Automation of an underground mining vehicle using reactive navigation and opportunistic localization

This paper describes the implementation of an autonomous navigation system onto a 30 tonne Load-Haul-Dump truck. The control architecture is based on a robust reactive wall-following behaviour. To make it purposeful we provide driving hints derived from an approximate nodal-map. For most of the time, the vehicle is driven with weak localization (odometry). This need only be improved at intersections where decisions must be made - a technique we refer to as opportunistic localization. The truck has achieved full-speed autonomous operation at an artificial test mine, and subsequently, at a operational underground mine.

Autonomous control of underground mining vehicles using reactive navigation

Describes how many of the navigation techniques developed by the robotics research community over the last decade may be applied to a class of underground mining vehicles (LHDs and haul trucks). We review the current state-of-the-art in this area and conclude that there are essentially two basic methods of navigation applicable. We describe an implementation of a reactive navigation system on a 30 tonne LHD which has achieved full-speed operation at a production mine.

The implementation of a novel, bio-inspired, robotic security system

The implementation of a robotic security solution generally requires one algorithm to route the robot around the environment and another algorithm to perform anomaly detection. Solutions to the routing problem require the robot to have a good estimate of its own pose. We present a novel security system that uses metrics generated by the localisation algorithm to perform adaptive anomaly detection. The localisation algorithm is a vision-based SLAM solution called RatSLAM, based on mechanisms within the hippocampus. The anomaly detection algorithm is based on the mechanisms used by the immune system to identify threats to the body. The system is explored using data gathered within an unmodified office environment. It is shown that the algorithm successfully reacts to the presence of people and objects in areas where they are not usually present and is tolerised against the presence of people in environments that are usually dynamic.

Coordinated research support services at Queensland University of Technology, Australia

Queensland University of Technology (QUT) is a large multidisciplinary university located in Brisbane, Queensland, Australia. QUT is increasing its research focus and is developing its research support services. It has adopted a model of collaboration between the Library, High Performance Computing and Research Support (HPC) and more broadly with Information Technology Services (ITS). Research support services provided by the Library include the provision of information resources and discovery services, bibliographic management software, assistance with publishing (publishing strategies, identifying high impact journals, dealing with publishers and the peer review process), citation analysis and calculating authors’ H Index. Research data management services are being developed by the Library and HPC working in collaboration. The HPC group within ITS supports research computing infrastructure, research development and engagement activities, researcher consultation, high speed computation and data storage systems , 2D/ 3D (immersive) visualisation tools, parallelisation and optimization of research codes, statistics/ data modeling training and support (both qualitative and quantitative) and support for the university’s central Access Grid collaboration facility. Development and engagement activities include participation in research grants and papers, student supervision and internships and the sponsorship, incubation and adoption of new computing technologies for research. ITS also provides other services that support research including ICT training, research infrastructure (networking, data storage, federated access and authorization, virtualization) and corporate systems for research administration. Seminars and workshops are offered to increase awareness and uptake of new and existing services. A series of online surveys on eResearch practices and skills and a number of focus groups was conducted to better inform the development of research support services. Progress towards the provision of research support is described within the context organizational frameworks; resourcing; infrastructure; integration; collaboration; change management; engagement; awareness and skills; new services; and leadership. Challenges to be addressed include the need to redeploy existing operational resources toward new research support services, supporting a rapidly growing research profile across the university, the growing need for the use and support of IT in research programs, finding capacity to address the diverse research support needs across the disciplines, operationalising new research support services following their implementation in project mode, embedding new specialist staff roles, cross-skilling Liaison Librarians, and ensuring continued collaboration between stakeholders.

RAP-A Indigenous supplement : guidelines for the adaptation and implementation of the RAP Program for indigenous adolescents

The RAP-A Indigenous supplement has been designed to provide guidelines for the Adaptation and implementation of the RAP Program for indigenous adolescents. It describes a variety of adaptations that have been made to RAP-A to make it more suitable for indigenous teenagers.

