Modeling and control of a robotic power line inspection vehicle

Power line inspection is a vital function for electricity supply companies but it involves labor-intensive and expensive procedures which are tedious and error-prone for humans to perform. A possible solution is to use an unmanned aerial vehicle (UAV) equipped with video surveillance equipment to perform the inspection. This paper considers how a small, electrically driven rotorcraft conceived for this application could be controlled by visually tracking the overhead supply lines. A dynamic model for a ducted-fan rotorcraft is presented and used to control the action of an Air Vehicle Simulator (AVS), consisting of a cable-array robot. Results show how visual data can be used to determine, and hence regulate in closed loop, the simulated vehicle’s position relative to the overhead lines.

Development and validation of a multilevel model for predicting workload under routine and nonroutine conditions in an air traffic management center

Objective: The aim of this study was to develop a model capable of predicting variability in the mental workload experienced by frontline operators under routine and nonroutine conditions. Background: Excess workload is a risk that needs to be managed in safety-critical industries. Predictive models are needed to manage this risk effectively yet are difficult to develop. Much of the difficulty stems from the fact that workload prediction is a multilevel problem. Method: A multilevel workload model was developed in Study 1 with data collected from an en route air traffic management center. Dynamic density metrics were used to predict variability in workload within and between work units while controlling for variability among raters. The model was cross-validated in Studies 2 and 3 with the use of a high-fidelity simulator. Results: Reported workload generally remained within the bounds of the 90% prediction interval in Studies 2 and 3. Workload crossed the upper bound of the prediction interval only under nonroutine conditions. Qualitative analyses suggest that nonroutine events caused workload to cross the upper bound of the prediction interval because the controllers could not manage their workload strategically. Conclusion: The model performed well under both routine and nonroutine conditions and over different patterns of workload variation. Application: Workload prediction models can be used to support both strategic and tactical workload management. Strategic uses include the analysis of historical and projected workflows and the assessment of staffing needs. Tactical uses include the dynamic reallocation of resources to meet changes in demand.

Mobile assessment of on-road air pollution and its sources along the East-West Highway in Bhutan

Human exposures in transportation microenvironments are poorly represented by ambient stationary monitoring. A number of on-road studies using vehicle-based mobile monitoring have been conducted to address this. Most previous studies were conducted on urban roads in developed countries where the primary emission source was vehicles. Few studies have examined on-road pollution in developing countries in urban settings. Currently, no study has been conducted for roadways in rural environments where a substantial proportion of the population live. This study aimed to characterize on-road air quality on the East-West Highway (EWH) in Bhutan and identify its principal sources. We conducted six mobile measurements of PM10, particle number (PN) count and CO along the entire 570 km length of the EWH. We divided the EWH into five segments, R1-R5, taking the road length between two district towns as a single road segment. The pollutant concentrations varied widely along the different road segments, with the highest concentrations for R5 compared with other road segments (PM10 = 149 µg/m3, PN = 5.74 × 104 particles/cm-3, CO = 0.19 ppm), which is the final segment of the road to the capital. Apart from vehicle emissions, the dominant sources were road works, unpaved roads and roadside combustion activities. Overall, the highest contributions above the background levels were made by unpaved roads for PM10 (6 times background), and vehicle emissions for PN and CO (5 and 15 times background, respectively). Notwithstanding the differences in instrumentation used and particle size range measured, the current study showed lower PN concentrations compared with similar on-road studies. However, concentrations were still high enough that commuters, road maintenance workers and residents living along the EWH, were potentially exposed to elevated pollutant concentrations from combustion and non-combustion sources. Future studies should focus on assessing the dispersion patterns of roadway pollutants and defining the short- and long-term health impacts of exposure in Bhutan, as well as in other developing countries with similar characteristics.

Air pollution levels measured at traffic hot spots : Brisbane urban corridor study

Air pollution levels were monitored continuously over a period of 4 weeks at four sampling sites along a busy urban corridor in Brisbane. The selected sites were representative of industrial and residential types of urban environment affected by vehicular traffic emissions. The concentration levels of submicrometer particle number, PM2.5, PM10, CO, and NOx were measured 5-10 meters from the road. Meteorological parameters and traffic flow rates were also monitored. The data were analysed in terms of the relationship between monitored pollutants and existing ambient air quality standards. The results indicate that the concentration levels of all pollutants exceeded the ambient air background levels, in certain cases by up to an order of magnitude. While the 24-hr average concentration levels did not exceed the standard, estimates for the annual averages were close to, or even higher than the annual standard levels.