Anthony Pounds-Cornish

Creating an ambient-intelligence environment using embedded agents

The Essex intelligent dormitory, iDorm, uses embedded agents to create an ambient-intelligence environment. In a five-and-a-half-day experiment, a user occupied the iDorm, testing its ability to learn user behavior and adapt to user needs. The embedded agent discreetly controls the iDorm according to user preferences. Our work focuses on developing learning and adaptation techniques for embedded agents. We seek to provide online, lifelong, personalized learning of anticipatory adaptive control to realize the ambient-intelligence vision in ubiquitous-computing environments. We developed the Essex intelligent dormitory, or iDorm, as a test bed for this work and an exemplar of this approach.

Evolving spiking neural network controllers for autonomous robots

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.

FPGA implementation of spiking neural networks - an initial step towards building tangible collaborative autonomous agents

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.

The iDorm - A Practical Deployment of Grid Technology

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.

Towards developing micro-scale robots for inaccessible fluidic environments

In this paper we introduce the development of dedicated hardware capable of controlling autonomous micro-scale robots for fault detection/repair in complex inaccessible fluidic environments. This work is part of a European Union funded project entitled SOCIAL, (self organized societies of connectionist intelligent agents capable of learning No IST-2001-38911). The projects aim is to produce a swarm of micro-scale (5 cm/sup 3/) autonomous robots that, through indirect communication, are capable of achieving fault detection and reparation in difficult, challenging and inaccessible environments. An application benchmark for this project is the on-line monitoring and maintenance of underwater pipelines like those found in the oil industry or desalination plants. The robots would move through the fluidic environment, continuously sensing for corrosion and scaling faults in the pipeline.

A Collaborating Team of Spiking Neural Network Based Robotic Agents for Inaccessible Fluidic Environments

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.