Bayesian hybrid algorithms and models : implementation and associated issues

This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’ in nature, in that they are a composition of components for which their individual properties may be easily described but the performance of the model or algorithm as a whole is less well understood. The aim of this research project is to after a better understanding of the performance of hybrid models and algorithms. The goal of this thesis is to analyse the computational aspects of hybrid models and hybrid algorithms in the Bayesian context. The first objective of the research focuses on computational aspects of hybrid models, notably a continuous finite mixture of t-distributions. In the mixture model, an inference of interest is the number of components, as this may relate to both the quality of model fit to data and the computational workload. The analysis of t-mixtures using Markov chain Monte Carlo (MCMC) is described and the model is compared to the Normal case based on the goodness of fit. Through simulation studies, it is demonstrated that the t-mixture model can be more flexible and more parsimonious in terms of number of components, particularly for skewed and heavytailed data. The study also reveals important computational issues associated with the use of t-mixtures, which have not been adequately considered in the literature. The second objective of the research focuses on computational aspects of hybrid algorithms for Bayesian analysis. Two approaches will be considered: a formal comparison of the performance of a range of hybrid algorithms and a theoretical investigation of the performance of one of these algorithms in high dimensions. For the first approach, the delayed rejection algorithm, the pinball sampler, the Metropolis adjusted Langevin algorithm, and the hybrid version of the population Monte Carlo (PMC) algorithm are selected as a set of examples of hybrid algorithms. Statistical literature shows how statistical efficiency is often the only criteria for an efficient algorithm. In this thesis the algorithms are also considered and compared from a more practical perspective. This extends to the study of how individual algorithms contribute to the overall efficiency of hybrid algorithms, and highlights weaknesses that may be introduced by the combination process of these components in a single algorithm. The second approach to considering computational aspects of hybrid algorithms involves an investigation of the performance of the PMC in high dimensions. It is well known that as a model becomes more complex, computation may become increasingly difficult in real time. In particular the importance sampling based algorithms, including the PMC, are known to be unstable in high dimensions. This thesis examines the PMC algorithm in a simplified setting, a single step of the general sampling, and explores a fundamental problem that occurs in applying importance sampling to a high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of the estimate under conditions on the importance function. Additionally, the exponential growth of the asymptotic variance with the dimension is demonstrated and we illustrates that the optimal covariance matrix for the importance function can be estimated in a special case.

Facilitating unlearning during implementation of new technology

Purpose – One of the critical issues for change management, particularly in relation to the implementation of new technologies, is the existence of prior knowledge and established mental models which may hinder change efforts. Understanding unlearning and how it might assist during organizational change is a way to address this resistance. The purpose of this paper is to present research designed to identify specific factors that facilitate unlearning. Design/methodology/approach – Drawing together issues identified as potential influencers of unlearning, a survey questionnaire was developed and administered in an Australian corporation undergoing large-scale change due to the implementation of an enterprise information system. The results were analyzed to identify specific factors that impact on unlearning. Findings – Findings from this paper identify factors that hinder or help the unlearning process during times of change including understanding the need for change, the level of organizational support and training, assessment of the change, positive experience and informal support, the organization's history of change, individual's prior outlooks, and individuals' feelings and expectations. Research limitations/implications – The use of only one organization does not allow for comparisons between organizations of different sizes, cultures or industries and therefore extension of this research is recommended. Practical implications – For practitioners, this paper provides specific elements at both the level of individuals and the organization that need to be considered for optimal unlearning during times of change. Originality/value – Previous literature on unlearning has been predominantly conceptual and theoretical. These empirical findings serve to further an earlier model based on qualitative research into potential influencers of unlearning.

ERP systems implementation and organizational culture : the case of Jordan

Jordan is adopting Enterprise Resource Planning (ERP) systems in both its public and private sectors. Jordan's emerging private sector has historically close ties to the public sector; though a global market orientation requires a shift in its organizational culture. ERPs however embed business processes which do not necessarily fit with traditional cultural practices, and implementation success is not assured. This study looks at the perceptions of both public and private sector ERP implementations in Jordan and assesses these on various measures of success. There were few differences between public and private sectors, but the benefits actually realized in Jordanian ERPs fell short of claims made for the technology in other cultures.

Detection and prevention of distributed denial of services attacks by collaborative effort of software agents, first prototype implementation

Distributed Denial of Services DDoS, attacks has become one of the biggest threats for resources over Internet. Purpose of these attacks is to make servers deny from providing services to legitimate users. These attacks are also used for occupying media bandwidth. Currently intrusion detection systems can just detect the attacks but cannot prevent / track the location of intruders. Some schemes also prevent the attacks by simply discarding attack packets, which saves victim from attack, but still network bandwidth is wasted. In our opinion, DDoS requires a distributed solution to save wastage of resources. The paper, presents a system that helps us not only in detecting such attacks but also helps in tracing and blocking (to save the bandwidth as well) the multiple intruders using Intelligent Software Agents. The system gives dynamic response and can be integrated with the existing network defense systems without disturbing existing Internet model. We have implemented an agent based networking monitoring system in this regard.

Reactive navigation and opportunistic localization for autonomous underground mining vehicles

This paper describes an autonomous navigation system for a large underground mining vehicle. The control architecture is based on a robust reactive wall-following behaviour. To make it purposeful we provide driving hints derived from an approximate nodal-map. For most of the time, the vehicle is driven with weak localization (odometry). This need only be improved at intersections where decisions must be made – a technique we refer to as opportunistic localization. The paper briefly reviews absolute and relative navigation strategies, and describes an implementation of a reactive navigation system on a 30 tonne Load-Haul-Dump truck. This truck has achieved full-speed autonomous operation at an artificial test mine, and subsequently, at a operational underground mine.

Teachers’ understanding of school connectedness : implications for the development and implementation of school based injury prevention strategies

Aim: Worldwide, injury is the leading cause of death and disability for young people. Injuries among young people are commonly associated with risk taking behaviour, including violence and transport risks, which often occur in the context of alcohol use. The school environment has been identified as having a significant role in shaping adolescent behaviour. In particular, school connectedness, the degree to which adolescents feel that they belong and are accepted at school, has been shown to be an important protective factor. Strategies for increasing school connectedness may therefore be effective in reducing risk taking and associated injury. Prior to developing connectedness strategies, it is important to understand the perspectives of those in the school regarding the construct and how it is realised in the school context. The aim of this research was to understand teachers’ perspectives of school connectedness, the strategies they employ to connect with students, and their perceptions of school connectedness as a strategy for risk taking and injury prevention. Method: In depth interviews of approximately 45 minutes duration were conducted with 13 Health and PE teachers and support staff from 2 high schools in Southeast Queensland, Australia. Additionally, 6 focus group workshop discussions were held with 35 Education department employees (5-6 per group), including teachers from 15 Southeast Queensland high schools. Results: Participants were found to place strong importance on the development of connectedness among students, including those at risk for problem behaviour. Strategies used to promote connectedness included building trust, taking an interest in each student and being available to talk to, and finding something positive for students to succeed at. Teachers identified strategies as being related to decreased risk taking behavior. Teacher training on school connectedness was perceived as an important and useful inclusion in a school based injury prevention program. Conclusions: The established link between increased school connectedness and decreased problem behaviour has implications for school based strategies designed to decrease adolescent risk taking behaviour and associated injury. Targeting school connectedness as a point of intervention, in conjunction with individual attitude and behaviour change programs, may be an effective injury prevention strategy.

Avoiding full extension field arithmetic in pairing computations

The most costly operations encountered in pairing computations are those that take place in the full extension field Fpk . At high levels of security, the complexity of operations in Fpk dominates the complexity of the operations that occur in the lower degree subfields. Consequently, full extension field operations have the greatest effect on the runtime of Miller’s algorithm. Many recent optimizations in the literature have focussed on improving the overall operation count by presenting new explicit formulas that reduce the number of subfield operations encountered throughout an iteration of Miller’s algorithm. Unfortunately, almost all of these improvements tend to suffer for larger embedding degrees where the expensive extension field operations far outweigh the operations in the smaller subfields. In this paper, we propose a new way of carrying out Miller’s algorithm that involves new explicit formulas which reduce the number of full extension field operations that occur in an iteration of the Miller loop, resulting in significant speed ups in most practical situations of between 5 and 30 percent.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.