  Robots are ideal for open-pit mining on the Moon as its a dull, dirty, and
dangerous task. The challenge is to scale up productivity with an
ever-increasing number of robots. This paper presents a novel method for
developing scalable controllers for use in multi-robot excavation and
site-preparation scenarios. The controller starts with a blank slate and does
not require human-authored operations scripts nor detailed modeling of the
kinematics and dynamics of the excavator. The 'Artificial Neural Tissue' (ANT)
architecture is used as a control system for autonomous robot teams to perform
resource gathering. This control architecture combines a variable-topology
neural-network structure with a coarse-coding strategy that permits specialized
areas to develop in the tissue. Our work in this field shows that fleets of
autonomous decentralized robots have an optimal operating density. Too few
robots result in insufficient labor, while too many robots cause antagonism,
where the robots undo each other's work and are stuck in gridlock. In this
paper, we explore the use of templates and task cues to improve group
performance further and minimize antagonism. Our results show light beacons and
task cues are effective in sparking new and innovative solutions at improving
robot performance when placed under stressful situations such as severe
time-constraint.
