A Survey of Robot Swarms' Relative Localization Method.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

For robot swarm applications, accurate positioning is one of the most important requirements for avoiding collisions and keeping formations and cooperation between individuals. However, in some worst cases, the GNSS (Global Navigation Satellite System) signals are weak due to the crowd being in a swarm or blocked by a forest, mountains, and high buildings in the environment. Thus, relative localization is an indispensable way to provide position information for the swarm. In this paper, we review the status and development of relative localization. It is first assessed that relative localization to obtain spatio-temporal relationships between individuals is necessary to achieve the stable operation of the group. After analyzing typical relative localization systems and algorithms from the perspective of functionality and practicality, this paper concludes that the UWB-based (ultra wideband) system is suitable for the relative localization of robots in large-scale applications. Finally, after analyzing the current challenges in the field of fully distributed localization for robotic swarms, a complete mechanism encompassing the relative localization process and the relationship between local and global localization that can be a possible direction for future research is proposed.

Authors

  • Siyuan Chen
    First author: Department of Computer Science, Columbia University in the City of New York, 10027; second, fourth, and sixth authors: Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853; third author: Department of Mechanical Engineering, Columbia University; fifth author: Uber AI Labs, San Francisco 94103; seventh author: Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University; and eighth author: Department of Mechanical Engineering and Institute of Data Science, Columbia University.
  • Dong Yin
    Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, 430070, Wuhan, Hubei, P.R. China.
  • Yifeng Niu
    College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China.