An Experimental Platform for Multi-spacecraft Phase-Array Communications

The emergence of small satellites and CubeSats for interplanetary exploration will mean hundreds if not thousands of spacecraft exploring every corner of the solar-system. Current methods for communication and tracking of deep space probes use ground based systems such as the Deep Space Network (DSN). However, the increased communication demand will require radically new methods to ease communication congestion. Networks of communication relay satellites located at strategic locations such as geostationary orbit and Lagrange points are potential solutions. Instead of one large communication relay satellite, we could have scores of small satellites that utilize phase arrays to effectively operate as one large satellite. Excess payload capacity on rockets can be used to warehouse more small satellites in the communication network. The advantage of this network is that even if one or a few of the satellites are damaged or destroyed, the network still operates but with degraded performance. The satellite network would operate in a distributed architecture and some satellites maybe dynamically repurposed to split and communicate with multiple targets at once. The potential for this alternate communication architecture is significant, but this requires development of satellite formation flying and networking technologies. Our research has found neural-network control approaches such as the Artificial Neural Tissue can be effectively used to control multirobot/multi-spacecraft systems and can produce human competitive controllers. We have been developing a laboratory experiment platform called Athena to develop critical spacecraft control algorithms and cognitive communication methods. We briefly report on the development of the platform and our plans to gain insight into communication phase arrays for space.Comment: 4 pages, 10 figures, IEEE Cognitive Communications for Aerospace Applications Worksho

A Coarse-Coding Framework for a Gene-Regulatory-Based Artificial Neural Tissue

Abstract. A developmental Artificial Neural Tissue (ANT) architecture inspired by the mammalian visual cortex is presented. It is shown that with the effective use of gene regulation that large phenotypes in the form of Artificial Neural Tissues do not necessarily pose an impediment to evolution. ANT includes a Gene Regulatory Network that controls cell growth/death and activation/inhibition of the tissue based on a coarse-coding framework. This scalable architecture can facilitate emergent (self-organized) task decomposition and require limited task specific information compared with fixed topologies. Only a global fitness function (without biasing a particular task decomposition strategy) is specified and self-organized task decomposition is achieved through a process of gene regulation, competitive coevolution, cooperation and specialization.