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.
Self-organization was originally introduced in the context of physics and chemistry to describe how microscopic processes give rise to macroscopic structures in out-of-equilibrium systems. Recent research, that extends this concept to ethology, suggests that it provides a concise description of a wide rage of collective phenomena in animals, especially in social insects. This description does not rely on individual complexity to account for complex spatiotemporal features which emerge at the colony level, but rather assumes that interactions among simple individuals can produce highly structured collective behaviors.  This study shows that a task as complicated as multi-object ‘ant-like annular sorting’ can be accomplished with ‘minimalist’ solutions employing simple mechanisms and minimal hardware. It provides an alternative to ‘patch sorting’ for multi-object sorting. Three different mechanisms, based on hypotheses about the behaviour of Leptothorax ants are investigated and comparisons are made. Mechanism I employs a simple clustering algorithm, with objects of different sizes. The mechanism explores the idea that it is the size difference of the object that promotes segregation. Mechanism II is an extension to our earlier two-object segregation mechanism. We test the ability of this mechanism to segregate an increased number of object types. Mechanism III uses a combined leaky integrator, which allows a greater segregation of object types while retaining the compactness of the structure. Its performance is improved by optimizing the mechanism's parameters using a genetic algorithm. We compare the three mechanisms in terms of sorting performance. Comparisons between the results of these sorting mechanisms and the behaviour of ants should facilitate further insights into both biological and robotic research and make a contribution to the further development of swarm robotics. We study the problem of construction by autonomous mobile robots focusing on the coordination strategy employed by the robots to solve a simple construction problem efficiently. In particular we address the problem of constructing a linear 2D structure in a planar bounded environment. A "minimalist" single-robot solution to the problem is given, as well as two multi-robot solutions, which are natural extensions to the single-robot approach, with varying degrees of inter-robot communication. Results show that with minimal inter-robot communication (1 bit of state), there is a significant improvement in the system performance. This improvement is invariant with respect to the size of the environment. In this paper, a distributed mechanism used by a robotic swarm to facilitate the building of a loose wall structure is discussed and results presented. The ability of a robotic swarm to utilise a spatio-temporal varying template in construction tasks is verified. The work is seen as a step towards the development of a generalised methodology for collective construction building tasks and establishes a means for the building of more complex structures. 