Expert Opinion: Serious Concerns regarding Western Australian Planning Commission (WAPC) Draft Policy 3.7 “Planning for Bushfire Risk Management” and associated Guidelines (May 2014)

10 page document containing expert assessment of shortcomings of Western Australian State Planning Policy SPP3.7- Planning for Bushfire Risk Management. Document produced on behalf of QUT and submitted to and published by the WAPC as part of their public consultation process for their draft policy.

Reframe QUT's evaluation framework : a case study of planning, policy and positioning leading to educational best practice

Every university in Australia has a set of policies that guide the institution in its educational practices, however, the policies are often developed in isolation to each other. Now imagine a space where policies are evidence-based, refined annually, cohesively interrelated, and meet stakeholders’ needs. Is this happenstance or the result of good planning? Culturally, Queensland University of Technology (QUT) is a risk-averse institution that takes pride in its financial solvency and is always keen to know “how are we going?” With a twenty-year history of annual reporting that assures the quality of course performance through multiple lines of evidence, QUT’s Learning and Teaching Unit went one step further and strategically aligned a suite of policies that take into consideration the needs of their stakeholders, collaborate with other areas across the institution and use multiple lines of evidence to inform curriculum decision-making. In QUT’s experience, strategic planning can lead to policy that is designed to meet stakeholders’ needs, not manage them; where decision-making is supported by evidence, not rhetoric; where all feedback is incorporated, not ignored; and where policies are cohesively interrelated, not isolated. While many may call this ‘policy nirvana’, QUT has positioned itself to demonstrate good educational practice through Reframe, its evaluation framework. In this case, best practice was achieved through the application of a theory of change and a design-led logic model that allows for transition to other institutions with different cultural specificity. The evaluation approach follows Seldin’s (2003) notion to offer depth and breadth to the evaluation framework along with Berk’s (2005) concept of multiple lines of evidence. In summary, this paper offers university executives, academics, planning and quality staff an opportunity to understand the critical steps that lead to strategic planning and design of evidence-based educational policy that positions a university for best practice in learning and teaching.

Convergence-guaranteed time-varying RRT path planning for profiling floats in 4-Dimensional flow

This paper presents an extension to the Rapidly-exploring Random Tree (RRT) algorithm applied to autonomous, drifting underwater vehicles. The proposed algorithm is able to plan paths that guarantee convergence in the presence of time-varying ocean dynamics. The method utilizes 4-Dimensional, ocean model prediction data as an evolving basis for expanding the tree from the start location to the goal. The performance of the proposed method is validated through Monte-Carlo simulations. Results illustrate the importance of the temporal variance in path execution, and demonstrate the convergence guarantee of the proposed methods.

Practical path planning and obstacle avoidance for autonomous mowing

There is a need for systems which can autonomously perform coverage tasks on large outdoor areas. Unfortunately, the state-of-the-art is to use GPS based localization, which is not suitable for precise operations near trees and other obstructions. In this paper we present a robotic platform for autonomous coverage tasks. The system architecture integrates laser based localization and mapping using the Atlas Framework with Rapidly-Exploring Random Trees path planning and Virtual Force Field obstacle avoidance. We demonstrate the performance of the system in simulation as well as with real world experiments.

Method for planning and executing obstacle-free paths for rotating excavation machinery

This invention concerns the control of rotating excavation machinery, for instance to avoid collisions with obstacles. In a first aspect the invention is a control system for autonomous path planning in excavation machinery, comprising: A map generation subsystem to receive data from an array of disparate and complementary sensors to generate a 3-Dimensional digital terrain and obstacle map referenced to a coordinate frame related to the machine's geometry, during normal operation of the machine. An obstacle detection subsystem to find and identify obstacles in the digital terrain and obstacle map, and then to refine the map by identifying exclusion zones that are within reach of the machine during operation. A collision detection subsystem that uses knowledge of the machine's position and movements, as well as the digital terrain and obstacle map, to identify and predict possible collisions with itself or other obstacles, and then uses a forward motion planner to predict collisions in a planned path. And, a path planning subsystem that uses information from the other subsystems to vary planned paths to avoid obstacles and collisions. In other aspects the invention is excavation machinery including the control system; a method for control of excavation machinery; and firmware and software versions of the control system.

