Real-time volume estimation of a dragline payload

This paper presents a method for measuring the in-bucket payload volume on a dragline excavator for the purpose of estimating the material's bulk density in real-time. Knowledge of the payload's bulk density can provide feedback to mine planning and scheduling to improve blasting and therefore provide a more uniform bulk density across the excavation site. This allows a single optimal bucket size to be used for maximum overburden removal per dig and in turn reduce costs and emissions in dragline operation and maintenance. The proposed solution uses a range bearing laser to locate and scan full buckets between the lift and dump stages of the dragline cycle. The bucket is segmented from the scene using cluster analysis, and the pose of the bucket is calculated using the Iterative Closest Point (ICP) algorithm. Payload points are identified using a known model and subsequently converted into a height grid for volume estimation. Results from both scaled and full scale implementations show that this method can achieve an accuracy of above 95%.

HIFiRE re-entry observation using an image-based visual servoing system

This paper presents an image based visual servoing system that is intended to be used for tracking and obtaining scientific observations of the HIFiRE vehicles. The primary aim of this tracking platform is to acquire and track the thermal signature emitted from the surface of the vehicle during the re-entry phase of the mission using an infra-red camera. The implemented visual servoing scheme uses a classical image based approach to identify and track the target using visual kinematic control. The paper utilizes simulation and experimental results to show the tracking performance of the system using visual feedback. Discussions on current implementation and control techniques to further improve the performance of the system are also explored.

Towards automated and in-situ, near-real time 3-D reconstruction of coral reef environments

Coral reefs are biologically complex ecosystems that support a wide variety of marine organisms. These are fragile communities under enormous threat from natural and human-based influences. Properly assessing and measuring the growth and health of reefs is essential to understanding impacts of ocean acidification, coastal urbanisation and global warming. In this paper, we present an innovative 3-D reconstruction technique based on visual imagery as a non-intrusive, repeatable, in situ method for estimating physical parameters, such as surface area and volume for efficient assessment of long-term variability. The reconstruction algorithms are presented, and benchmarked using an existing data set. We validate the technique underwater, utilising a commercial-off-the-shelf camera and a piece of staghorn coral, Acropora cervicornis. The resulting reconstruction is compared with a laser scan of the coral piece for assessment and validation. The comparison shows that 77% of the pixels in the reconstruction are within 0.3 mm of the ground truth laser scan. Reconstruction results from an unknown video camera are also presented as a segue to future applications of this research.

Super-orbital re-entry in Australia : laboratory measurement, simulation and flight observation

There are large uncertainties in the aerothermodynamic modelling of super-orbital re-entry which impact the design of spacecraft thermal protection systems (TPS). Aspects of the thermal environment of super-orbital re-entry flows can be simulated in the laboratory using arc- and plasma jet facilities and these devices are regularly used for TPS certification work [5]. Another laboratory device which is capable of simulating certain critical features of both the aero and thermal environment of super-orbital re-entry is the expansion tube, and three such facilities have been operating at the University of Queensland in recent years[10]. Despite some success, wind tunnel tests do not achieve full simulation, however, a virtually complete physical simulation of particular re-entry conditions can be obtained from dedicated flight testing, and the Apollo era FIRE II flight experiment [2] is the premier example which still forms an important benchmark for modern simulations. Dedicated super-orbital flight testing is generally considered too expensive today, and there is a reluctance to incorporate substantial instrumentation for aerothermal diagnostics into existing missions since it may compromise primary mission objectives. An alternative approach to on-board flight measurements, with demonstrated success particularly in the ‘Stardust’ sample return mission, is remote observation of spectral emissions from the capsule and shock layer [8]. JAXA’s ‘Hayabusa’ sample return capsule provides a recent super-orbital reentry example through which we illustrate contributions in three areas: (1) physical simulation of super-orbital re-entry conditions in the laboratory; (2) computational simulation of such flows; and (3) remote acquisition of optical emissions from a super-orbital re entry event.

