Through our work we have identified hardware and software components that enable us to mimic biological entities and colonies within the context of multi-agent systems. Specifically, we use control networks with fieldbus or wireless data links to connect distributed hardware and software modules. Although there are many implementations of multi-agent systems, most literature seems to focus on the algorithmic aspects. We describe how (LonWorks) control networks, coupled with minimal custom electromechanical devices, can be used to construct multi-agent systems. Important issues include real-time system constraints, fault-tolerance, synchronization, and, of course, solving the robotic tasks at hand. We describe three proof-of-concept projects currently underway: a biped walking robot; a hexapod colony; and a complex autonomous vehicle. It is believed that attributes observed in these projects can be used to build application-specific systems in other areas.

In this paper we show how a self-organizing Kohonen neural network can use hyperellipsoid clustering (HEC) to build maps from actual sonar data. Since the HEC algorithm uses the Mahalanobis distance, the elongated shapes (typical of sonar data) can be learned. The Mahalanobis distance metric also gives a stochastic measurement of a data point's association with a node. Hence, the HEC Kohonen can be used to build topographical maps and to recognize its own topographical cites for self-localization. The number of nodes can also be regulated in a self-organizing manner by using the Kolmogorov-Smirnov (KS) test for cluster compactness. The KS test determines whether a node should be divided (mitosis) or pruned completely. By incorporating principal component analysis, the HEC Kohonen can handle problems with several dimensions (3D, time-series, etc.). The HEC Kohonen is also multifunctional in that it accommodates compact geometric motion planning and self-referencing algorithms. It can also be used to solve a host of other pattern recognition problems.