Replanning of multiple autonomous vehicles in material handling

The fully automated docks in Australia present opportunities for applications of autonomous vehicles and engineering innovation. When planning tasks to be done by multi-autonomous vehicles in an enclosed area with a known dynamic map (i.e. bi-directional path network), there are many issues that have not yet been comprehensively solved. The real world presents more complexity than the initial algorithms addressed. There are problems that occur due to interaction with the real-world. This means autonomous vehicles can stop, are affected, or face problems, and hence tasks and vehicles' paths and motion need to be replanned. In order to replan, a greater understanding of the state of vehicles, the state of the map, and importantly the importance of tasks and vehicles is definitely needed. This paper explores the improvements made to replanning by gaining a thorough understanding of the map and then utilising map information to make the best, most efficient replanning decision. Five replanning Methods are investigated and four Options which combine the Methods in different ways, are tested in this research. A map analysis Method is also presented. Simulation studies show that map information based replanning is the most efficient Method out of those tested. © 2006 IEEE

Consulting revenue sharing, auditor effort and independence, and the regulation of auditor compensation

The joint provision of audit and non-audit services by audit firms to their audit clients has posed a threat to auditor independence. To mitigate the independence problem, the US Securities and Exchange Commission (SEC) issued a regulation (. SEC, 2003) that prohibits audit partners from receiving compensation for the sale of non-audit services to their audit clients. This study examines the effects of this regulatory change on the effort and reporting decisions of audit partners. We show that partners in an audit firm strategically change the firm's liability-sharing rule. As a consequence, the regulation restores truthful reporting but has an undesirable negative effect on audit effort. The effect of the regulation on the welfare of the economy (defined as the total payoff to both audit firms and their clients) hinges on the tradeoff between the benefit of the regulation, which is derived from the inducement of truthful reporting, and the cost of the regulation, which results from less diligent audit work. We show that the regulation is more likely to increase the welfare in a strong legal regime (where the legal liability cost of auditor litigation is high) than in a weak legal regime. © 2011 Elsevier Inc.postprin

Human biomechanical model based optimal design of assistive shoulder exoskeleton

© Springer International Publishing Switzerland 2015. Robotic exoskeletons are being developed to assist humans in tasks such as robotic rehabilitation, assistive living, industrial and other service applications. Exoskeletons for the upper limb are required to encompass the shoulder whilst achieving a range of motion so as to not impede the wearer, avoid collisions with the wearer, and avoid kinematic singularities during operation. However this is particularly challenging due to the large range of motion of the human shoulder. In this paper a biomechanical model based optimisation is applied to the design of a shoulder exoskeleton with the objective of maximising shoulder range of motion. A biomechanical model defines the healthy range of motion of the human shoulder. A genetic algorithm maximises the range of motion of the exoskeleton towards that of the human, whilst taking into account collisions and kinematic singularities. It is shown how the optimisation can increase the exoskeleton range of motion towards that equivalent of the human, or towards a subset of human range of motion relevant to specific applications

Distributed classifier migration in XCS for classification of electroencephalographic signals

This paper presents an investigation into combining migration strategies inspired by multi-deme Parallel Genetic Algorithms with the XCS Learning Classifier System to provide parallel and distributed classifier migration. Migrations occur between distributed XCS classifier sub-populations using classifiers ranked according to numerosity, fitness or randomly selected. The influence of the degree-of-connectivity introduced by Fully-Connected, Bi-directional Ring and Uni-directional Ring topologies is examined. Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification of noisy, artefact-inclusive human electroencephalographic signals. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE

An algorithm for surface growing from laser scan generated point clouds

In robot applications requiring interaction with a partially/unknown environment, mapping is of paramount importance. This paper presents an effective surface growing algorithm for map building based on laser scan generated point clouds. The algorithm directly converts a point cloud into a surface and normals form which sees a significant reduction in data size and is in a desirable format for planning the interaction with surfaces. It can be used in applications such as robotic cleaning, painting and welding. © 2007 Springer-Verlag Berlin Heidelberg

Classification of EEG signals using a genetic-based machine learning classifier

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE

Distributed simultaneous task allocation and motion coordination of autonomous vehicles using a parallel computing cluster

Task allocation and motion coordination are the main factors that should be consi-dered in the coordination of multiple autonomous vehicles in material handling systems. Presently, these factors are handled in different stages, leading to a reduction in optimality and efficiency of the overall coordination. However, if these issues are solved simultaneously we can gain near optimal results. But, the simultaneous approach contains additional algorithmic complexities which increase computation time in the simulation environment. This work aims to reduce the computation time by adopting a parallel and distributed computation strategy for Simultaneous Task Allocation and Motion Coordination (STAMC). In the simulation experiments, each cluster node executes the motion coordination algorithm for each autonomous vehicle. This arrangement enables parallel computation of the expensive STAMC algorithm. Parallel and distributed computation is performed directly within the interpretive MATLAB environment. Results show the parallel and distributed approach provides sub-linear speedup compared to a single centralised computing node. © 2007 Springer-Verlag Berlin Heidelberg

A prototype climbing robot for inspection of complex ferrous structures

© 2010 by World Scientific Publishing Co. Pte. Ltd. Currently many hazardous maintenance and inspection tasks, such as paint inspection and corrosion condition monitoring of steel structures, are being performed manually by workers, which causes serious health and safety problems. This paper presents a concept climbing robot, with the aim of exploring highly complex ferrous structures such as steel bridges, for the purpose of inspection duties. To demonstrate this concept, a quadruped prototype is developed. A modular architecture that simplifies the development process and improves reusability has been implemented. Permanent magnet compliant pads on each foot provide a simple method of adhesion on the highly complex and unsmooth surface of a bridge. A simple detachment mechanism has been employed. Experiments have been conducted to prove the concept and test the design of the prototype

Infrastructure robotics: Research challenges and opportunities

Infrastructure robotics is about research on and development of methodologies that enable robotic systems to be used in civil infrastructure inspection, maintenance and rehabilitation. This paper briefly discusses the current research challenges and opportunities in infrastructure robotics, and presents a review of the research activities and projects in this field at the Centre for Autonomous Systems, University of Technology Sydney

Collapse scenarios of high-rise buildings using plastic limit analysis

Author name used in this publication: W. K. Chow2009-2010 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe