The proposed return to the Moon by 2020 will represent one of the one of the most dramatic and challenging steps in human exploration as the international community prepares to establish a permanent presence, a homestead in the ultimate frontier. Prior to sending humans, however, there will be a number of robotic precursor missions. Even after humans alight on our closest celestial neighbor, robots will continue to play a crucial role, performing tasks that are too dangerous or even too mundane for astronauts. We must accordingly seek to mitigate mission risk whenever and wherever possible. Excavation tasks, for building landing pads, constructing habitats and generally establishing infrastructure, will undoubtedly be delegated to robotic systems. We propose that a multiagent methodology will be required to successfully accomplish these tasks and mitigate the associated risks. However, a multiagent approach in an unstructured environment will pose significant control challenges. We present a control architecture and philosophy for multiagent robotic systems. Such a system has been implemented in computer simulation and in a representative network of small laboratory rovers. The control paradigm is based on a flexible machine learning algorithm, which we call an "artificial neural tissue." An evolutionary approach, that is, an artificial Darwinian selection process, is used to derive the control strategy in computer simulation. The result of this process can then be directly ported to the physical system to accomplish the desired tasks.