Geoff Poulton

Animal Behaviour Understanding using Wireless Sensor Networks

This paper presents research that is being conducted by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) with the aim of investigating the use of wireless sensor networks for automated livestock monitoring and control. It is difficult to achieve practical and reliable cattle monitoring with current conventional technologies due to challenges such as large grazing areas of cattle, long time periods of data sampling, and constantly varying physical environments. Wireless sensor networks bring a new level of possibilities into this area with the potential for greatly increased spatial and temporal resolution of measurement data. CSIRO has created a wireless sensor platform for animal behaviour monitoring where we are able to observe and collect information of animals without significantly interfering with them. Based on such monitoring information, we can identify each animals behaviour and activities successfully

On connectivity of reconfigurable impact networks in ageless aerospace vehicles

The research results presented in this paper were obtained as part of the joint CSIRO-NASA Ageless Aerospace Vehicle (AAV) project. We describe the underlying principles, methodology, and preliminary results of modelling and simulating a multi-cellular sensor and communication network in a dynamic decentralised setting, motivated by a self-monitoring, self-repairing AAV. Such networks are expected to detect and react to multiple impacts and damage over a wide range of impact energies. In particular, we address the problem of forming a reconfigurable network (a minimum spanning tree) connecting cells that detected non-critical impacts, in presence of connectivity disruptions caused by critical impacts. The presented algorithm is based on the ant colony metaphor and may be complemented by gradient-based techniques. In addition, we measure the robustness of impact networks and present quantitative metrics that clearly identify phase transitions in network connectivity, separating chaotic dynamics from ordered and robust patterns.

A Simulator for Self-Adaptive Energy Demand Management

A Demand-Side Program Simulation Tool is designed to predict the response from different deployment strategies of distributed domestic energy management. To date, there are several case studies of demand management and control projects from around the world. To achieve results with sufficient generality, case studies need to be conducted over long periods, with a reasonable number of diverse households. Such case studies require large capital to set up hardware and software.To bypass these financial and temporal investments, we have designed a simulator for energy suppliers to use in order to learn the likely performance of large-scale deployments. Of main interest is the prediction of not only the level and firmness of demand response in critical peak pricing trials, but also the householdpsilas comfortable level and satisfaction level. As an example of the power of the simulator we have used it to develop and test a new self-adaptive methodology to intelligently control the energy demand. The methodology is adaptive to global factors, such as the market energy price, as well as local conditions, such as the satisfaction level of households. This paper outlines self-adaptive methodologies used within the simulator. Experimental results show energy consumption under different control strategies and the improvement of system behavior through adaptive design. With the self-adaptive demand management strategy, the total energy consumed by one million householdspsila controllable loads has reduced dramatically while the satisfaction level of households is well maintained. This is one of the very first simulators that take into account both technical and human behavior aspects.

Designing self-assembly for 2-dimensional building blocks

In this paper we present a genetic algorithm-based approach towards designing self-assembling objects comprised of square smart blocks. Each edge of each block can have one of three polarities (+1, -1 or 0) which defines how blocks stick together - opposite polarities attract, like polarities repel, and a 0 face neither attracts nor repels. In addition to this property, the block contains an internal state machine which can change the polarity of any number of its sides following the detection of an event (for example, two blocks sticking or unsticking). The aim of this work is to evolve block parameters and rule sets of the state machine which allow the self-assembly of desired basic structures that can be used as primitive building blocks for the assembly of more complicated objects. We detail a genetic algorithm-based approach that can be used to evolve the rule sets of interaction for a number of interacting blocks, so that the final shape or states of a structure formed by the blocks can approximate some target shapes or satisfy some global goals. We have assumed a list of simple identical properties for each block, and observed that a great diversity of complex structures can be achieved.

Set-Points Based Optimal Multi-Agent Coordination for Controlling Distributed Energy Loads

The management of a very large number of distributed energy resources, energy loads and generators, to create aggregated quantity of power is a hot research topic. We consider a multi-agent system comprising multiple energy loads, each with a dedicated controller. This paper introduces our latest research in self-organization of coordinated behavior of multiple agents. Energy resource agents coordinate with each other to achieve a balance between the overall consumption by the multi-agent collective and the stress on the community. In order to reduce the overall communication load while permitting efficient coordinated responses, information exchange is through indirect communications between resource agents and a broker agent. It gives a decentralized coordination approach that does not rely on intensive computation by a central processor. The algorithm presented here can coordinate different types of loads by controlling their set-points. The coordination strategy is optimized by a genetic algorithm. A fast coordination convergence has been achieved.

