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.

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.

The evolving landscape of natural resource planning and governance in Australia

Australia’s governance of land and natural resources involves multiple polycentric domains of decision-making from global through to local levels. Although certainly complex, these arrangements have not necessarily translated into better decision-making or better environmental outcomes as evidenced by the growing concerns over the health and future of the Great Barrier Reef, (GBR). However within this system, arrangements for natural resource management (NRM) and reef water quality, which both use Australia’s integrated regional NRM model, have showed signs of improving decision-making and environmental outcomes in the GBR. In this paper we describe the latest evolutions in the governance and planning for natural resource use and management in Australia. We begin by reviewing the experience with first generation NRM as published in major audits and evaluations. As our primary interest is the health and future of the GBR, we then consider the impact of changes of second generation planning and governance outcomes in Queensland. We find that first generation plans, although developed under a relatively cohesive governance context, faced substantial problems in target setting, implementation, monitoring and review. Despite this, they were able to progress improvements in water quality in the Great Barrier Reef Regions. Second generation plans, currently being developed, face an even greater risk of failure due to the lack of bilateralism and cross-sectoral cooperation across the NRM governance system. The findings highlight the critical need to re-build and enhance the regional NRM model for NRM planning to have a positive impact on environmental outcomes in the GBR.

Planning and governance approaches for protecting World Heritage Areas: A comparison of the Great Barrier Reef and the Florida Everglades

The focus of this paper is on two World Heritage Areas: the Great Barrier Reef in Queensland, Australia and the Everglades in Florida. While both are World Heritage listed by the UNESCO, the Everglades is on the "World Heritage in Danger" list and the Great Barrier Reef could be on this list within the next year if present pressures continue. This paper examines the planning approaches and governance structures used in these two areas (Queensland and Florida) to manage the growth and development pressures. To make the analysis manageable, given the scale of these World Heritage areas, case studies at the local government level will be used: the Cairns Regional Council in Queensland and Monroe County in Florida. The case study analysis will involve three steps: (1) examination of the various plans at the federal, state, local levels that impact upon environmental quality in the Great Barrier Reef and Everglades; (2) assessing the degree to which these plans have been implemented; and (3) determine if (and how) the plans have improved environmental quality. In addition to the planning analysis we will also examine the governance structures (Lebel et al. 2006) within which planning operates. In any comparative analysis context is important (Hantrais 2009). Contextual differences between Queensland and Florida have previously been examined by Sipe, et al. (2007) and will be used as the starting point for this analysis. Our operating hypothesis and preliminary analysis suggests that the planning approaches and governance structures used in Florida and Queensland are considerably different, but the environmental outcomes may be similar. This is based, in part, on Vella (2004) who did a comparative analysis of environmental practices in the sugar industry in Florida and Queensland. This research re-examines this hypothesis and broadens the focus beyond the sugar industry to growth and development more broadly.

Oncology service initiatives and research in regional Australia

Objective: This paper reflects on the recent growth of cancer research being conducted through some of Australia’s rural centres. It encompasses work being done across the fields of clinical, translational and health services research. Design: This is a collaborative piece with contributions from rural health researchers, clinical and cancer services staff from several different regions. Conclusion: The past decade has seen an expansion in cancer research in rural and regional Australia driven in part by the recognition that cancer patients in remote areas experience poorer outcomes than their metropolitan counterparts. This work has led to the development of more effective cancer networks and new models of care designed to meet the particular needs of the rural cancer patient. It is hoped that the growth of cancer research in regional centres will, in time, reduce the disparity between rural and urban communities and improve outcomes for cancer patients across both populations.

Weather variability and influenza A (H7N9) transmission in Shanghai, China: A Bayesian spatial analysis

Abstract Background A novel avian influenza A (H7N9) virus was first found in humans in Shanghai, and infected over 433 patients in China. To date, very little is known about the spatiotemporal variability or environmental drivers of the risk of H7N9 infection. This study explored the spatial and temporal variation of H7N9 infection and assessed the effects of temperature and rainfall on H7N9 incidence. Methods A Bayesian spatial conditional autoregressive (CAR) model was used to assess the spatiotemporal distribution of the risk of H7N9 infection in Shanghai, by district and fortnight for the period 19th February–14th April 2013. Data on daily laboratory-confirmed H7N9 cases, and weather variability including temperature (°C) and rainfall (mm) were obtained from the Chinese Information System for Diseases Control and Prevention and Chinese Meteorological Data Sharing Service System, respectively, and aggregated by fortnight. Results High spatial variations in the H7N9 risk were mainly observed in the east and centre of Shanghai municipality. H7N9 incidence rate was significantly associated with fortnightly mean temperature (Relative Risk (RR): 1.54; 95% credible interval (CI): 1.22–1.94) and fortnightly mean rainfall (RR: 2.86; 95% CI: 1.47–5.56). Conclusion There was a substantial variation in the spatiotemporal distribution of H7N9 infection across different districts in Shanghai. Optimal temperature and rainfall may be one of the driving forces for H7N9.

Exploring the usefulness of structural-functional approaches to systematically assess the functionality of governance arrangements for natural resource management planning in two Australian case studies

This research developed and applied an evaluative framework to analyse multiple scales of decision-making for environmental management planning. It is the first exploration of the sociological theory of structural-functionalism and its usefulness to support evidence based decision-making in a planning context. The framework was applied to analyse decision-making in Queensland's Cape York Peninsula and Wet Tropics regions.

