Hybridized trajectory generation and many-objectives optimization for multi-agent quadrotor UAVs
This research generates a large collection of optimized trajectories for multi-agent quadrotors. The hybridized algorithm extracts trajectories with various trade-off for all agents without discrimination. This allows the resources of all agents to contribute towards the completion of a task. Tw...
| Main Author: | |
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2018
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| Online Access: | https://eprints.nottingham.ac.uk/48060/ |
| _version_ | 1848797681695064064 |
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| author | Tharumanathan, Premeela |
| author_facet | Tharumanathan, Premeela |
| author_sort | Tharumanathan, Premeela |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This research generates a large collection of optimized trajectories for multi-agent quadrotors. The hybridized algorithm extracts trajectories with various trade-off for all agents without discrimination. This allows the resources of all agents to contribute towards the completion of a task.
Two variations of multi-agent quadrotor missions are applied within this work. The first is spatially spread flight mission, MA-SPREAD whereas the second is formation flight, MA-FORMATION. The trajectories are designed within three environments: i) Highly Cluttered Indoor, ii) Cityscape and iii) Mountainous terrain. The initial path nodes are generated through a sampling based planner. Here, Rapidly Exploring Random Trees is expanded into Multi-Agent Rapidly Exploring Random Forest. These paths are used to form the initial population for Genetic Algorithm. Next, we apply Many-Objectives Optimization towards the optimization of all agents and its objectives.
This study strikes a balance between diverse and well minimized solutions through dimensionality reduction. Result shows that the algorithm can successfully find a diverse set of well minimized solutions within each environment. The end user will be supplied with high resolution visual imagery of each test environment and well-organized data that defines the trade-offs of each trajectory. These easy to understand information will assist the end user in making a final choice regarding the best multi-agent quadrotor trajectories for their mission. |
| first_indexed | 2025-11-14T20:07:45Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-48060 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:07:45Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-480602025-02-28T13:55:24Z https://eprints.nottingham.ac.uk/48060/ Hybridized trajectory generation and many-objectives optimization for multi-agent quadrotor UAVs Tharumanathan, Premeela This research generates a large collection of optimized trajectories for multi-agent quadrotors. The hybridized algorithm extracts trajectories with various trade-off for all agents without discrimination. This allows the resources of all agents to contribute towards the completion of a task. Two variations of multi-agent quadrotor missions are applied within this work. The first is spatially spread flight mission, MA-SPREAD whereas the second is formation flight, MA-FORMATION. The trajectories are designed within three environments: i) Highly Cluttered Indoor, ii) Cityscape and iii) Mountainous terrain. The initial path nodes are generated through a sampling based planner. Here, Rapidly Exploring Random Trees is expanded into Multi-Agent Rapidly Exploring Random Forest. These paths are used to form the initial population for Genetic Algorithm. Next, we apply Many-Objectives Optimization towards the optimization of all agents and its objectives. This study strikes a balance between diverse and well minimized solutions through dimensionality reduction. Result shows that the algorithm can successfully find a diverse set of well minimized solutions within each environment. The end user will be supplied with high resolution visual imagery of each test environment and well-organized data that defines the trade-offs of each trajectory. These easy to understand information will assist the end user in making a final choice regarding the best multi-agent quadrotor trajectories for their mission. 2018-02-24 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by_nc_sa https://eprints.nottingham.ac.uk/48060/1/PREMEELA%20T.%20NATHAN%20THESIS.pdf Tharumanathan, Premeela (2018) Hybridized trajectory generation and many-objectives optimization for multi-agent quadrotor UAVs. PhD thesis, University of Nottingham. |
| spellingShingle | Tharumanathan, Premeela Hybridized trajectory generation and many-objectives optimization for multi-agent quadrotor UAVs |
| title | Hybridized trajectory generation and many-objectives optimization for multi-agent quadrotor UAVs |
| title_full | Hybridized trajectory generation and many-objectives optimization for multi-agent quadrotor UAVs |
| title_fullStr | Hybridized trajectory generation and many-objectives optimization for multi-agent quadrotor UAVs |
| title_full_unstemmed | Hybridized trajectory generation and many-objectives optimization for multi-agent quadrotor UAVs |
| title_short | Hybridized trajectory generation and many-objectives optimization for multi-agent quadrotor UAVs |
| title_sort | hybridized trajectory generation and many-objectives optimization for multi-agent quadrotor uavs |
| url | https://eprints.nottingham.ac.uk/48060/ |