Automation of underground LHD and truck haulage

This paper reviews the state-of-the-art in the automation of underground mining vehicles and reports on the development of an autonomous navigation system under development through the CMTE with sponsorship arranged by AMIRA. Past attempts at automating LHDs and haul trucks are described and their particular strengths and weaknesses are discussed. The auto-guidance system being developed overcomes some of the limitations of state-of-the-art prototype æcommercialÆ systems. It can be retrofitted to existing remote controlled vehicles, uses minimum installed infrastructure and is flexible enough for rapid relocation to alternate routes. The navigation techniques use data fusion of two separate sets of sensors combining natural feature recognition, nodal maps and inertial navigation techniques. Collision detection is incorporated and people and other traffic are excluded from the tramming area. This paper describes the work being done by the group with regard to auto-tramming and also outlines the future goals.

Automation of underground truck haulage

This paper reviews the state-of-the art in the automation of underground truck haulage. Past attemps at automating LHDs and haul trucks are described and their particular strengths and weaknesses are listed. We argue that the simple auto-tram...

Automation of underground truck haulage

This paper reviews the state-of-the-art in the automation of underground truck haulage. Past attempts at automating LHDs and haul trucks are described and their particular strengths and weaknesses are listed. We argue that the simple auto-tramming systems currently being commercialised, that follow rail-type guides placed along the back, cannot match the performance, flexibility and reliability of systems based on modern mobile robotic principles. In addition, the lack of collision detection research in the underground environment is highlighted.

A workplace-based Participatory Action Research project to investigate the effectiveness of nutrition and physical activity interventions for truck drivers in six Queensland transport industry workplaces: 2012-2015

The Queensland Transport Industry Workplace Health Intervention project was a Participatory Action Research (PAR) project to investigate the effectiveness of workplace-based nutrition and physical activity health promotion interventions for truck drivers in transport industry workplaces in south-east Queensland. The project was conducted by a research team at the Queensland University of Technology (QUT), and was funded by the Queensland Government under the Healthier.Happier.Workplaces initiative.

Chronic disease risks and use of a smartphone application during a physical activity and dietary intervention in Australian truck drivers

- Objective This study examined chronic disease risks and the use of a smartphone activity tracking application during an intervention in Australian truck drivers (April-October 2014). - Methods Forty-four men (mean age=47.5 [SD 9.8] years) completed baseline health measures, and were subsequently offered access to a free wrist-worn activity tracker and smartphone application (Jawbone UP) to monitor step counts and dietary choices during a 20-week intervention. Chronic disease risks were evaluated against guidelines; weekly step count and dietary logs registered by drivers in the application were analysed to evaluate use of the Jawbone UP. - Results Chronic disease risks were high (e.g. 97% high waist circumference [≥94 cm]). Eighteen drivers (41%) did not start the intervention; smartphone technical barriers were the main reason for drop out. Across 20-weeks, drivers who used the Jawbone UP logged step counts for an average of 6 [SD 1] days/week; mean step counts remained consistent across the intervention (weeks 1–4=8,743[SD 2,867] steps/day; weeks 17–20=8,994[SD 3,478] steps/day). The median number of dietary logs significantly decreased from start (17 [IQR 38] logs/weeks) to end of the intervention (0 [IQR 23] logs/week; p<0.01); the median proportion of healthy diet choices relative to total diet choices logged increased across the intervention (weeks 1–4=38[IQR 21]%; weeks 17–20=58[IQR 18]%). - Conclusions Step counts were more successfully monitored than dietary choices in those drivers who used the Jawbone UP. - Implications Smartphone technology facilitated active living and healthy dietary choices, but also prohibited intervention engagement in a number of these high-risk Australian truck drivers.