Myra S. Wilson

The Flame system: automating electrical failure mode and effects analysis (FMEA)

It is well known that FMEA is both tedious and time consuming-so much so, that an FMEA analysis on the design of a system is often only completed after a first prototype has been constructed. This situation can lead to time, effort and money being wasted. Automating the FMEA process will improve the speed and consistency with which an FMEA analysis can be performed. The Flame system aims to provide engineers with a knowledge based system (KBS) which is capable of performing automated FMEA. At present, we are concentrating our efforts on electrical design FMEA, however mechanical and software FMEA will be the subjects of future study. The input to the Flame system consists of a physical description of a particular circuit and a description of that circuits functionality. The output from Flame will be a complete (or near complete) FMEA form which can be checked, annotated and signed off by an engineer. The Flame system demonstrates that it is indeed possible to provide engineers with a means of performing automated electrical FMEA. The application considered is automobile systems.

BeeIP - A Swarm Intelligence based routing for wireless ad hoc networks

Agent-based routing in wireless ad hoc networks defines a set of rules that all the participating nodes follow. Routing becomes a collaboration between nodes, reducing computational and resource costs. Swarm Intelligence uses agent-like entities from insect societies as a metaphor to solve the routing problem. Certain insects exchange information about their activities and the environment in which they operate in order to complete their tasks in an adaptive, efficient and scalable manner. This paper examines Swarm Intelligence based routing protocols, along with a newly proposed bee-inspired routing protocol for providing multi-path routing in wireless ad hoc networks of mobile nodes. Simulation results indicate that applying Swarm Intelligence offers a significant level of adaptability and efficiency that, under several network conditions, allow the protocol to outperform traditional approaches.

Combining functional and structural reasoning for safety analysis of electrical designs

Increasing complexity of design in automotive electrical systems has been paralleled by increased demands for analysis of the safety and reliability aspects of those designs. Such demands can place a great burden on the engineers charged with carrying out the analysis. This paper describes how the intended functions of a circuit design can be combined with a qualitative model of the electrical circuit that fulfils the functions, and used to analyse the safety of the design. FLAME, an automated failure mode and effects analysis system based on these techniques, is described in detail. FLAME has been developed over several years, and is capable of composing an FMEA report for many different electrical subsystems. The paper also addresses the issue of how the use of functional and structural reasoning can be extended to sneak circuit analysis and fault tree analysis.

Evolving hierarchical robot behaviours

Inspired by the work of Brooks, many researchers involved in programming robots have turned to the behaviour-based approach. At present, the behaviours are designed by hand and hard-wired into the architecture. The work presented in this paper looks at using an evolutionary algorithm approach (based on the genetic algorithm) to construct behaviours. Building from well-defined primitive behaviours, hierarchies can be evolved to produce more complex behaviour. The behaviours in the evolutionary system are tested in simulation, but the best are then tested on a mobile robot for grounding in the real world. This allows the evolutionary process to rapidly drive the development of the behaviours using simulation while also ensuring their suitability in the real world. In the paper we show how this evolutionary process evolves practical hierarchical behaviours for the detection of a goal object in a series of mazes.

BeeIP: bee-inspired protocol for routing in mobile ad-hoc networks

We introduce a new bee-inspired routing protocol for mobile ad hoc networks. Emphasis is given to the ability of bees to evaluate paths by considering several quality factors. In order to achieve similar behaviour in the networking environment, BeeIP is using cross-layering. Fetching parameters from the lower PHY and MAC layers to the core of the protocol, offers the artificial bees the ability to make predictions about the links future performance. Our approach is compared with two well-known routing protocols in the area, the destination sequenced distance-vector protocol (DSDV), and the adaptive on-demand distance vector protocol (AODV). The outcome shows that BeeIP achieves higher data delivery rates and less control overhead than DSDV, and slightly better results compared to AODV, initializing less route discovery processes.

Vector-valued function estimation by grammatical evolution for autonomous robot control

An autonomous mobile robot requires a robust onboard controller that makes intelligent responses in dynamic environments. Current solutions tend to lead to unnecessarily complex solutions that only work in niche environments. Evolutionary techniques such as genetic programming (GP) can successfully be used to automatically program the controller, minimizing the limitations arising from explicit or implicit human design criteria, based on the robots experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been applied to various problems, particularly those for which GP has performed. We formulate robot control as vector-valued function estimation and present a novel generative grammar for vector-valued functions. A consideration of the crossover operator leads us to propose a design criterion for the application of GE to vector-valued function estimation, along with a second novel generative grammar which meets this criterion. The suitability of these grammars for vector-valued function estimation is assessed empirically on a simulated task for the Khepera robot.

Reliability and flexibility-a mutually exclusive problem for robotic assembly?

One problem often encountered when performing assembly using robots is how to deal with variation and uncertainty in the workcell. Either the workcell becomes tightly constrained, thus losing the flexibility of the system, or errors occur, which reduces the reliability of the system. This paper attempts to address this problem using a hybrid architecture. A planning system produces a high-level ordering of the assembly in terms of part motions, which are translated into robot motions by an adaptive run-time execution system. The execution system contains a flexible hierarchy of competent modular units which combine to perform the assembly reliably. The work in this paper attempts to reduce the complexity found in assembly planning and to provide a reactive run-time system which can deal with uncertainty and variation without reference to a computationally expensive global-world model. The background to this work is presented, along with an experimental system designed to test out the ideas. Conclusions about the usefulness of the system are drawn at the end of the paper.

Swarm intelligence to wireless ad hoc networks: adaptive honeybee foraging during communication sessions

With no fixed infrastructure, discovering new ways of managing high mobility and limited resources to produce optimized routing in wireless ad hoc networks is the key objective of active research. Adaptive foraging principles found in insects have attracted the research community to develop new approaches that benefit from the simplicity and collaborative behaviours of these natural multi-agent systems. This paper discusses both traditional and swarm-intelligence-based routing and investigates the extent to which a new bee-inspired approach, termed BeeIP, results in adaptive, robust and optimized routing in networks of high mobility. BeeIP is directly and quantitatively compared with the state-of-the-art protocols using a variety of performance metrics. The results show that it outperforms the others by keeping low and balanced end-to-end packet delay under stressful network conditions, such as high traffic and mobility rates. In addition, BeeIP is indirectly and qualitatively compared with the first bee-inspired routing protocol, BeeAdHoc. The resulting discussion indicates that the proposed design can offer better packet delivery ratio and use smaller control packets, thus less overhead, by utilizing an enhanced adaptive path monitoring mechanism inspired by honeybee foraging.

Evolutionary coordination system for fixed-wing communications unmanned aerial vehicles

A system to coordinate the movement of a group of unmanned aerial vehicles that provide a network backbone over mobile ground-based vehicles with communication needs is presented. Using evolutionary algorithms, the system evolves flying manoeuvres that position the aerial vehicles by fulfilling two key requirements; i) they maximise net coverage and ii) they minimise the power consumption. Experimental results show that the proposed coordination system is able to offer a desirable level of adaptability with respect to the objectives set, providing useful feedback for future research directions.

From Animals to Animats 14

The Animals to Animats Conference brings together researchers from ethology, psychology, ecology, artificial intelligence, artificial life, robotics, engineering, and related fields to further understanding of the behaviors and underlying mechanisms that allow natural and synthetic agents (animats) to adapt and survive in uncertain environments. The work presented focuses on well-defined models--robotic, computer-simulation, and mathematical--that help to characterize and compare various organizational principles or architectures underlying adaptive behavior in both natural animals and animats.