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dc.contributor.advisorRowlands, David
dc.contributor.authorEngwirda, Anthony
dc.date.accessioned2018-01-23T04:45:35Z
dc.date.available2018-01-23T04:45:35Z
dc.date.issued2007
dc.identifier.doi10.25904/1912/322
dc.identifier.urihttp://hdl.handle.net/10072/368079
dc.description.abstractA Large Scale Robot Colony (LSRC) is a complex artifact comprising of a significant population of both mobile and static robots. LSRC research is in its literary infancy and it is therefore necessary to rely upon external fields for the appropriate framework, Multi Agent Systems (MAS) and Large Scale Systems (LSS). At the intersection of MAS, LSS and LSRC exist near identical issues, problems and solutions. If attention is paid to coherence then solution portability is possible. The issue of Self-Reliability is poorly addressed by the MAS research field. Disparity between the real world and simulation is another area of concern. Despite these deficiencies, MAS and LSS are perceived as the most appropriate frameworks. MAS research focuses on three prime areas, cognitive science, management and interaction. LSRC is focused on Self-Sustainability, Self-Management and Self-Organization. While LSS research was not primarily intended for populations of mobile robots, it does address key issues of LSRC, such as effective sustainability and management. Implementation of LSRC that is based upon the optimal solution for any one or two of the three aspects will be inferior to a coherent solution based upon all three. LSRC’s are complex organizations with significant populations of both static and mobile robots. The increase in population size and the requirement to address the issue of Self-Reliance give rise to new issues. It is no longer sufficient to speak only in terms of robot intelligence, architecture, interaction or team behaviour, even though these are still valid topics. Issues such as population sustainability and management have greater significance within LSRC. As the size of a robot populations increases, minor uneconomical decisions and actions inhibit the performance of the population. Interaction must be made economical within the context of the LSRC. Sustainability of the population becomes significant as it enables stable performance and extended operational lifespan. Management becomes significant as a mechanism to direct the population so as to achieve near optimal performance. The Self-Sustainability, Self-Management and Self-Organization of LSRC are vastly more complex than in team robotics. Performance of the overall population becomes more significant than individual or team achievement. This thesis is a presentation of the Cooperative Autonomous Robot Colony (CARC) architecture. The CARC architecture is novel in that it offers a coherent baseline solution to the issue of mobile robot Self-Reliance. This research uses decomposition as a mechanism to reduce problem complexity. Self-Reliance is decomposed into Self-Sustainability, Self-Management, and Self-Organization. A solution to the issue of Self-Reliance will comprise of conflicting sub-solutions. A product of this research is a set of guidelines that manages the conflict of sub-solutions and maintains a coherent solution. In addressing the issue of Self-Reliance, it became apparent that Economies of Scale, played an important role. The effects of Economies of Scale directed the research towards LSRC’s. LSRC’s demonstrated improved efficiency and greater capability to achieve the requirements of Self-Reliance. LSRC’s implemented with the CARC architecture would extend human capability, enabling large scale operations to be performed in an economical manner, within real world and real time environments, including those of a remote and hostile nature. The theory and architecture are supported using published literature, experiments, observations and mathematical projections. Contributions of this work are focused upon the three pillars of Self-Reliance addressed by CARC: Self-Sustainability, Self-Management and Self-Organization. The chapter on Self-Sustainability explains and justifies the relevance of this issue, what it is, why it is important and how it can be achieved. Self-Sustainability enables robots to continue to operate beyond disabling events by addressing failure and routine maintenance. Mathematical projections are used to compare populations of non-sustained and sustained robots. Computer modeling experiments are used to demonstrate the feasibility of Self-Sustainability, including extended operational life, the maintenance of optimal work flow and graceful physical degradation (GPD). A detailed explanation is presented of Sustainability Functions, Colony Sites, Static Robot Roles, Static Robot Failure Options, and Polymorphism. The chapter on Self-Management explores LSS research as a mechanism to exert influence over a LSRC. An experimental reactive management strategy is demonstrated. This strategy while limited does indicate promising potential directions for future research including the Man in the Loop (MITL) strategy highly desired by NASA JPL for off world command and control of a significant robot colony (Huntsberger, et. al., 2000). Experiments on Communication evaluate both Broadcast Conveyance (BC) and Message Passing Conveyance (MPC). These experiments demonstrate the potential of Message Passing as a low cost system for LSRC communication. Analysis of Metrics indicates that a Performance Based Feedback Method (PBFM) and a Task Achievement Method (TAM) are both necessary and sufficient to monitor a LSRC. The chapter on Self-Organization describes a number of experiments, algorithms and protocols on Reasoning Robotics, a minor variant of Reactive Robotics. Reasoning Robotics utilizes an Event Driven Architecture (EDA) rather than a Stimulus Driven Architecture (SDA) common to Reactive Robotics. Enhanced robot performance is demonstrated by a combination of EDA and environmental modification enabling stigmergy. These experiments cover Intersection Navigation with contingency for Multilane Intersections, a Radio Packet Controller (RPC) algorithm, Active and Passive Beacons including a communication protocol, mobile robot navigation using Migration Decision Functions (MDF’s), including MDF positional errors. The central issue addressed by this thesis is the production of Self-Reliance guidelines for LSRC’s. Self-Reliance is perceived as a critical issue in advancing the useful and productive applications for LSRC’s. LSRC’s are complex with many issues in related fields of MAS and LSS. Decomposition of Self-Reliance into Self-Sustainability, Self-Management and Self-Organization were used to aid in problem understanding. It was found that Self-Sustainability extends the operational life of individual robots and the LSRC. Self-Management enables the exertion of human influence over the LSRC, such that the ratio of humans to robots is reduced but not eliminated. Self-Organization achieves and enhances performance through a routine and reliable LSRC environment. The product of this research was the novel CARC architecture, which consists of a set of Self-Reliance guidelines and algorithms. The Self-Reliance guidelines manage conflict between optimal solutions and provide a framework for LSRC design. This research was supported by literature, experiments, observations and mathematical projections.
dc.languageEnglish
dc.publisherGriffith University
dc.publisher.placeBrisbane
dc.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.
dc.subject.keywordsLarge Scale Robot Colony
dc.subject.keywordsLSRC
dc.subject.keywordsmobile and static robots
dc.subject.keywordsMulti Agent Systems
dc.subject.keywordsMAS
dc.subject.keywordsLarge Scale Systems
dc.subject.keywordsLSS
dc.titleSelf-Reliance Guidelines for Large Scale Robot Colonies
dc.typeGriffith thesis
gro.facultyFaculty of Engineering and Information Technology
gro.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.
gro.hasfulltextFull Text
dc.rights.accessRightsPublic
gro.identifier.gurtIDgu1315461256745
gro.identifier.ADTnumberadt-QGU20070913.100750
gro.source.ADTshelfnoADT0572
gro.thesis.degreelevelThesis (PhD Doctorate)
gro.thesis.degreeprogramDoctor of Philosophy (PhD)
gro.departmentGriffith School of Engineering
gro.griffith.authorEngwirda, Anthony E.


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