Autonomous underwater vehicle trajectory design coupled with predictive ocean models : a case study

Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceano- graphic research community. Contrary to traditional moored or static platforms, mobile sensors require intelligent planning strategies to manoeuvre through the ocean. However, the ability to navigate to high-value locations and collect data with specific scientific merit is worth the planning efforts. In this study, we examine the use of ocean model predictions to determine the locations to be visited by an AUV, and aid in planning the trajectory that the vehicle executes during the sampling mission. The objectives are: a) to provide near-real time, in situ measurements to a large-scale ocean model to increase the skill of future predictions, and b) to utilize ocean model predictions as a component in an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. We present an algorithm designed to generate paths for AUVs to track a dynamically evolving ocean feature utilizing ocean model predictions. This builds on previous work in this area by incorporating the predicted current velocities into the path planning to assist in solving the 3-D motion planning problem of steering an AUV between two selected locations. We present simulation results for tracking a fresh water plume by use of our algorithm. Additionally, we present experimental results from field trials that test the skill of the model used as well as the incorporation of the model predictions into an AUV trajectory planner. These results indicate a modest, but measurable, improvement in surfacing error when the model predictions are incorporated into the planner.

Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection. We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to the model to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks. We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles.

Networked robotic sensor platform deployments for use in coastal environmental assessment in southern California

Mobile sensor platforms such as Autonomous Underwater Vehicles (AUVs) and robotic surface vessels, combined with static moored sensors compose a diverse sensor network that is able to provide macroscopic environmental analysis tool for ocean researchers. Working as a cohesive networked unit, the static buoys are always online, and provide insight as to the time and locations where a federated, mobile robot team should be deployed to effectively perform large scale spatiotemporal sampling on demand. Such a system can provide pertinent in situ measurements to marine biologists whom can then advise policy makers on critical environmental issues. This poster presents recent field deployment activity of AUVs demonstrating the effectiveness of our embedded communication network infrastructure throughout southern California coastal waters. We also report on progress towards real-time, web-streaming data from the multiple sampling locations and mobile sensor platforms. Static monitoring sites included in this presentation detail the network nodes positioned at Redondo Beach and Marina Del Ray. One of the deployed mobile sensors highlighted here are autonomous Slocum gliders. These nodes operate in the open ocean for periods as long as one month. The gliders are connected to the network via a Freewave radio modem network composed of multiple coastal base-stations. This increases the efficiency of deployment missions by reducing operational expenses via reduced reliability on satellite phones for communication, as well as increasing the rate and amount of data that can be transferred. Another mobile sensor platform presented in this study are the autonomous robotic boats. These platforms are utilized for harbor and littoral zone studies, and are capable of performing multi-robot coordination while observing known communication constraints. All of these pieces fit together to present an overview of ongoing collaborative work to develop an autonomous, region-wide, coastal environmental observation and monitoring sensor network.

Supervisory safety practices in the work-related driving context

Work-related driving crashes are the most common cause of work-related injury, death, and absence from work in Australia and overseas. Surprisingly however, limited attention has been given to initiatives designed to improve safety outcomes in the work-related driving setting. This research paper will present preliminary findings from a research project designed to examine the effects of increasing work-related driving safety discussions on the relationship between drivers and their supervisors and motivations to drive safely. The research project was conducted within a community nursing population, where 112 drivers were matched with 23 supervisors. To establish discussions between supervisors and drivers, safety sessions were conducted on a monthly basis with supervisors of the drivers. At these sessions, the researcher presented context specific, audio-based anti-speeding messages. Throughout the course of the intervention and following each of these safety sessions, supervisors were instructed to ensure that all drivers within their workgroup listened to each particular anti-speeding message at least once a fortnight. In addition to the message, supervisors were also encouraged to frequently promote the anti-speeding message through any contact they had with their drivers (i.e., face to face, email, SMS text, and/or paper based contact). Fortnightly discussions were subsequently held with drivers, whereby the researchers ascertained the number and type of discussions supervisors engaged in with their drivers. These discussions also assessed drivers’ perceptions of the group safety climate. In addition to the fortnightly discussion, drivers completed a daily speed reporting form which assessed the proportion of their driving day spent knowingly over the speed limit. As predicted, the results found that if supervisors reported a good safety climate prior to the intervention, increasing the number of safety discussions resulted in drivers reporting a high quality relationship (i.e., leader-member exchange) with their supervisor post intervention. In addition, if drivers reported a good safety climate, increasing the number of discussions resulted in increased motivation to drive safely post intervention. Motivations to drive safely prior to the intervention also predicted self-reported speeding over the subsequent three months of reporting. These results suggest safety discussions play an important role in improving the exchange between supervisors and their drivers and drivers’ subsequent motivation to drive safely and, in turn, self reported speeding.

