A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems' Worldviews

Suyun LSandrar Bae, Federico Rossi, Joshua Vander Hook, Scott Davidoff, Kwan Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Autonomous multi-robot systems, where a team of robots shares information to perform tasks that are beyond an individual robot's abilities, hold great promise for a number of applications, such as planetary exploration missions. Each robot in a multi-robot system that uses the shared-world coordination paradigm autonomously schedules which robot should perform a given task, and when, using its worldview-the robot's internal representation of its belief about both its own state, and other robots' states. A key problem for operators is that robots' worldviews can fall out of sync (often due to weak communication links), leading to desynchronization of the robots' scheduling decisions and inconsistent emergent behavior (e.g., tasks not performed, or performed by multiple robots). Operators face the time-consuming and difficult task of making sense of the robots' scheduling decisions, detecting de-synchronizations, and pinpointing the cause by comparing every robot's worldview. To address these challenges, we introduce MOSAIC Viewer, a visual analytics system that helps operators (i) make sense of the robots' schedules and (ii) detect and conduct a root cause analysis of the robots' desynchronized worldviews. Over a year-long partnership with roboticists at the NASA Jet Propulsion Laboratory, we conduct a formative study to identify the necessary system design requirements and a qualitative evaluation with 12 roboticists. We find that MOSAIC Viewer is faster- A nd easier-to-use than the users' current approaches, and it allows them to stitch low-level details to formulate a high-level understanding of the robots' schedules and detect and pinpoint the cause of the desynchronized worldviews.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages24-35
Number of pages12
ISBN (Electronic)9781728180090
DOIs
StatePublished - Oct 2020
Event15th IEEE Conference on Visual Analytics Science and Technology, VAST 2020 - Virtual, Salt Lake City, United States
Duration: Oct 25 2020Oct 30 2020

Publication series

NameProceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020

Conference

Conference15th IEEE Conference on Visual Analytics Science and Technology, VAST 2020
CountryUnited States
CityVirtual, Salt Lake City
Period10/25/2010/30/20

Keywords

  • Applications
  • Debugging
  • Human-Subjects Qualitative Studies
  • I.3.8 [Computer Graphics]
  • Multi-Robot Systems

ASJC Scopus subject areas

  • Media Technology
  • Modeling and Simulation

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