Rehman, Faheem Ur;
(2024)
Underwater transportation of a Payload using multiple Hovering Autonomous Underwater Vehicles (HAUVs).
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Underwater transportation is likely to become an important requirement for both the naval and commercial sectors; it reduces the chances of detection and provides an opportunity for the precise placement of offshore underwater installations and devices. However, there is currently no established approach for multivehicular underwater transportation. The aim of this work is therefore to determine which would be the best approach to successfully transport a payload using multiple Hovering Autonomous Underwater Vehicles (HAUVs) and how the risk of collision with other surfaces and objects could be reduced or eliminated. Dynamic models were developed for three transportation systems: Rigid Connection Transportation System (RCTS), Dynamic Connection Transportation System (DCTS), and Leader-Follower Formation Control Transportation System (LFFCTS). A centralised control system for RCTS and DCTS and a distributed control system for LFFCTS were designed to deliver the desired motion responses. Among transportation systems, RCTS was found to be the most appropriate option. However, the advantages offered by LFFCTS and DCTS cannot be denied when there is a need to traverse through a narrow channel or avoid an obstacle. The sensor-based wall-following method was found to be better than the sensor-based hard-switching method in terms of external collision avoidance. When comparing the Rapidly-exploring Random Tree Star (RRT*) path-planning and wall-following methods, the RRT* consumed higher power owing to the oscillation of thrust forces. The RRT* path-planning and wall-following methods were blended to avoid both initially known and unknown obstacles. This research provides clear guidance for evaluating multivehicular underwater transportation systems under a set of different conditions and selecting the best strategy considering different environmental and sensory conditions. Future work could consider using a machine-learning-based neural network for control system design to improve the performance of transportation systems.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Underwater transportation of a Payload using multiple Hovering Autonomous Underwater Vehicles (HAUVs) |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10185963 |
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