Robot Communication Via Motion: A Study on Modalities for Robot-to-Human Communication in the Field

Michael S Fulton, Chelsey Edge, Junaed Sattar

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this article, we propose, implement, and evaluate a motion-based communication system for field robots: robots that operate in dynamic, unstructured, outdoor environments. We perform two pilot studies to guide our development of the system, then evaluate it alongside an audio communication system, an LCD display, and a system of blinking LEDs. We compare the usage of these four systems with three different robots from up to five different viewpoints of interaction in a large study administered via Amazon Mechanical Turk. We contribute in two ways to the development of a more robust form of field human-robot interaction, wherein robots can select the most appropriate communication vector for a given situation and context. First, we contribute a motion-based communication system for field robots along with three baseline systems against which to test it. Second, we present results from our development of this motion system, showing that it is easier to learn than a baseline blinking LED system, viable for use underwater, aerial, and terrestrial field robots, and less negatively affected by adverse viewpoints than other communication methods.

Original languageEnglish (US)
Article number15
JournalACM Transactions on Human-Robot Interaction
Volume11
Issue number2
DOIs
StatePublished - Jun 2022

Bibliographical note

Funding Information:
M. Fulton was supported by National Science Foundation GRFP grant 00074041 (Fellow ID 2019279948). C. Edge and J. Sattar were supported by National Science Foundation grant IIS 1845364. Authors’ address: M. Fulton, C. Edge, and J. Sattar, University of Minnesota, 100 Union Street SE, Minneapolis, Minnesota, USA, 55455; emails: {fulto081, edge0037, junaed}@umn.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2022 Association for Computing Machinery. 2573-9522/2022/02-ART15 $15.00 https://doi.org/10.1145/3495245

Funding Information:
The authors wish to thank the following: Noah Seichter, Hannah Dubois, Mustaf Ahmed, Kheim Vuong, Muntaqim Mehtaz, Owen Queeglay, Jungseok Hong, Karin de Langis, and Sophie Fulton for their contributions to this work, in helping to prepare or administer studies, edit and analyze video, and general advice/suggestions. Additionally, the authors thank the Minnesota Robotics Institute and the National Science Foundation for their support in our research.

Publisher Copyright:
© 2022 Association for Computing Machinery.

Keywords

  • Robotics
  • field robotics
  • human-robot-interaction
  • non-humanoid robots

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