Non-man-entry sewer renovation robot characteristics.

BROADHURST, Simon John. (2000). Non-man-entry sewer renovation robot characteristics. Doctoral, Sheffield Hallam University (United Kingdom)..

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The reported work lies in the area of automation in the construction industry, and involves multi-disciplinary engineering studies. In particular, sewer renovation methods, computer vision (CV) and robotics are all included. More specifically, the key objective of the research programme was to investigate the characteristics of retrofit components suited to mounting on an industrial / proprietary sewer tractor. The overall aim was the provision of a non-man-entry (NME) sewer renovation robot to undertake reconnection of lateral junctions, following a cured-in-place (CIP) relining process. The programme primarily involved theoretical studies of the requisite sensory and kinematic components, incorporation of a novel computer vision sensing system and production of a chainage measurement system and robotic drill task arm. The theory was supported by laboratory testing using a modified proprietary tractor, with emphasis placed on promoting applications of information technology driven systems (i.e. CV) to construction-industry tasks within hazardous environments involving significant health issues. The use of such techniques in the construction industry is rare.Chapter 1 reviews the context and history of sewer maintenance/dereliction in the UK. NME sewers are the most common type and are, by definition, difficult to maintain. Renovation, typically employing CIP liners, is therefore a cost-effective alternative to replacement. Lateral connections are, inevitably, blocked off during the relining process; it is suggested that application of a robust robotic system to the task of reconnecting them is novel and offers clear potential within such a hazardous environment.Chapters 2 and 3 develop the underlying theoretical models of the CV and kinematic systems respectively. The novel CV work (provided by third party specialists employing the TINA CV research environment) was incorporated by the author to provide detection and classification of lateral junctions, crucially noting the particular properties of direct and reflected illumination. Classification aspects include estimation of lateral / NME intersection angle and closure-to-target distance from the robot. The author proposes a separate procedure for estimating lateral diameter. A chainage measurement system, using a rotary encoder and inclinometer, was developed to determine invert path distance travelled. This allows for the inevitable wander and thereby gives the system robustness. The novel application of GRASP (a robotic modelling and simulation design tool) to NME environments, provided the ability to model arm designs without the need for the production of more than one expensive physical prototype. A mathematical solution for determining the requisite arm kinematics is presented.Chapter 4 details the hardware requirements of the robotic system components, whilst Chapters 5 and 6 present the laboratory evaluation results for the kinematic and CV systems respectively. The abilities of the CV system qualitatively to detect laterals under reflected illumination, and to provide quantitative classification data, are demonstrated. The chainage measurement system is assessed under a variety of initialisation conditions to determine suitability to task, and the ability of the robotic arm to physically simulate lateral reconnection is investigated.Chapter 7 discusses the specification for an industrially-applicable prototype, based on the findings herein. Appropriate comparisons with the pre-prototype system are made, including cost. Finally, Chapter 8 draws conclusions and makes suggestions for further work. Supporting documentation is provided in Chapter 9 and the Appendices.

Item Type: Thesis (Doctoral)
Thesis advisor - Taylor, N.
Thesis advisor - Cockerham, Graham
Thesis advisor - Pridmore, T.
Additional Information: Thesis (Ph.D.)--Sheffield Hallam University (United Kingdom), 2000.
Research Institute, Centre or Group - Does NOT include content added after October 2018: Sheffield Hallam Doctoral Theses
Depositing User: EPrints Services
Date Deposited: 10 Apr 2018 17:18
Last Modified: 26 Apr 2021 11:32

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