Author: Richard Ellis
Ellis, Richard, 2021 Developing a flexible and adaptive shipyard fleet for last mile delivery, Flinders University, College of Science and Engineering
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As part of the Ship building program, Defence Company Australia is interested in implementing Autonomous Ground Vehicles (AGVs) and potentially aerial drones to move components and materials to various locations in the Shipyard. This is intended to not only increase cost efficiencies but also improve safety for workers by way of reduction of heavy lifting.
Consequently, the objective of this project is to explore the development of a flexible and adaptive AGV system that can be applied to the shipyard once building locations have been confirmed.
It would address the key research question of ‘how can autonomous vehicles be used for effective fleet delivery in an adaptive system allowing for last-mile delivery?’
To answer this question the study takes an option based approach to developing the shipyard AGV model, where various options are evaluated to see which option is more ideal.
by evaluating the different options and using the pros from previous options an adaptive shipyard model was successfully implemented where the drop off, pickup points and number of AGVs could be controlled. Overall, the system performed as expected and was able to replicate similar trends seen in other AGV models as well as the one from industry. However, the model is not as user friendly and still requires code elements to be adjusted prior to a simulation run.
Further development of the model would involve integrating pedestrians and a better user interface to provide quicker more reliable results between simulations as well as further investigation into the impacts of an AGV system in terms of human factors through interviews of current employees for which the system will work alongside.
Nevertheless, the study made a valuable contribution in addressing the key problem of how AGV fleet management systems can be applied in shipbuilding.
Keywords: AGV, AGVs, Automated Guided Vehicles, Collision Avoidance, Deadlock, Decision Making, Flexible Manufacturing, Logistics, Material Handling, Scheduling
Subject: Engineering thesis
Thesis type: Masters
Completed: 2021
School: College of Science and Engineering
Supervisor: Giselle Rampersad