{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T17:35:37Z","timestamp":1782408937997,"version":"3.54.5"},"reference-count":35,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T00:00:00Z","timestamp":1691539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFF1101103"],"award-info":[{"award-number":["2022YFF1101103"]}]},{"name":"National Key Research and Development Program of China","award":["2020YFC1606801"],"award-info":[{"award-number":["2020YFC1606801"]}]},{"name":"National Key Research and Development Program of China","award":["4222042"],"award-info":[{"award-number":["4222042"]}]},{"name":"Beijing Municipal Natural Science Foundation","award":["2022YFF1101103"],"award-info":[{"award-number":["2022YFF1101103"]}]},{"name":"Beijing Municipal Natural Science Foundation","award":["2020YFC1606801"],"award-info":[{"award-number":["2020YFC1606801"]}]},{"name":"Beijing Municipal Natural Science Foundation","award":["4222042"],"award-info":[{"award-number":["4222042"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A single unmanned surface combatant (USV) has poor mission execution capability, so the cooperation of multiple unmanned surface ships is widely used. Cooperative hunting is an important aspect of multi USV collaborative research. Therefore, this paper proposed a cooperative hunting method for multi-USV based on the A* algorithm in an environment with obstacles. First, based on the traditional A* algorithm, a path smoothing method based on USV minimum turning radius is proposed. At the same time, the post order traversal recursive algorithm in the binary tree method is used to replace the enumeration algorithm to obtain the optimal path, which improves the efficiency of the A* algorithm. Second, a biomimetic multi USV swarm collaborative hunting method is proposed. Multiple USV clusters simulate the hunting strategy of lions to pre-form on the target\u2019s path, so multiple USV clusters do not require manual formation. During the hunting process, the formation of multiple USV groups is adjusted to limit the movement and turning of the target, thereby reducing the range of activity of the target and improving the effectiveness of the algorithm. To verify the effectiveness of the algorithm, two sets of simulation experiments were conducted. The results show that the algorithm has good performance in path planning and target search.<\/jats:p>","DOI":"10.3390\/s23167058","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T10:30:48Z","timestamp":1691577048000},"page":"7058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Cooperative Hunting Method for Multi-USV Based on the A* Algorithm in an Environment with Obstacles"],"prefix":"10.3390","volume":"23","author":[{"given":"Zhihao","family":"Chen","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8565-4430","authenticated-orcid":false,"given":"Zhiyao","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"},{"name":"China Food Flavor and Nutrition Health Innovation Center, Beijing Technology and Business University, Beijing 100048, China"},{"name":"Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiping","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"},{"name":"China Food Flavor and Nutrition Health Innovation Center, Beijing Technology and Business University, Beijing 100048, China"},{"name":"Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"},{"name":"Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China"},{"name":"School of Arts and Sciences, Beijing Institute of Fashion Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4261-1585","authenticated-orcid":false,"given":"Jiabin","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"},{"name":"China Food Flavor and Nutrition Health Innovation Center, Beijing Technology and Business University, Beijing 100048, China"},{"name":"Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yu, J., Liu, G., Xu, J., Zhao, Z., Chen, Z., Yang, M., Wang, X., and Bai, Y. 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