Multi-objective four-dimensional vehicle motion planning in large dynamic environments

This paper presents Multi-Step A* (MSA*), a search algorithm based on A* for multi-objective 4D vehicle motion planning (three spatial and one time dimension). The research is principally motivated by the need for offline and online motion planning for autonomous Unmanned Aerial Vehicles (UAVs). For UAVs operating in large, dynamic and uncertain 4D environments, the motion plan consists of a sequence of connected linear tracks (or trajectory segments). The track angle and velocity are important parameters that are often restricted by assumptions and grid geometry in conventional motion planners. Many existing planners also fail to incorporate multiple decision criteria and constraints such as wind, fuel, dynamic obstacles and the rules of the air. It is shown that MSA* finds a cost optimal solution using variable length, angle and velocity trajectory segments. These segments are approximated with a grid based cell sequence that provides an inherent tolerance to uncertainty. Computational efficiency is achieved by using variable successor operators to create a multi-resolution, memory efficient lattice sampling structure. Simulation studies on the UAV flight planning problem show that MSA* meets the time constraints of online replanning and finds paths of equivalent cost but in a quarter of the time (on average) of vector neighbourhood based A*.

Improvement through people for the emerging new work practices in manufacturing

This paper describes the changes occurring in manufacturing industries and their effect on knowledge and skills necessary to perform effectively in the new environments. The changes in knowledge and skills are presented as a summary to illustrate the extent of the change. The concept of multiskilling is used to conceptualise the emerging new knowledge and skills and finally some guidelines for designing training programs to acquire multiskilling are presented.

Incorporating parameter uncertainty into quantitative microbial risk assessment (QMRA)

Modern statistical models and computational methods can now incorporate uncertainty of the parameters used in Quantitative Microbial Risk Assessments (QMRA). Many QMRAs use Monte Carlo methods, but work from fixed estimates for means, variances and other parameters. We illustrate the ease of estimating all parameters contemporaneously with the risk assessment, incorporating all the parameter uncertainty arising from the experiments from which these parameters are estimated. A Bayesian approach is adopted, using Markov Chain Monte Carlo Gibbs sampling (MCMC) via the freely available software, WinBUGS. The method and its ease of implementation are illustrated by a case study that involves incorporating three disparate datasets into an MCMC framework. The probabilities of infection when the uncertainty associated with parameter estimation is incorporated into a QMRA are shown to be considerably more variable over various dose ranges than the analogous probabilities obtained when constants from the literature are simply ‘plugged’ in as is done in most QMRAs. Neglecting these sources of uncertainty may lead to erroneous decisions for public health and risk management.

Development of an online condition monitoring system for slow speed machinery

One of the main challenges of slow speed machinery condition monitoring is that the energy generated from an incipient defect is too weak to be detected by traditional vibration measurements due to its low impact energy. Acoustic emission (AE) measurement is an alternative for this as it has the ability to detect crack initiations or rubbing between moving surfaces. However, AE measurement requires high sampling frequency and consequently huge amount of data are obtained to be processed. It also requires expensive hardware to capture those data, storage and involves signal processing techniques to retrieve valuable information on the state of the machine. AE signal has been utilised for early detection of defects in bearings and gears. This paper presents an online condition monitoring (CM) system for slow speed machinery, which attempts to overcome those challenges. The system incorporates relevant signal processing techniques for slow speed CM which include noise removal techniques to enhance the signal-to-noise and peak-holding down sampling to reduce the burden of massive data handling. The analysis software works under Labview environment, which enables online remote control of data acquisition, real-time analysis, offline analysis and diagnostic trending. The system has been fully implemented on a site machine and contributing significantly to improve the maintenance efficiency and provide a safer and reliable operation.

