  Agriculture is facing a labor crisis, leading to increased interest in fleets
of small, under-canopy robots (agbots) that can perform precise, targeted
actions (e.g., crop scouting, weeding, fertilization), while being supervised
by human operators remotely. However, farmers are not necessarily experts in
robotics technology and will not adopt technologies that add to their workload
or do not provide an immediate payoff. In this work, we explore methods for
communication between a remote human operator and multiple agbots and examine
the impact of audio communication on the operator's preferences and
productivity. We develop a simulation platform where agbots are deployed across
a field, randomly encounter failures, and call for help from the operator. As
the agbots report errors, various audio communication mechanisms are tested to
convey which robot failed and what type of failure occurs. The human is tasked
with verbally diagnosing the failure while completing a secondary task. A user
study was conducted to test three audio communication methods: earcons,
single-phrase commands, and full sentence communication. Each participant
completed a survey to determine their preferences and each method's overall
effectiveness. Our results suggest that the system using single phrases is the
most positively perceived by participants and may allow for the human to
complete the secondary task more efficiently. The code is available at:
https://github.com/akamboj2/Agbot-Sim.
