevin I-Kai Wang
    Department of Electrical and Computer Engineering, The University of Auckland, Auckland 1023, New Zealand. kevin.wang@auckland.ac.nz.
  • Wenbin Dai
    Department of Automation, Shanghai Jiao Tong University, Shanghai 200000, China. w.dai@sjtu.edu.cn.