Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios
Unmanned aerial vehicles (UAVs) are widely used in situations with uncertain and risky areas lacking network coverage. In natural disasters, timely delivery of first aid supplies is crucial. Current UAVs face risks such as crashing into birds or unexpected structures. Airdrop systems with parachutes...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Journal of Electronic Science and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674862X25000047 |
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| author | Zarina Kutpanova Mustafa Kadhim Xu Zheng Nurkhat Zhakiyev |
| author_facet | Zarina Kutpanova Mustafa Kadhim Xu Zheng Nurkhat Zhakiyev |
| author_sort | Zarina Kutpanova |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) are widely used in situations with uncertain and risky areas lacking network coverage. In natural disasters, timely delivery of first aid supplies is crucial. Current UAVs face risks such as crashing into birds or unexpected structures. Airdrop systems with parachutes risk dispersing payloads away from target locations. The objective here is to use multiple UAVs to distribute payloads cooperatively to assigned locations. The civil defense department must balance coverage, accurate landing, and flight safety while considering battery power and capability. Deep Q-network (DQN) models are commonly used in multi-UAV path planning to effectively represent the surroundings and action spaces. Earlier strategies focused on advanced DQNs for UAV path planning in different configurations, but rarely addressed non-cooperative scenarios and disaster environments. This paper introduces a new DQN framework to tackle challenges in disaster environments. It considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return. A new DQN model is developed, which incorporates the battery life, safe flying distance between UAVs, and remaining delivery points to encode surrounding hazards into the state space and Q-networks. Additionally, a unique reward system is created to improve UAV action sequences for better delivery coverage and safe landings. The experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance. |
| format | Article |
| id | doaj-art-098c8c4c2c344e4ab5f003da06f8977f |
| institution | Kabale University |
| issn | 2666-223X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Electronic Science and Technology |
| spelling | doaj-art-098c8c4c2c344e4ab5f003da06f8977f2025-08-20T03:30:44ZengKeAi Communications Co., Ltd.Journal of Electronic Science and Technology2666-223X2025-06-0123210030310.1016/j.jnlest.2025.100303Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenariosZarina Kutpanova0Mustafa Kadhim1Xu Zheng2Nurkhat Zhakiyev3Department of Computer Engineering, Astana IT University, Astana, 010000, KazakhstanSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China; Corresponding author.Department of Computational and Data Science, Astana IT University, Astana, 010000, KazakhstanUnmanned aerial vehicles (UAVs) are widely used in situations with uncertain and risky areas lacking network coverage. In natural disasters, timely delivery of first aid supplies is crucial. Current UAVs face risks such as crashing into birds or unexpected structures. Airdrop systems with parachutes risk dispersing payloads away from target locations. The objective here is to use multiple UAVs to distribute payloads cooperatively to assigned locations. The civil defense department must balance coverage, accurate landing, and flight safety while considering battery power and capability. Deep Q-network (DQN) models are commonly used in multi-UAV path planning to effectively represent the surroundings and action spaces. Earlier strategies focused on advanced DQNs for UAV path planning in different configurations, but rarely addressed non-cooperative scenarios and disaster environments. This paper introduces a new DQN framework to tackle challenges in disaster environments. It considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return. A new DQN model is developed, which incorporates the battery life, safe flying distance between UAVs, and remaining delivery points to encode surrounding hazards into the state space and Q-networks. Additionally, a unique reward system is created to improve UAV action sequences for better delivery coverage and safe landings. The experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance.http://www.sciencedirect.com/science/article/pii/S1674862X25000047Deep Q-networkFirst aid deliveryMulti-UAV path planningReinforcement learningUnmanned aerial vehicle (UAV) |
| spellingShingle | Zarina Kutpanova Mustafa Kadhim Xu Zheng Nurkhat Zhakiyev Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios Journal of Electronic Science and Technology Deep Q-network First aid delivery Multi-UAV path planning Reinforcement learning Unmanned aerial vehicle (UAV) |
| title | Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios |
| title_full | Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios |
| title_fullStr | Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios |
| title_full_unstemmed | Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios |
| title_short | Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios |
| title_sort | multi uav path planning for multiple emergency payloads delivery in natural disaster scenarios |
| topic | Deep Q-network First aid delivery Multi-UAV path planning Reinforcement learning Unmanned aerial vehicle (UAV) |
| url | http://www.sciencedirect.com/science/article/pii/S1674862X25000047 |
| work_keys_str_mv | AT zarinakutpanova multiuavpathplanningformultipleemergencypayloadsdeliveryinnaturaldisasterscenarios AT mustafakadhim multiuavpathplanningformultipleemergencypayloadsdeliveryinnaturaldisasterscenarios AT xuzheng multiuavpathplanningformultipleemergencypayloadsdeliveryinnaturaldisasterscenarios AT nurkhatzhakiyev multiuavpathplanningformultipleemergencypayloadsdeliveryinnaturaldisasterscenarios |