# Autonomous Multirobot Excavation for Lunar Applications

## TL;DR

This paper demonstrates a novel autonomous multirobot excavation approach using Artificial Neural Tissue (ANT) for lunar base preparation, enabling robots to interpret blueprints, excavate terrain, and develop techniques without human intervention.

## Contribution

It introduces ANT for multirobot lunar excavation, allowing autonomous interpretation, planning, and execution of complex tasks with minimal supervision, including innovative techniques like slot-dozing.

## Key findings

- Robots successfully interpret blueprints and excavate terrain autonomously.
- ANT evolves creative excavation techniques such as slot-dozing.
- The approach addresses robot interference and team size optimization.

## Abstract

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.