{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T09:09:56Z","timestamp":1785834596852,"version":"3.56.0"},"reference-count":40,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61971048"],"award-info":[{"award-number":["61971048"]}]},{"name":"National Natural Science Foundation of China","award":["2020YFC1511702"],"award-info":[{"award-number":["2020YFC1511702"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFF0607400"],"award-info":[{"award-number":["2022YFF0607400"]}]},{"name":"National Key Research and Development Program of China","award":["61971048"],"award-info":[{"award-number":["61971048"]}]},{"name":"National Key Research and Development Program of China","award":["2020YFC1511702"],"award-info":[{"award-number":["2020YFC1511702"]}]},{"name":"National Key Research and Development Program of China","award":["2022YFF0607400"],"award-info":[{"award-number":["2022YFF0607400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the challenge of coordinated combat involving multiple UAVs in reconnaissance and search attacks, we propose the Multi-UAV Distributed Self-Organizing Cooperative Intelligence Surveillance and Combat (CISCS) strategy. This strategy employs distributed control to overcome issues associated with centralized control and communication difficulties. Additionally, it introduces a time-constrained formation controller to address the problem of unstable multi-UAV formations and lengthy formation times. Furthermore, a multi-task allocation algorithm is designed to tackle the issue of allocating multiple tasks to individual UAVs, enabling autonomous decision-making at the local level. The distributed self-organized multi-UAV cooperative reconnaissance and combat strategy consists of three main components. Firstly, a multi-UAV finite time formation controller allows for the rapid formation of a mission-specific formation in a finite period. Secondly, a multi-task goal assignment module generates a task sequence for each UAV, utilizing an improved distributed Ant Colony Optimization (ACO) algorithm based on Q-Learning. This module also incorporates a colony disorientation strategy to expand the search range and a search transition strategy to prevent premature convergence of the algorithm. Lastly, a UAV obstacle avoidance module considers internal collisions and provides real-time obstacle avoidance paths for multiple UAVs. In the first part, we propose a formation algorithm in finite time to enable the quick formation of multiple UAVs in a three-dimensional space. In the second part, an improved distributed ACO algorithm based on Q-Learning is introduced for task allocation and generation of task sequences. This module includes a colony disorientation strategy to expand the search range and a search transition strategy to avoid premature convergence. In the third part, a multi-task target assignment module is presented to generate task sequences for each UAV, considering internal collisions. This module provides real-time obstacle avoidance paths for multiple UAVs, preventing premature convergence of the algorithm. Finally, we verify the practicality and reliability of the strategy through simulations.<\/jats:p>","DOI":"10.3390\/s23177398","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T10:47:08Z","timestamp":1692874028000},"page":"7398","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4522-3374","authenticated-orcid":false,"given":"Zibin","family":"Liang","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing 100192, China"},{"name":"Ministry of Education Key Laboratory of Modern Measurement & Control Technology, Beijing 100101, China"},{"name":"School of Automation, Beijing Information Science & Technology University, Beijing 100192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing 100192, China"},{"name":"Ministry of Education Key Laboratory of Modern Measurement & Control Technology, Beijing 100101, China"},{"name":"School of Automation, Beijing Information Science & Technology University, Beijing 100192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"Fu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing 100192, China"},{"name":"Ministry of Education Key Laboratory of Modern Measurement & Control Technology, Beijing 100101, China"},{"name":"School of Automation, Beijing Information Science & Technology University, Beijing 100192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Khalil, H., Rahman, S.U., Ullah, I., Khan, I., Alghadhban, A.J., Al-Adhaileh, M.H., Ali, G., and ElAffendi, M. 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