Genetic-Based Optimisation Technique for the Development of Automated Inspection and Restoration Systems for Bridges

Abstract

Automation and robotics are receiving significant attention in the field of inspection and restoration of steel bridges. However, the success level of the field implementations depends on numerous technological factors. This dissertation addresses aspects of the design, development and subsequent implementation of such on-site devices. The restoration process poses a high level of health hazard and carries environmental pollution risk. For these reasons, it is high on the consideration list for automation. The varied scale and geometry of bridges are some of the limiting conditions for performing the inspection and restoration tasks. Further aspects of concern are access provisions, the diversity of tasks required in the assessment and restoration of a bridge and compatibility between the operational characteristics of the automated device, tasks layout and direction of approach. The key factors, which arise as a result of the above analysis, are access, mobility, navigation, manipulation, probe change and control. In order to efficiently produce design alternatives, based on the industry (customers and designers) requirements, the engineering design framework is adopted. Due to the growing complexity of the required devices, new methodologies and approaches are needed. This dissertation presents a design methodology to generate alternatives for further considerations. The author's work combines: (i) research and suitability assessment of the existing enabling technologies, (ii) extensive task selection and analysis, (iii) incorporation of the industry requirements for generating the set of design criteria, and (iv) an innovative application of Genetic Algorithms. GA is used as a tool for simultaneous optimisation of the robot’s kinematic parameters, based on the criteria of collision and singularity avoidance, percentage of coverage, productivity and dexterity. Analysis and justification of a two-step approach is presented, with the former combining all the parameters, and the latter handling the chosen criteria. The methodology is then tested and verified on an existing construction robot (MPIR) from Technion. Finally, it is applied to two case studies, spherical and articulated manipulators performing a range of restoration activities on a selected bridge geometry model. A sensitivity analysis was also carried out on each case study in order to identify areas where improvements could be made. In general, the methodology is successful in choosing the more task-suitable manipulator and optimising the ranges of its kinematic parameters. This could be extended to optimise other parameters according to a set of alternative criteria. In doing so, it can bridge over several phases of the engineering design with a single approach