Children re-read and re-write their neighborhoods : critical literacies and identity work

This chapter considers the complex literate repertoires of 21st century children in multicultural primary classrooms in Adelaide South Australia. It draws on the curricular and pedagogical work of two experienced primary school teachers who explore culture, race and class, by positioning children as textual producers across a variety of media. In particular we discuss two child-authored texts – A is for Arndale – a local alphabet book co-authored by children aged between eight and ten, and – Cooking Afghani Style - a magazine style film produced by a multi-aged class of children (aged eight to thirteen) recently arrived in Australia. In the process of making these texts, primary children engaged in reading as a cultural practice – re-reading and re-writing their neighbourhoods and identities (both individual and collective). This involved frequent excursions to local key sites, both familiar and unfamiliar to the children. They investigated how diverse children experienced and lived their lives in particular places within changing communities.

Critical literacies in place : teachers who work for just and sustainable communities

This chapter draws upon theories of social justice, critical literacy and place-based pedagogies and two research projects to discuss how teachers are working ethically and creatively towards a sustainable and just society in their place(s).

The relative impact of work-related stress, life stress and driving environment stress on driving outcomes

Previous research has shown the association between stress and crash involvement. The impact of stress on road safety may also be mediated by behaviours including cognitive lapses, errors, and intentional traffic violations. This study aimed to provide a further understanding of the impact that stress from different sources may have upon driving behaviour and road safety. It is asserted that both stress extraneous to the driving environment and stress directly elicited by driving must be considered part of a dynamic system that may have a negative impact on driving behaviours. Two hundred and forty-seven public sector employees from Queensland, Australia, completed self-report measures examining demographics, subjective work-related stress, daily hassles, and aspects of general mental health. Additionally, the Driver Behaviour Questionnaire (DBQ) and the Driver Stress Inventory (DSI) were administered. All participants drove for work purposes regularly, however the study did not specifically focus on full-time professional drivers. Confirmatory factor analysis of the predictor variables revealed three factors: DSI negative affect; DSI risk taking; and extraneous influences (daily hassles, work-related stress, and general mental health). Moderate intercorrelations were found between each of these factors confirming the ‘spillover’ effect. That is, driver stress is reciprocally related to stress in other domains including work and domestic life. Structural equation modelling (SEM) showed that the DSI negative affect factor influenced both lapses and errors, whereas the DSI risk-taking factor was the strongest influence on violations. The SEMs also confirmed that daily hassles extraneous to the driving environment may influence DBQ lapses and violations independently. Accordingly, interventions may be developed to increase driver awareness of the dangers of excessive emotional responses to both driving events and daily hassles (e.g. driving fast to ‘blow off steam’ after an argument). They may also train more effective strategies for self-regulation of emotion and coping when encountering stressful situations on the road.

Assurance of learning : the role of work integrated learning and industry partners

In the partnering with students and industry it is important for universities to recognize and value the nature of knowledge and learning that emanates from work integrated learning experiences is different to formal university based learning. Learning is not a by-product of work rather learning is fundamental to engaging in work practice. Work integrated learning experiences provide unique opportunities for students to integrate theory and practice through the solving of real world problems. This paper reports findings to date of a project that sought to identify key issues and practices faced by academics, industry partners and students engaged in the provision and experience of work integrated learning within an undergraduate creative industries program at a major metropolitan university. In this paper, those findings are focused on some of the particular qualities and issues related to the assessment of learning at and through the work integrated experience. The findings suggest that the assessment strategies needed to better value the knowledges and practices of the Creative Industries. The paper also makes recommendations about how industry partners might best contribute to the assessment of students’ developing capabilities and to continuous reflection on courses and the assurance of learning agenda.

Work integrated learning : an industry partners’ perspective

In recent years the Australian tertiary education sector may be said to be undergoing a vocational transformation. Vocationalism, that is, an emphasis on learning directed at work related outcomes is increasingly shaping the nature of tertiary education. This paper reports some findings to date of a project that seeks to identify the key issues faced by students, industry and university partners engaged in the provision of WIL within an undergraduate program offered by the Creative Industries faculty of a major metropolitan university. Here, those findings are focussed on some of the motivations and concerns of the industry partners who make their workplaces available for student internships. Businesses are not universities and do not perceive of themselves as primarily learning institutions. However, their perspectives of work integrated learning and their contributions to it need to understand more fully at practical and conceptual levels of learning provision. This paper and the findings presented here suggest that the diversity of industry partner motivations and concerns contributing to WIL provision requires that universities understand and appreciate those partners as contributors with them to a culture of learning provision and support. These industry partner contribution need to be understood as valuing work as learning, not work as something that needs to be integrated with learning to make that learning more authentic and thereby more vocational.