Dynamic zone modelling for HVAC system control

This paper presents the development and validation of a dynamic zone model used for improved control of a heating, ventilation and air conditioning (HVAC) system to reduce energy consumption and improve the quality of the indoor environment. In particular, the paper focuses on a zone modelling technique that uses physical-principles based real-time model fitting and prediction methodology, taking advantage of genetic algorithm based problem solving. An air-conditioning zone model is deduced from an energy and mass balance and then expressed in terms of electric circuit theory, where the electric circuit is used to represent functions of the building elements. Experimental results for real-time zone model fitting and prediction are given. The results show that our model is capable of accurately predicting the indoor temperature of a dynamic zone. This dynamic model is useful for control strategies that require knowledge of the dynamic characteristics of HVAC systems.

Directed self-assembly of 2-dimensional mesoblocks using top-down/bottom-up design

In this paper we present a general design methodology suitable for a class of complex multi-agent systems which are capable of self-assembly. Our methodology is based on a top-down, bottom-up approach, which has the potential to achieve a range of global design goals whilst retaining emergent behaviour somewhere in the system, and thereby allowing access to a richer solution space. Our experimental environment is a software system to model 2-dimensional self-assembly of groups of autonomous agents, where agents are defined as square smart blocks. The general design goal for such systems is to direct the self-assembly process to produce a specified structure. The potential of this design methodology has been realised by demonstrating its application to a toy problem - the self-assembly of rectangles of different sizes and shapes in a two-dimensional mesoblock environment. The design procedure shows different choices available for decomposing a system goal into subsidiary goals, as well as the steps needed to ensure a match to what is achievable from the bottom-up process. Encouraging results have been obtained, which allows mesoblock rectangles of specified size to be assembled in a directed fashion. Two different approaches to the same problem were presented, showing the flexibility of the method.

Self-organising impact boundaries in ageless aerospace vehicles

Self-monitoring, self-repairing aerospace vehicles require modular, flexible and adaptive sensing and communication networks. In general, a modular (multi-cellular) sensing and communication network is expected to detect and react to impact location, energy and damage over a wide range of impacts. It is critical that global response emerges as a result of interactions involving transfer of information embedded locally, avoiding single points-of-failure. This work presents mechanisms ensuring self-organisation of autonomous cells into robust and continuously connected impact boundaries. The spatiotemporal stability is demonstrated for a variety of cell shapes in a dynamic environment with varying energy dissipation and damage probability models.

Vision-Based Drivable Surface Detection in Autonomous Ground Vehicles

One of the primary tasks for most autonomous ground vehicles is road following. For safe maneuvering the vehicle needs to correctly identify the drivable surface. Our work is focused on the use of simple video cameras as the sensor devices. We describe a new machine learning approach to drivable surface detection that automatically combines a set of rectangular features and histogram backprojection based image segmentation algorithms to produce superior results. The machine learning algorithm is based on the AdaBoost method, one of a class of boosting techniques which are applicable to many image processing tasks such as object and face recognition or image segmentation. The algorithm is trained and tested on video data obtained from video cameras mounted on an autonomous tractor at our Queensland site. The algorithm approach, together with the simple feature-based weak classifiers used, produces significantly improved drivable surface detection results

Critical damage reporting in intelligent sensor networks

In this paper, we present a Top-Down/Bottom-Up (TDBU) design approach for critical damage reporting in intelligent sensor networks This approach is a minimal hierarchical decomposition of the problem, which seeks a balance between achievability and complexity Our simulated environment models two-dimensional square cells as autonomous agents which sense their local environment, reporting critical damage as rapidly as possible to a report delivery site (portal) by using only the adjacent-cell communication links The global goal is to design agent properties which will allow the multi-agent network to detect critical damage anywhere on the network and to communicate this information to a portal whose location is unknown to the agents We apply a TDBU approach together with genetic algorithms (GA) to address the global goal Simulations show that our system can successfully report critical damage much better than random methods.