Radiometric temperature analysis of the Hayabusa spacecraft re-entry

Hayabusa, an unmanned Japanese spacecraft, was launched to study and collect samples from the surface of the asteroid 25143 Itokawa. In June 2010, the Hayabusa spacecraft completed it’s seven year voyage. The spacecraft and the sample return capsule (SRC) re-entered the Earth’s atmosphere over the central Australian desert at speeds on the order of 12 km/s. This provided a rare opportunity to experimentally investigate the radiative heat transfer from the shock-compressed gases in front of the sample return capsule at true-flight conditions. This paper reports on the results of observations from a tracking camera situated on the ground about 100 km from where the capsule experienced peak heating during re-entry.

Empirical evaluation of an autonomous vehicle in an urban environment

Operation in urban environments creates unique challenges for research in autonomous ground vehicles. Due to the presence of tall trees and buildings in close proximity to traversable areas, GPS outage is likely to be frequent and physical hazards pose real threats to autonomous systems. In this paper, we describe a novel autonomous platform developed by the Sydney-Berkeley Driving Team for entry into the 2007 DARPA Urban Challenge competition. We report empirical results analyzing the performance of the vehicle while navigating a 560-meter test loop multiple times in an actual urban setting with severe GPS outage. We show that our system is robust against failure of global position estimates and can reliably traverse standard two-lane road networks using vision for localization. Finally, we discuss ongoing efforts in fusing vision data with other sensing modalities.

Exponentiated gradient algorithms for large-margin structured classification

We consider the problem of structured classification, where the task is to predict a label y from an input x, and y has meaningful internal structure. Our framework includes supervised training of Markov random fields and weighted context-free grammars as special cases. We describe an algorithm that solves the large-margin optimization problem defined in [12], using an exponential-family (Gibbs distribution) representation of structured objects. The algorithm is efficient—even in cases where the number of labels y is exponential in size—provided that certain expectations under Gibbs distributions can be calculated efficiently. The method for structured labels relies on a more general result, specifically the application of exponentiated gradient updates [7, 8] to quadratic programs.

Broadening of transgenic adenocarcinoma of the mouse prostate (TRAMP) model to represent late stage androgen depletion independent cancer

BACKGROUND The transgenic adenocarcinoma of the mouse prostate (TRAMP) model closely mimics PC-progression as it occurs in humans. However, the timing of disease incidence and progression (especially late stage) makes it logistically difficult to conduct experiments synchronously and economically. The development and characterization of androgen depletion independent (ADI) TRAMP sublines are reported. METHODS Sublines were derived from androgen-sensitive TRAMP-C1 and TRAMP-C2 cell lines by androgen deprivation in vitro and in vivo. Epithelial origin (cytokeratin) and expression of late stage biomarkers (E-cadherin and KAI-1) were evaluated using immunohistochemistry. Androgen receptor (AR) status was assessed through quantitative real time PCR, Western blotting, and immunohistochemistry. Coexpression of AR and E-cadherin was also evaluated. Clonogenicity and invasive potential were measured by soft agar and matrigel invasion assays. Proliferation/survival of sublines in response to androgen was assessed by WST-1 assay. In vivo growth of subcutaneous tumors was assessed in castrated and sham-castrated C57BL/6 mice. RESULTS The sublines were epithelial and displayed ADI in vitro and in vivo. Compared to the parental lines, these showed (1) significantly faster growth rates in vitro and in vivo independent of androgen depletion, (2) greater tumorigenic, and invasive potential in vitro. All showed substantial downregulation in expression levels of tumor suppressor, E-cadherin, and metastatis suppressor, KAI-1. Interestingly, the percentage of cells expressing AR with downregulated E-cadherin was higher in ADI cells, suggesting a possible interaction between the two pathways. CONCLUSIONS The TRAMP model now encompasses ADI sublines potentially representing different phenotypes with increased tumorigenicity and invasiveness.