3 research outputs found
A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems' Worldviews
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- and 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.Comment: To appear in IEEE Conference on Visual Analytics Science and
Technology (VAST) 202