Discovering future disaster management capability needs using scenario planning

In recent years disaster risk reduction efforts have focused on disturbances ranging from climate variability, seismic hazards, geo-political instability and public and animal health crises. These factors combined with uncertainty derived from inter-dependencies within and across systems of critical infrastructure create significant problems of governance for the private and public sector alike. The potential for rapid spread of impacts, geographically and virtually, can render a comprehensive understanding of disaster response and recovery needs and risk mitigation issues beyond the grasp of competent authority. Because of such cascading effects communities and governments at local and state-levels are unlikely to face single incidents but rather series of systemic impacts: often appearing concurrently. A further point to note is that both natural and technological hazards can act directly on socio-technical systems as well as being propagated by them: as network events. Such events have been categorised as ‘outside of the box,’ ‘too fast,’ and ‘too strange’ (Lagadec, 2004). Emergent complexities in linked systems can make disaster effects difficult to anticipate and recovery efforts difficult to plan for. Beyond the uncertainties of real world disasters, that might be called familiar or even regular, can we safely assume that the generic capability we use now will suit future disaster contexts? This paper presents initial scoping of research funded by the Bushfire and Natural Hazards Cooperative Research Centre seeking to define future capability needs of disaster management organisations. It explores challenges to anticipating the needs of representative agencies and groups active in before, during and after phases of emergency and disaster situations using capability deficit assessments and scenario assessment.

Achieving intention-centric BPM through automated planning

As a result of the more distributed nature of organisations and the inherently increasing complexity of their business processes, a significant effort is required for the specification and verification of those processes. The composition of the activities into a business process that accomplishes a specific organisational goal has primarily been a manual task. Automated planning is a branch of artificial intelligence (AI) in which activities are selected and organised by anticipating their expected outcomes with the aim of achieving some goal. As such, automated planning would seem to be a natural fit to the BPM domain to automate the specification of control flow. A number of attempts have been made to apply automated planning to the business process and service composition domain in different stages of the BPM lifecycle. However, a unified adoption of these techniques throughout the BPM lifecycle is missing. As such, we propose a new intention-centric BPM paradigm, which aims on minimising the specification effort by exploiting automated planning techniques to achieve a pre-stated goal. This paper provides a vision on the future possibilities of enhancing BPM using automated planning. A research agenda is presented, which provides an overview of the opportunities and challenges for the exploitation of automated planning in BPM.

Melanoma cell colony expansion parameters revealed by approximate Bayesian computation

In vitro studies and mathematical models are now being widely used to study the underlying mechanisms driving the expansion of cell colonies. This can improve our understanding of cancer formation and progression. Although much progress has been made in terms of developing and analysing mathematical models, far less progress has been made in terms of understanding how to estimate model parameters using experimental in vitro image-based data. To address this issue, a new approximate Bayesian computation (ABC) algorithm is proposed to estimate key parameters governing the expansion of melanoma cell (MM127) colonies, including cell diffusivity, D, cell proliferation rate, λ, and cell-to-cell adhesion, q, in two experimental scenarios, namely with and without a chemical treatment to suppress cell proliferation. Even when little prior biological knowledge about the parameters is assumed, all parameters are precisely inferred with a small posterior coefficient of variation, approximately 2–12%. The ABC analyses reveal that the posterior distributions of D and q depend on the experimental elapsed time, whereas the posterior distribution of λ does not. The posterior mean values of D and q are in the ranges 226–268 µm2h−1, 311–351 µm2h−1 and 0.23–0.39, 0.32–0.61 for the experimental periods of 0–24 h and 24–48 h, respectively. Furthermore, we found that the posterior distribution of q also depends on the initial cell density, whereas the posterior distributions of D and λ do not. The ABC approach also enables information from the two experiments to be combined, resulting in greater precision for all estimates of D and λ.

Predictive motion planning for AUVs subject to strong time-varying currents and forecasting uncertainties

This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A*-like algorithms. Vehicle performance limits are incorporated into the algorithm with disturbances represented as spatial and temporally varying ocean currents with a bounded uncertainty in their predictions. The proposed algorithm is demonstrated through simulations using a 4-Dimensional, spatially distributed time-series predictive ocean current model. Results show the combined NRMPC and A* approach is capable of generating energy-efficient paths which are resistant to both dynamic disturbances and ocean model uncertainty.

Can social resilience inform SA/SIA for adaptive planning for climate change in vulnerable regions?

This book represents a landmark effort to probe and analyze the theory and empirics of designing water disaster management policies. It consists of seven chapters that examine, in-depth and comprehensively, issues that are central to crafting effective policies for water disaster management. The authors use historical surveys, institutional analysis, econometric investigations, empirical case studies, and conceptual-theoretical discussions to clarify and illuminate the complex policy process. The specific topics studied in this book include a review and analysis of key policy areas and research priority areas associated with water disaster management, community participation in disaster risk reduction, the economics and politics of ‘green’ flood control, probabilistic flood forecasting for flood risk management, polycentric governance and flood risk management, drought management with the aid of dynamic inter-generational preferences, and how social resilience can inform SA/SIA for adaptive planning for climate change in vulnerable areas. A unique feature of this book is its analysis of the causes and consequences of water disasters and efforts to address them successfully through policy-rich, cross-disciplinary and transnational papers. This book is designed to help enrich the sparse discourse on water disaster management policies and galvanize water professionals to craft creative solutions to tackle water disasters efficiently, equitably, and sustainably. This book should also be of considerable use to disaster management professionals, in general, and natural resource policy analysts.