Adaptive depth estimation in image based visual servo control of dynamic systems

This paper considers the question of designing a fully image based visual servo control for a dynamic system. The work is motivated by the ongoing development of image based visual servo control of small aerial robotic vehicles. The observed targets considered are coloured blobs on a flat surface to which the normal direction is known. The theoretical framework is directly applicable to the case of markings on a horizontal floor or landing field. The image features used are a first order spherical moment for position and an image flow measurement for velocity. A fully non-linear adaptive control design is provided that ensures global stability of the closed-loop system. © 2005 IEEE.

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.

Guided chaos : path planning and control for a UAV-forced landing

In recent years, unmanned aerial vehicles (UAVs) have been widely used in combat, and their potential applications in civil and commercial roles are also receiving considerable attention by industry and the research community. There are numerous published reports of UAVs used in Earth science missions [1], fire-fighting [2], and border security [3] trials, with other speculative deployments, including applications in agriculture, communications, and traffic monitoring. However, none of these UAVs can demonstrate an equivalent level of safety to manned aircraft, particularly in the case of an engine failure, which would require an emergency or forced landing. This may be arguably the main factor that has prevented these UAV trials from becoming full-scale commercial operations, as well as restricted operations of civilian UAVs to only within segregated airspace.

Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in one dimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

Improved joint control using a genetic algorithm for a humanoid robot

This paper describes experiments conducted in order to simultaneously tune 15 joints of a humanoid robot. Two Genetic Algorithm (GA) based tuning methods were developed and compared against a hand-tuned solution. The system was tuned in order to minimise tracking error while at the same time achieve smooth joint motion. Joint smoothness is crucial for the accurate calculation of online ZMP estimation, a prerequisite for a closedloop dynamically stable humanoid walking gait. Results in both simulation and on a real robot are presented, demonstrating the superior smoothness performance of the GA based methods.

A Helicopter named Dolly : behavioural cloning for autonomous helicopter control

This paper considers the pros and cons of using Behavioural cloning for the development of low-level helicopter automation modules. Over the course of this project several Behavioural cloning approaches have been investigated. The results of the most effective Behavioural cloning approach are then compared to PID modules designed for the same aircraft. The comparison takes into consideration development time, reliability, and control performance. It has been found that Behavioural cloning techniques employing local approximators and a wide state-space coverage during training can produce stabilising control modules in less time than tuning PID controllers. However, performance and reliabity deficits have been found to exist with the Behavioural Cloning, attributable largely to the time variant nature of the dynamics due to the operating environment, and the pilot actions being poor for teaching. The final conclusion drawn here is that tuning PID modules remains superior to behavioural cloning for low-level helicopter automation.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

Simultaneous localisation and mapping from natural landmarks using RatSLAM

This paper describes the current state of RatSLAM, a Simultaneous Localisation and Mapping (SLAM) system based on models of the rodent hippocampus. RatSLAM uses a competitive attractor network to fuse visual and odometry information. Energy packets in the network represent pose hypotheses, which are updated by odometry and can be enhanced or inhibited by visual input. This paper shows the effectiveness of the system in real robot tests in unmodified indoor environments using a learning vision system. Results are shown for two test environments; a large corridor loop and the complete floor of an office building.

Fuzzy associative memory for humanoid robot joint control

Traditional approaches to joint control required accurate modelling of the system dynamic of the plant in question. Fuzzy Associative Memory (FAM) control schemes allow adequate control without a model of the system to be controlled. This paper presents a FAM based joint controller implemented on a humanoid robot. An empirically tuned PI velocity control loop is augmented with this feed forward FAM, with considerable reduction in joint position error achieved online and with minimal additional computational overhead.

Control issues for velocity sourced Series Elastic Actuators

The Series Elasic Actuator has been proposed as a method for providing safe force or torque based acutation for robots that interact with humans. In this paper we look at some outstanding issues in the implementation and control of Series Elastic Actuators. The study addresses issues in making the Series Elastic Actuator respond effectively in the presence of physical difficulties such as restriction, using a computation efficient controller. The improvement over previous implementations is achieved by treating the motor as a velocity source to the elastic element, rather than as a torque source.

Case-control study of early childhood caries in Australia

The aim of this case-control study of 617 children was to investigate early childhood caries (ECC) risk indicators in a non-fluoridated region in Australia. ECC cases were recruited from childcare facilities, public hospitals and private specialist clinics to source children from different socioeconomic backgrounds. Non-ECC controls were recruited from the same childcare facilities. A multinomial logistic modelling approach was used for statistical analysis. The results showed that a large percentage of children tested positive for Streptococcus mutans if their mothers also tested positive. A common risk indicator found in ECC children from childcare facilities and public hospitals was visible plaque (OR 4.1, 95% CI 1.0-15.9, and OR 8.7, 95% CI 2.3-32.9, respectively). Compared to ECC-free controls, the risk indicators specific to childcare cases were enamel hypoplasia (OR 4.2, 95% CI 1.0-18.3), difficulty in cleaning child's teeth (OR 6.6, 95% CI 2.2-19.8), presence of S. mutans (OR 4.8, 95% CI 0.7-32.6), sweetened drinks (OR 4.0, 95% CI 1.2-13.6) and maternal anxiety (OR 5.1, 95% CI 1.1-25.0). Risk indicators specific to public hospital cases were S. mutans presence in child (OR 7.7, 95% CI 1.3-44.6) or mother (OR 8.1, 95% CI 0.9-72.4), ethnicity (OR 5.6, 95% CI 1.4-22.1), and access of mother to pension or health care card (OR 20.5, 95% CI 3.5-119.9). By contrast, a history of chronic ear infections was found to be protective for ECC in childcare children (OR 0.28, 95% CI 0.09-0.82). The biological, socioeconomic and maternal risk indicators demonstrated in the present study can be employed in models of ECC that can be usefully applied for future longitudinal studies.

