In this paper, we will introduce a novel system where identical miniaturized robotic agents with limited capabilities will collaborate to form a team that is capable of localizing and repairing scale formations in tanks and pipes within inaccessible fluidic environments. Each robotic agent is an autonomous entity that is based on the biologically inspired spiking neural networks (SNNs) that communicate using pulses or spikes. The weights of the SNN are evolved using adaptive genetic algorithm (GA) that uses adaptive crossover and mutation to converge relatively fast to solutions that allow the robots to complete the desired tasks. The robotic agents communicate using indirect communication to move towards the site of scale formation and collaborate to repair damages.

This work contains the results of an initial study into the FPGA implementation of a spiking neural network. This work was undertaken as a task in a project that aims to design and develop a new kind of tangible collaborative autonomous agent. The project intends to exploit/investigate methods for engineering emergent collective behaviour in large societies of actual miniature agents that can learn and evolve. Such multi-agent systems could be used to detect and collectively repair faults in a variety of applications where it is difficult for humans to gain access, such as fluidic environments found in critical components of material/industrial systems. The initial achievement of implementation of a spiking neural network on a FPGA hardware platform and results of a robotic wall following task are discussed by comparison with software driven robots and simulations.

In this paper we introduce a novel mechanism for controlling autonomous mobile robots that is based on using spiking neural networks (SNNs). The SNNs are inspired by biological neurons that communicate using pulses or spikes. As SNNs have shown to be excellent control systems for biological organisms, they have the potential to produce good control systems for autonomous robots. In this paper we present the use and benefits of SNNs for mobile robot control. We also present an adaptive genetic algorithm (GA) to evolve the weights of the SNNs online using real robots. The adaptive GA using adaptive crossover and mutation converge in a small number of generations to solutions that allow the robots to complete the desired tasks. We have performed many experiments using real mobile robots to test the evolved SNNs in which the SNNs provided a good response.

The paper describes research that has been carried out in "extrovert-Gadgets", a research project funded in the context of EU IST/FET proactive initiative "Disappearing Computer". It presents a set of architectures for the composition of ubiquitous computing applications. The proposed architectures are part of GAS (Gadgetware Architectural Style), a generic architectural style, which can be used to describe everyday environments populated with computational artifacts. The overall innovation of the GAS approach lies in viewing the process where people configure and use complex collections of interacting eGadgets, as having much in common with the process where system builders design software systems out of components. This approach regards the everyday environment as being populated with tens even hundreds of artifacts, which people (who are always in control) associate in ad-hoc and dynamic ways.

A purpose built environmental testbed for Intelligent Interactive Environments research, based upon student accommodation (iDorm) is described. It is argued to be a practical example of Grid technology. The testbed combines several different networks and provides a common protocol to act as a gateway between the different sensors and effectors in the iDorm. A variety of interfaces to allow users to control and monitor the different services are described and critiqued. Future plans for extensions to the iDorm are described along with future plans for research experiments using intelligent agents as monitoring tools and interfaces themselves.