Planning trajectory for UAVs using the self-organizing migrating algorithm.
Ensuring efficient and safe trajectory planning for UAVs in complex and dynamic environments is a critical challenge, especially for UAVs that are increasingly deployed in applications like environmental monitoring, disaster management, and surveillance. The primary complications in the safe control...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327016 |
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| author | Quoc Bao Diep Thanh-Cong Truong Ivan Zelinka |
| author_facet | Quoc Bao Diep Thanh-Cong Truong Ivan Zelinka |
| author_sort | Quoc Bao Diep |
| collection | DOAJ |
| description | Ensuring efficient and safe trajectory planning for UAVs in complex and dynamic environments is a critical challenge, especially for UAVs that are increasingly deployed in applications like environmental monitoring, disaster management, and surveillance. The primary complications in the safe control of UAVs include real-time obstacle avoidance, adaptation to unpredictable environmental changes, and coordination among multiple UAVs to prevent collisions. This paper addresses these challenges by proposing a novel approach for UAV trajectory planning that integrates obstacle avoidance and target acquisition. We introduce a new cost function designed to minimize the distance to the target while maximizing the distance from obstacles, effectively balancing these competing objectives to ensure safety and efficiency. To optimize this cost function, we employ the self-organizing migrating algorithm, a swarm intelligence algorithm inspired by the cooperative and competitive behaviors observed in natural organisms. Our method enables UAVs to autonomously generate safe and efficient paths in real-time, adapt to dynamic changes, and scale to large swarms without relying on centralized control. Simulation results across three scenarios-including a complex environment with ten UAVs and multiple obstacles-demonstrate the effectiveness of our approach. The UAVs successfully reach their targets while avoiding collisions, confirming the reliability and robustness of the proposed method. This work contributes to advancing autonomous UAV operations by providing a scalable and adaptable solution for trajectory planning in challenging environments. |
| format | Article |
| id | doaj-art-950ecb5874744e2a893ff6b71fc19a62 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-950ecb5874744e2a893ff6b71fc19a622025-08-20T03:28:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032701610.1371/journal.pone.0327016Planning trajectory for UAVs using the self-organizing migrating algorithm.Quoc Bao DiepThanh-Cong TruongIvan ZelinkaEnsuring efficient and safe trajectory planning for UAVs in complex and dynamic environments is a critical challenge, especially for UAVs that are increasingly deployed in applications like environmental monitoring, disaster management, and surveillance. The primary complications in the safe control of UAVs include real-time obstacle avoidance, adaptation to unpredictable environmental changes, and coordination among multiple UAVs to prevent collisions. This paper addresses these challenges by proposing a novel approach for UAV trajectory planning that integrates obstacle avoidance and target acquisition. We introduce a new cost function designed to minimize the distance to the target while maximizing the distance from obstacles, effectively balancing these competing objectives to ensure safety and efficiency. To optimize this cost function, we employ the self-organizing migrating algorithm, a swarm intelligence algorithm inspired by the cooperative and competitive behaviors observed in natural organisms. Our method enables UAVs to autonomously generate safe and efficient paths in real-time, adapt to dynamic changes, and scale to large swarms without relying on centralized control. Simulation results across three scenarios-including a complex environment with ten UAVs and multiple obstacles-demonstrate the effectiveness of our approach. The UAVs successfully reach their targets while avoiding collisions, confirming the reliability and robustness of the proposed method. This work contributes to advancing autonomous UAV operations by providing a scalable and adaptable solution for trajectory planning in challenging environments.https://doi.org/10.1371/journal.pone.0327016 |
| spellingShingle | Quoc Bao Diep Thanh-Cong Truong Ivan Zelinka Planning trajectory for UAVs using the self-organizing migrating algorithm. PLoS ONE |
| title | Planning trajectory for UAVs using the self-organizing migrating algorithm. |
| title_full | Planning trajectory for UAVs using the self-organizing migrating algorithm. |
| title_fullStr | Planning trajectory for UAVs using the self-organizing migrating algorithm. |
| title_full_unstemmed | Planning trajectory for UAVs using the self-organizing migrating algorithm. |
| title_short | Planning trajectory for UAVs using the self-organizing migrating algorithm. |
| title_sort | planning trajectory for uavs using the self organizing migrating algorithm |
| url | https://doi.org/10.1371/journal.pone.0327016 |
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