Distributed Robotic Vision as a service

The vision sense of standalone robots is limited by line of sight and onboard camera capabilities, but processing video from remote cameras puts a high computational burden on robots. This paper describes the Distributed Robotic Vision Service, DRVS, which implements an on-demand distributed visual object detection service. Robots specify visual information requirements in terms of regions of interest and object detection algorithms. DRVS dynamically distributes the object detection computation to remote vision systems with processing capabilities, and the robots receive high-level object detection information. DRVS relieves robots of managing sensor discovery and reduces data transmission compared to image sharing models of distributed vision. Navigating a sensorless robot from remote vision systems is demonstrated in simulation as a proof of concept.

Probabilistic load flow for distribution systems with uncertain PV generation

Large integration of solar Photo Voltaic (PV) in distribution network has resulted in over-voltage problems. Several control techniques are developed to address over-voltage problem using Deterministic Load Flow (DLF). However, intermittent characteristics of PV generation require Probabilistic Load Flow (PLF) to introduce variability in analysis that is ignored in DLF. The traditional PLF techniques are not suitable for distribution systems and suffer from several drawbacks such as computational burden (Monte Carlo, Conventional convolution), sensitive accuracy with the complexity of system (point estimation method), requirement of necessary linearization (multi-linear simulation) and convergence problem (Gram–Charlier expansion, Cornish Fisher expansion). In this research, Latin Hypercube Sampling with Cholesky Decomposition (LHS-CD) is used to quantify the over-voltage issues with and without the voltage control algorithm in the distribution network with active generation. LHS technique is verified with a test network and real system from an Australian distribution network service provider. Accuracy and computational burden of simulated results are also compared with Monte Carlo simulations.

A Distributed Robotic Vision Service

Robotic vision is limited by line of sight and onboard camera capabilities. Robots can acquire video or images from remote cameras, but processing additional data has a computational burden. This paper applies the Distributed Robotic Vision Service, DRVS, to robot path planning using data outside line-of-sight of the robot. DRVS implements a distributed visual object detection service to distributes the computation to remote camera nodes with processing capabilities. Robots request task-specific object detection from DRVS by specifying a geographic region of interest and object type. The remote camera nodes perform the visual processing and send the high-level object information to the robot. Additionally, DRVS relieves robots of sensor discovery by dynamically distributing object detection requests to remote camera nodes. Tested over two different indoor path planning tasks DRVS showed dramatic reduction in mobile robot compute load and wireless network utilization.

New perspectives on design and delivery: The context of service desk

This research has identified the trends apparent in service desk design and delivery literature. By doing archival analysis, this investigation has led to the development of a generic framework which has identified three themes in service desk design – User groups, Support models, and Technology types – and two themes in service desk delivery – Direction of delivery, and Executive support level. This research also aims to provide an understanding of service desk functions and the challenges faced by organisations in delivering those functions.

Field performance of in-service cast iron gas reticulation pipe buried in reactive clay

Field instrumentation of an in-service cast iron gas pipe buried in a residential area is detailed in this paper. The aim of the study was to monitor the long-term pipe behavior to understand the mechanisms of pipe bending in relation to ground movement as a result of seasonal fluctuation of soil moisture content. Field data showed that variation of soil temperature, suction, and moisture content are closely related to the prevailing climate. Change of soil temperature is generally related to the ambient air temperature, with a variation of approximately −3°C −3°C per meter depth from the ground surface in summer (decrease with depth) and winter (increase with depth). Seasonal cyclic variation in moisture content was observed with maxima in February and March, and a minimum around September. The pipe top was under tensile strain during summer and subsequently subjected to compressive strain as soil swelling occurred as a result of increase in moisture content. The study suggests that downward pipe bending occurs in summer because of soil shrinkage, while upward pipe bending occurs in winter when the soil swells.