Autonomous multirobot excavation for lunar applications

Jekanthan Thangavelautham, Kenneth Law, Terence Fu, Nader Abu El Samid, Alexander D.S. Smith, Gabriele M.T. D'Eleuterio

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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 three-dimensional (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 °C. This minimizes base life-support complexity and reduces launch mass. ANT is compelling for a lunar mission because it does not 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. Although many challenges remain with this technology, our work shows a compelling pathway for field testing this approach.

Original languageEnglish (US)
Pages (from-to)2330-2362
Number of pages33
Issue number12
StatePublished - Dec 1 2017
Externally publishedYes


  • ISRU
  • KEYWORDS: neural-networks
  • excavation
  • human-competitive
  • multirobots

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • General Mathematics
  • Computer Science Applications


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