Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour
© 2019 Elsevier B.V. The foraging behaviour of bacteria in colonies exhibits motility patterns that are simple and reasoned by stimuli. Notwithstanding its simplicity, bacteria behaviour demonstrates a level of intelligence that can feasibly inspire the creation of solutions to address numerous opti...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/1721.1/136384 |
| _version_ | 1826199974228000768 |
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| author | Kurdi, Heba AlDaood, Munirah F Al-Megren, Shiroq Aloboud, Ebtesam Aldawood, Abdulrahman S Youcef-Toumi, Kamal |
| author_facet | Kurdi, Heba AlDaood, Munirah F Al-Megren, Shiroq Aloboud, Ebtesam Aldawood, Abdulrahman S Youcef-Toumi, Kamal |
| author_sort | Kurdi, Heba |
| collection | MIT |
| description | © 2019 Elsevier B.V. The foraging behaviour of bacteria in colonies exhibits motility patterns that are simple and reasoned by stimuli. Notwithstanding its simplicity, bacteria behaviour demonstrates a level of intelligence that can feasibly inspire the creation of solutions to address numerous optimisation problems. One such challenge is the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) to perform cooperative tasks for future autonomous systems. In light of this, this paper proposes a bacteria-inspired heuristic for the efficient distribution of tasks amongst deployed UAVs. The usage of multi-UAVs is a promising concept to combat the spread of the red palm weevil (RPW) in palm plantations. For that purpose, the proposed bacteria-inspired heuristic was utilised to resolve the multi-UAV task allocation problem when combating RPW infestation. The performance of the proposed algorithm was benchmarked in simulated detect-and-treat missions against three long-standing multi-UAV task allocation strategies, namely opportunistic task allocation, auction-based scheme, and the max-sum algorithm, and a recently introduced locust-inspired algorithm for the allocation of multi-UAVs. The experimental results demonstrated the superior performance of the proposed algorithm, as it substantially improved the net throughput and maintained a steady runtime performance under different scales of fleet sizes and number of infestations, thereby expressing the high flexibility, scalability, and sustainability of the proposed bacteria-inspired approach. |
| first_indexed | 2024-09-23T11:28:46Z |
| format | Article |
| id | mit-1721.1/136384 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:28:46Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1363842021-10-28T04:16:03Z Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour Kurdi, Heba AlDaood, Munirah F Al-Megren, Shiroq Aloboud, Ebtesam Aldawood, Abdulrahman S Youcef-Toumi, Kamal © 2019 Elsevier B.V. The foraging behaviour of bacteria in colonies exhibits motility patterns that are simple and reasoned by stimuli. Notwithstanding its simplicity, bacteria behaviour demonstrates a level of intelligence that can feasibly inspire the creation of solutions to address numerous optimisation problems. One such challenge is the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) to perform cooperative tasks for future autonomous systems. In light of this, this paper proposes a bacteria-inspired heuristic for the efficient distribution of tasks amongst deployed UAVs. The usage of multi-UAVs is a promising concept to combat the spread of the red palm weevil (RPW) in palm plantations. For that purpose, the proposed bacteria-inspired heuristic was utilised to resolve the multi-UAV task allocation problem when combating RPW infestation. The performance of the proposed algorithm was benchmarked in simulated detect-and-treat missions against three long-standing multi-UAV task allocation strategies, namely opportunistic task allocation, auction-based scheme, and the max-sum algorithm, and a recently introduced locust-inspired algorithm for the allocation of multi-UAVs. The experimental results demonstrated the superior performance of the proposed algorithm, as it substantially improved the net throughput and maintained a steady runtime performance under different scales of fleet sizes and number of infestations, thereby expressing the high flexibility, scalability, and sustainability of the proposed bacteria-inspired approach. 2021-10-27T20:35:08Z 2021-10-27T20:35:08Z 2019 2020-08-14T13:33:53Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136384 en 10.1016/J.ASOC.2019.105643 Applied Soft Computing Journal Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Other repository |
| spellingShingle | Kurdi, Heba AlDaood, Munirah F Al-Megren, Shiroq Aloboud, Ebtesam Aldawood, Abdulrahman S Youcef-Toumi, Kamal Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour |
| title | Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour |
| title_full | Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour |
| title_fullStr | Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour |
| title_full_unstemmed | Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour |
| title_short | Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour |
| title_sort | adaptive task allocation for multi uav systems based on bacteria foraging behaviour |
| url | https://hdl.handle.net/1721.1/136384 |
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