Relocating a flood-affected community : good planning or good politics?

Problem, research strategy and findings: On January 10, 2011, the town of Grantham, Queensland (Australia), was inundated with a flash flood in which 12 of the town's 370 residents drowned. The overall damage bill in Queensland was AUD∃2.38 billion (USD∃2.4 billion) with 35 deaths, and more than three-quarters of the state was declared a flood disaster zone. In this study, we focus on the unusual and even rare decision to relocate Grantham in March 2011. The Lockyer Valley Regional Council (LVRC) acquired a 377-hectare (932-acre) site to enable a voluntary swap of equivalent-sized lots. In addition, planning regulations were set aside to streamline the relocation of a portion of the town. We review the natural hazard literature as it relates to community relocation, state and local government documents related to Grantham, and reports and newspaper articles related to the flood. We also analyze data from interviews with key stakeholders. We document the process of community relocation, assess the relocation process in Grantham against best practice, examine whether the process of community relocation can be upscaled and if the Grantham relocation is an example of good planning or good politics. Takeaway for practice: Our study reveals two key messages for practice. Community relocation (albeit a small one) is possible, and the process can be done quickly; some Grantham residents moved into their new, relocated homes in December 2012, just 11 months after the flood. Moreover, the role of existing planning regulations can be a hindrance to quick action; political leadership, particularly at the local level, is key to implementing the relocation.

Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process

One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.

ACE research vignette 041: Business Planning and the emergence of new ventures

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. In this vignette, Christophe Garonne and Per Davidsson examine the value of business planning for business start-ups.

Powering innovation through tax concessions: The changing research & development tax incentives

The changes to the R&D tax concession in 2011 were touted as the biggest reform to business innovation policy in over a decade. Three years later, as part of the 2014 Federal Budget, a reduction in the concession rates was announced. While the most recent of the pro-posed changes are designed to align with the reduction in company tax rate, the Australian Federal Government also indicated that the gain to revenue from the reduction in the incentive scheme will be redirected by the Government to repair the Budget and fund policy priori-ties. The consequence is that the R&D concessions, while designed to encourage innovation, are clearly linked with the tax system. As such, the first part of this article considers whether the R&D concession is a changing tax for changing times. Leading on from part one, this article also addresses a second question of ‘what’s tax got to do with it’? To answer this question, the article argues that, rather than ever being substantive tax reform, the constantly changing measures simply alter the criteria and means by which companies become eligible for a Federal Government subsidy for qualifying R&D activity, whatever that amount is. It further argues that when considered as part of the broader innovation agenda, all R&D tax concessions should be evaluated as a government spending program in the same way as any direct spending on innovation. When this is done, the tax regime is arguably merely the administrative policy instrument by which the subsidy is delivered. However, this may not be best practice to distribute those funds fairly, efficiently, and without distortion, while at the same time maintaining adequate government control and accountability. Finally, in answering the question of ‘what’s tax got to do with it?’ the article concludes that the answer is: very little.

The small business CGT concessions: Evidence from the perspective of the tax practitioner

On 21 September 1999 Division 152 was inserted into the Income Tax Assessment Act (1997) (ITAA 1997). Division 152 contains the small business CGT concessions, which enables eligible small business taxpayers to reduce the amount of tax payable on capital gains arising from certain CGT events that occur after 11:45 am on 21 September 1999. One of the principal objectives of the legislation is to provide a concessionary regime for small business owners who do not have the same ability to access the concessionary superannuation regime generally available to employees. When announcing the introduction of the concessions the then Federal Treasurer, Mr Peter Costello, specifically stated that the objective of Division 152 was to provide ‘small business people with access to funds for retirement or expansion’. The purpose of this article is to: one, assess the extent to which small business taxpayers understand the CGT small business concessions, particularly when considering the sale of their business; two, determine which of the four small business CGT concessions are most commonly adopted and/or recommended by tax practitioners to clients; and three, to determine whether the superannuation changes in relation to the capping of the concessional superannuation thresholds have had an impact on the use of the small business retirement concession.

Curiosity driven reinforcement learning for motion planning on humanoids

Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment.