3D perception for mining robotics

This paper discusses some of the sensing technologies available for guiding robot manipulators for a class of underground mining tasks including drilling jumbos, bolting arms, shotcreters or explosive chargers. Data acquired with such sensors, in the laboratory and underground, is presented.

Modeling and control of a 3500 tonne mining robot

Draglines are extremely large machines that are widely used in open-cut coal mines for overburden stripping. Since 1994 we have been working toward the development of a computer control system capable of automatically driving a dragline for a large portion of its operating cycle. This has necessitated the development and experimental evaluation of sensor systems, machines models, closed-loop control controllers, and an operator interface. This paper describes our steps toward the goal through scale-model and full-scale field experimentation.

Robot control of a 3,500 tonne mining machine

The mining industry is highly suitable for the application of robotics and automation technology since the work is arduous, dangerous and often repetitive. This paper describes the development of an automation system for a physically large and complex field robotic system - a 3,500 tonne mining machine (a dragline). The major components of the system are discussed with a particular emphasis on the machine/operator interface. A very important aspect of this system is that it must work cooperatively with a human operator, seamlessly passing the control back and forth in order to achieve the main aim - increased productivity.

Vision-based control for mining automation

The mining industry is highly suitable for the application of robotics and automation technology since the work is both arduous and dangerous. Visual servoing is a means of integrating noncontact visual sensing with machine control to augment or replace operator based control. This article describes two of our current mining automation projects in order to demonstrate some, perhaps unusual, applications of visual servoing, and also to illustrate some very real problems with robust computer vision

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.

Analysis and evaluation of use of the V1 and South East Freeway Bicycleway post completion of Stage C of the V1

Executive Summary: Completion of the Veloway 1 (V1) will provide a dedicated and safe route for cyclists between the Brisbane CBD and the Gateway Motorway off-ramp at Eight Mile Plains alongside the South East Motorway. The V1 is being delivered in stages and when completed will provide a dedicated 3m wide cycleway 17km in length. Two stages (D and E) remain to be constructed to complete the V1. Major trip attractors along the V1 include the Mater, Princes Alexandra and Greenslopes Hospitals, two campuses of Griffith University, Garden City shopping centre and the Australian Tax Office. This report assesses the available evidence on the impacts on cycling behaviour of the recently completed V1 Stage C. The data sources informing this review include three intercept surveys, motion activated traffic cameras and travel time surveys on the V1 and adjoining South East Freeway Bikeway (SEFB), Strava app data, and cyclist crash data along Logan Road. The key findings from the evidence are that the completed V1 Stage C has: a Attracted cyclists from Holland Park, Holland Park West, Mt Gravatt and southern parts of Tarragindi onto the V1 Stage C. b Reduced the crash exposure of pedestrians to cyclists by attracting higher speed cyclists off the adjoining SEFB onto the cycling dedicated V1 Stage C. c Reduced the potential crash exposure of cyclists to motor vehicles by attracting cyclists off Logan Road on to the V1. d Provided travel time benefits to cyclists and reduced road crossings (eight down to two). e Predominantly attracted adults commuting alone to and from work and university. The evidence shows that the two traffic crossings across Birdwood Road (required as a temporary measure until the V1 is completed) negate much of the travel time gains of the V1 Stage C compared to the adjoining SEFB for southbound cyclists. Many cyclists accessing the V1 Stage C from the south are cycling in high-volume vehicular traffic lanes to reduce their travel time along Birdwood Road, but in the process are increasing their exposure to crashes with motor vehicles. Based on these findings this report recommends that TMR: a. Continue with plans to complete the V1 Veloway b. Undertake an engineering feasibility assessment to determine the viability of constructing a section of the V1 Stage E from the intersection Weller and Birdwood Roads over Marshall Road and along Bapaume Road on the western side of the Motorway to the intersection of Bapaume and Sterculia Roads. c. In the interim, improve signage and Birdwood Road crossing points for cyclists accessing and egressing the southern end of the V1 Stage C. d. Work with Brisbane City Council to identify the safest and most practical bicycle facilities to facilitate cycle travel between Logan Road and the V1 south of Birdwood Road. e. Improve the awareness of the V1 Stage C through signage for cyclists approaching from the north with the aim of providing a better understanding of the route of the V1 to the south. f. Refine the use of motion activated traffic cameras to improve the capture rate of useable images and obtain an ongoing collection over time of V1 usage data. g. Undertake discussions with Strava, Inc. to refine the presentation of Strava data to improve visual understanding of maps showing before and after cycle route volumes along and on roads leading to the V1.

Appendix 8: Quantifying the effects of environmental factors on catch rates in South-East fisheries: 1986-2006

The collection of basic environmental data by industry members was successful and offers a way of overcoming the problems associated with differences in scale between the environment and fisheries datasets. A simple method of collecting environmental data was developed that was only a small time burden on skippers, yet has the potential to provide very useful information on the same scale as the catch and effort data recorded in the logbooks. The success of this trial was aided by the natural interest of fishers to learn more about the environment in which they fish. The archival temperature-depth tags chosen proved robust, reliable and easy to use. While the use of large scale environmental data may not yield significant improvements in stock assessments for most SESSF species, fine-scale data collected from selected vessels using methods developed during this project may, in the longer term, be useful for incorporation into CPUE standardisations in the future...

From ImageNet to mining: Adapting visual object detection with minimal supervision

This paper presents visual detection and classification of light vehicles and personnel on a mine site.We capitalise on the rapid advances of ConvNet based object recognition but highlight that a naive black box approach results in a significant number of false positives. In particular, the lack of domain specific training data and the unique landscape in a mine site causes a high rate of errors. We exploit the abundance of background-only images to train a k-means classifier to complement the ConvNet. Furthermore, localisation of objects of interest and a reduction in computation is enabled through region proposals. Our system is tested on over 10km of real mine site data and we were able to detect both light vehicles and personnel. We show that the introduction of our background model can reduce the false positive rate by an order of magnitude.

Estimates of lost material stock of buildings and roads due to the great east Japan earthquake and tsunami

This article describes research conducted for the Japanese government in the wake of the magnitude 9.0 earthquake and tsunami that struck eastern Japan on March 11, 2011. In this study, material stock analysis (MSA) is used to examine the losses of building and infrastructure materials after this disaster. Estimates of the magnitude of material stock that has lost its social function as a result of a disaster can indicate the quantities required for reconstruction, help garner a better understanding of the volumes of waste flows generated by that disaster, and also help in the course of policy deliberations in the recovery of disaster-stricken areas. Calculations of the lost building and road materials in the five prefectures most affected were undertaken. Analysis in this study is based on the use of geographical information systems (GIS) databases and statistics; it aims to (1) describe in spatial terms what construction materials were lost, (2) estimate the amount of infrastructure material needed to rehabilitate disaster areas, and (3) indicate the amount of lost material stock that should be taken into consideration during government policy deliberations. Our analysis concludes that the material stock losses of buildings and road infrastructure are 31.8 and 2.1 million tonnes, respectively. This research approach and the use of spatial MSA can be useful for urban planners and may also convey more appropriate information about disposal based on the work of municipalities in disaster-afflicted areas.