  In this paper, a control approach called Artificial Neural Tissue (ANT) is
applied to multirobot excavation for lunar base preparation tasks including
clearing landing pads and burying of habitat modules. We show for the first
time, a team of autonomous robots excavating a terrain to match a given 3D
blueprint. Constructing mounds around landing pads will provide physical
shielding from debris during launch/landing. Burying a human habitat modules
under 0.5 m of lunar regolith is expected to provide both radiation shielding
and maintain temperatures of -25 $^{o}$C. This minimizes base life-support
complexity and reduces launch mass. ANT is compelling for a lunar mission
because it doesn't require a team of astronauts for excavation and it requires
minimal supervision. The robot teams are shown to autonomously interpret
blueprints, excavate and prepare sites for a lunar base. Because little
pre-programmed knowledge is provided, the controllers discover creative
techniques. ANT evolves techniques such as slot-dozing that would otherwise
require excavation experts. This is critical in making an excavation mission
feasible when it is prohibitively expensive to send astronauts. The controllers
evolve elaborate negotiation behaviors to work in close quarters. These and
other techniques such as concurrent evolution of the controller and team size
are shown to tackle problem of antagonism, when too many robots interfere
reducing the overall efficiency or worse, resulting in gridlock. While many
challenges remain with this technology our work shows a compelling pathway for
field testing this approach.
