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.
The excavation of foundations, general earthworks and earth removal tasks are activities which involve the machine operator in a series of repetitive operations, suggesting opportunities for the automation through the introduction of robotic technologies with subsequent improvements in machine utilisation and throughput. The automation of the earth removal process is also likely to provide a number of other benefits such as a reduced dependence on operator skills and a lower operator work load, both of which might be expected to contribute to improvements in quality and, in particular, the removal of the need for a local operator when working in hazardous environments. The Lancaster University Computerised Intelligent Excavator or LUCIE has demonstrated the achievement of automated and robotic excavation through the implementation of an integrated, real-time, artificial intelligence based control system utilising a novel form of motion control strategy for movement of the excavator bucket through ground. Having its origins in the systematic observation of a range of machine operators of differing levels of expertise, the control strategy as evolved enables the autonomous excavation of a high quality rectangular trench in a wide variety of types and conditions of ground and the autonomous removal of obstacles such as boulders along the line of that trench. The paper considers the development of the LUCIE programme since its inception and sets out in terms of the machine kinematics the evolution and development of the real-time control strategy from an implementation on a one-fifth scale model of a back-hoe arm to a full working system on a JCB801 360° tracked excavator.