Biologically inspired joint control for a humanoid robot

The GuRoo is a 1.2 m tall, 23 degree of freedom humanoid constructed at the University of Queensland for research into humanoid robotics. The key challenge being addressed by the GuRoo project is the development of appropriate learning strategies for control and coordination of the robot's many joints. The development of learning strategies is seen as a way to side-step the inherent intricacy of modeling a multi-DOF biped robot. This paper outlines the approach taken to generate an appropriate control scheme for the joints of the GuRoo. The paper demonstrates the determination of local feedback control parameters using a genetic algorithm. The feedback loop is then augmented by a predictive modulator that learns a form of feed-forward control to overcome the irregular loads experienced at each joint during the gait cycle. The predictive modulator is based on the CMAC architecture. Results from tests on the GuRoo platform show that both systems provide improvements in stability and tracking of joint control.

Biologically inspired visual landmark processing for simultaneous localization and mapping

This paper illustrates a method for finding useful visual landmarks for performing simultaneous localization and mapping (SLAM). The method is based loosely on biological principles, using layers of filtering and pooling to create learned templates that correspond to different views of the environment. Rather than using a set of landmarks and reporting range and bearing to the landmark, this system maps views to poses. The challenge is to produce a system that produces the same view for small changes in robot pose, but provides different views for larger changes in pose. The method has been developed to interface with the RatSLAM system, a biologically inspired method of SLAM. The paper describes the method of learning and recalling visual landmarks in detail, and shows the performance of the visual system in real robot tests.

Disease prevention and control

Globally, the main contributors to morbidity and mortality are chronic diseases, including cardiovascular disease and diabetes. Chronic diseases are costly and partially avoidable, with around sixty percent of deaths and nearly fifty percent of the global disease burden attributable to these conditions. By 2020, chronic illnesses will likely be the leading cause of disability worldwide. Existing health care systems, both national and international, that focus on acute episodic health conditions, cannot address the worldwide transition to chronic illness; nor are they appropriate for the ongoing care and management of those already afflicted with chronic diseases. International and Australian strategic planning documents articulate similar elements to manage chronic disease; including the need for aligning sectoral policies for health, forming partnerships and engaging communities in decision-making. The Australian National Chronic Disease Strategy focuses on four core areas for managing chronic disease; prevention across the continuum, early detection and treatment, integrated and coordinated care, and self-management. Such a comprehensive approach incorporates the entire population continuum, from the ‘healthy’, to those with risk factors, through to people suffering from chronic conditions and their sequelae. This chapter examines comprehensive approach to the prevention, management and care of the population with non-communicable, chronic diseases and communicable diseases. It analyses models of care in the context of need, service delivery options and the potential to prevent or manage early intervention for chronic and communicable diseases. Approaches to chronic diseases require integrated approaches that incorporate interventions targeted at both individuals and populations, and emphasise the shared risk factors of different conditions. Communicable diseases are a common and significant contributor to ill health throughout the world. In many countries, this impact has been minimised by the combined efforts of preventative health measures and improved treatment of infectious diseases. However in underdeveloped nations, communicable diseases continue to contribute significantly to the burden of disease. The aim of this chapter is to outline the impact that chronic and communicable diseases have on the health of the community, the public health strategies that are used to reduce the burden of those diseases and the old and emerging risks to public health from infectious diseases.

Liking vs. wanting food : importance for human appetite control and weight regulation

Current train of thought in appetite research is favouring an interest in non-homeostatic or hedonic (reward) mechanisms in relation to overconsumption and energy balance. This tendency is supported by advances in neurobiology that precede the emergence of a new conceptual approach to reward where affect and motivation (liking and wanting) can be seen as the major force in guiding human eating behaviour. In this review, current progress in applying processes of liking and wanting to the study of human appetite are examined by discussing the following issues: How can these concepts be operationalised for use in human research to reflect the neural mechanisms by which they may be influenced? Do liking and wanting operate independently to produce functionally significant changes in behaviour? Can liking and wanting be truly experimentally separated or will an expression of one inevitably contain elements of the other? The review contains a re-examination of selected human appetite research before exploring more recent methodological approaches to the study of liking and wanting in appetite control. In addition, some theoretical developments are described in four diverse models that may enhance current understanding of the role of these processes in guiding ingestive behaviour. Finally, the implications of a dual process modulation of food reward for weight gain and obesity are discussed. The review concludes that processes of liking and wanting are likely to have independent roles in characterising susceptibility to weight gain. Further research into the dissociation of liking and wanting through implicit and explicit levels of processing would help to disclose the relative importance of these components of reward for appetite control and weight regulation.