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 @math 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.
We present a model of construction using iterative amorphous depositions and give a distributed algorithm to reliably build ramps in unstructured environments. The relatively simple local strategy for interacting with irregularly shaped, partially built structures gives rise robust adaptive global properties. We illustrate the algorithm in both the single robot and multi-robot case via simulation and describe how to solve key technical challenges to implementing this algorithm via a robotic prototype.  Planetary excavator robots face unique and extreme engineering constraints relative to terrestrial counterparts. In space missions mass is always at a premium because it is the main driver behind launch costs. Lightweight operation, due to low mass and reduced gravity, hinders excavation and mobility by reducing the forces a robot can effect on its environment. This work shows that there is a quantifiable, non-dimensional threshold that distinguishes the regimes of lightweight and nominal excavation. This threshold is crossed at lower weights for continuous excavators e.g. bucket-wheels than discrete scrapers. This research introduces novel experimentation that for the first time subjects excavators to gravity offload a cable pulls up on the robot with five-sixths its weight, to simulate lunar gravity while they dig. A 300 kg excavator robot offloaded to 1 6 g successfully collects 0.5 kg s using a bucket-wheel, with no discernible effect on mobility. For a discrete scraper of the same weight, production rapidly declines as rising excavation resistance stalls the robot. These experiments suggest caution in interpreting low-gravity performance predictions based solely on testing in Earth gravity. Experiments were conducted in GRC-1, a washed industrial silica-sand devoid of agglutinates and of the sub-75-micron basaltic fines that make up 40 of lunar regolith. The important dangers related to dust are thus not directly addressed. The achieved densities for experimentation are 1640 kg m3 very loose loose and 1720 kg m3 medium dense. This work develops a novel robotic bucket-wheel excavator, featuring unique direct transfer from bucket-wheel to dump bed as a solution to material transfer difficulties identified in past literature.