TY - GEN
T1 - A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems' Worldviews
AU - Bae, Suyun LSandrar
AU - Rossi, Federico
AU - Hook, Joshua Vander
AU - Davidoff, Scott
AU - Ma, Kwan Liu
N1 - Funding Information:
The development of MOSAIC Viewer was enabled by the JPL/Caltech/ArtCenter data visualization program. We would like to thank Santiago Lombeyda, Hillary Mushkin, Maggie Hendrie, Alessandra Fleck, and Sarah Strickler for their feedback and contribution on the earlier prototypes of MOSAIC Viewer. Also, special thanks to the MOSAIC team! This research is sponsored in part by the U.S. National Science Foundation through grant IIS-1741536. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Applications
KW - Debugging
KW - Human-Subjects Qualitative Studies
KW - I.3.8 [Computer Graphics]
KW - Multi-Robot Systems
UR - http://www.scopus.com/inward/record.url?scp=85099865641&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099865641&partnerID=8YFLogxK
U2 - 10.1109/VAST50239.2020.00008
DO - 10.1109/VAST50239.2020.00008
M3 - Conference contribution
AN - SCOPUS:85099865641
T3 - Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020
SP - 24
EP - 35
BT - Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE Conference on Visual Analytics Science and Technology, VAST 2020
Y2 - 25 October 2020 through 30 October 2020
ER -