1.上海交通大学 海洋智能装备与系统集成技术教育部重点实验室,上海 200240
2.上海交通大学 海洋工程国家重点实验室,上海 200240
[ "徐明华,男,硕士研究生" ]
[ "王斌,男,教授" ]
收稿:2025-05-02,
网络首发:2025-06-24,
纸质出版:2025-08-05
移动端阅览
徐明华, 金建钢, 王斌. 水声通信多约束下水下无人航行器集群路径规划策略[J]. 哈尔滨工程大学学报, 2025,46(8):1583-1592.
Minghua XU, Jiangang JIN, Bin WANG. Path planning for underwater unmanned vehicle cluster mine detection in weak communication environments[J]. Journal of Harbin Engineering University, 2025, 46(8): 1583-1592.
徐明华, 金建钢, 王斌. 水声通信多约束下水下无人航行器集群路径规划策略[J]. 哈尔滨工程大学学报, 2025,46(8):1583-1592. DOI: 10.11990/jheu.202505001.
Minghua XU, Jiangang JIN, Bin WANG. Path planning for underwater unmanned vehicle cluster mine detection in weak communication environments[J]. Journal of Harbin Engineering University, 2025, 46(8): 1583-1592. DOI: 10.11990/jheu.202505001.
针对水下无人航行器集群在水声通信影响下水雷探测任务的路径规划与任务分配需求,本文提出了一种基于自适应大邻域算法的两阶段协同探测方法。通过“粗扫全覆盖+精扫路径规划”策略,结合通信约束动态优化水下无人航行器航速,利用多水下无人航行器协同完成海域粗扫并定位可疑目标区域。随后对可疑目标区域进行精细化扫描并以探测效率最优为目标,采用容量限制的水下无人航行器路径规划模型对多个可疑目标区域进行路径规划。数值仿真结果表明:本文提出的方法能根据海域面积、通信带宽及水下无人航行器数量生成高效探测方案,边际效益量化模型验证了其经济性与有效性。
A two-stage cooperative detection method based on an adaptive large neighborhood search algorithm was developed to fulfill the requirements for planning the path and allocating tasks for underwater unmanned vehicle (UUV) clusters in mine detection missions governed by underwater acoustic communication. Adopting a "coarse sweeping full coverage + fine sweeping path planning" strategy
the method first dynamically optimizes the speed of the UUV by incorporating communication constraints
enabling multi-UUV collaboration to achieve coarse sweeping of the sea area and the location of suspicious target regions. Subsequently
a refined scanning of the suspicious target regions is conducted. Moreover
aiming to maximize the detection efficiency
a capacitated vehicle routing problem model is employed for path planning across multiple suspicious target regions. The numerical simulation demonstrates that the proposed method can generate high-efficiency detection schemes based on the sea area
communication bandwidth
and the number of UUVs. The marginal benefit quantification model further verifies the cost-effectiveness and efficacy of this approach.
陈昭, 丁一杰, 张治强. 无人潜航器发展历程及运用优势研究[J]. 舰船科学技术, 2024, 46(23): 98-102.
CHEN Zhao, DING Yijie, ZHANG Zhiqiang. Research on the development history and application advantages of unmanned underwater vehicle[J]. Ship science and technology, 2024, 46(23): 98-102.
殷虎, 石磊鑫. 多UUV集群协同作业技术研究现状及发展趋势分析[J]. 舰船电子工程, 2024, 44(2): 4-9.
YIN Hu, SHI Leixin. Research status and development trend of multi-UUV cluster collaborative work technology[J]. Ship electronic engineering, 2024, 44(2): 4-9.
张建库. 海洋环境下UUV协同任务规划方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2019.
ZHANG Jianku. Research on UUV collaborative task planning method in marine environment[D]. Harbin: Harbin Engineering University, 2019.
LE THI H A, NGUYEN D M, PHAM DINH T. A DC programming approach for planning a multisensor multizone search for a target[J]. Computers&operations research, 2014, 41: 231-239.
张汝波, 李建军, 杨玉. 基于改进蚁群算法的AUV航路避障任务规划[J]. 华中科技大学学报(自然科学版), 2015, 43(S1): 428-430.
ZHANG Rubo, LI Jianjun, YANG Yu. AUV route planning study for obstacle avoidance task based on improved ant colony algorithm[J]. Journal of Huazhong University of Science and Technology (natural science edition), 2015, 43(S1): 428-430.
WU Lian, LI Yiping, LIU Jian. Based on improved bio-inspired model for path planning by multi-AUV[C]//Proceedings of the 2018 International Conference on Electronics and Electrical Engineering Technology. New York: ACM, 2018: 123-135.
YAN Zheping, LI Jiyun, WU Yi, et al. A real-time path planning algorithm for AUV in unknown underwater environment based on combining PSO and waypoint guidance[J]. Sensors, 2019, 19(1): 20.
SOLARI F J, ROZENFELD A F, VILLAR S A, et al. Artificial potential fields for the obstacles avoidance system of an AUV using a mechanical scanning sonar[C]//2016 3rd IEEE/OES South American International Symposium on Oceanic Engineering (SAISOE). Piscataway, NJ, 2016: 1-6.
HU X, CAO Z, CHEN Y. Dynamic task assignment and path optimization for multi-AUVs system[J]. IEEE transactions on intelligent transportation systems, 26[2025-07-24]
孙伟昌, 罗志浩, 石建迈, 等. 无人机覆盖路径规划方法综述[J/OL ] . 控制理论与应用, 2024: 1-21. (2024-12-02). https://kns.cnki.net/kcms/detail/44.1240.TP.20241130.0849.004.html https://kns.cnki.net/kcms/detail/44.1240.TP.20241130.0849.004.html .
SUN Weichang, LUO Zhihao, SHI Jianmai, et al. Overview of UAV coverage path planning methods[J/OL ] . Control theory & applications, 2024: 1-21. (2024-12-02). https://kns.cnki.net/kcms/detail/44.1240.TP.20241130.0849.004.html https://kns.cnki.net/kcms/detail/44.1240.TP.20241130.0849.004.html .
严浙平, 刘祥玲. 多UUV协调控制技术研究现状及发展趋势[J]. 水下无人系统学报, 2019, 27(3): 226-231.
YAN Zheping, LIU Xiangling. Research status and development trend of multi-UUV coordinated control technology: a review[J]. Journal of unmanned undersea systems, 2019, 27(3): 226-231.
ZHAO Zhenyi, HU Qiao, FENG Haobo, et al. A cooperative hunting method for multi-AUV swarm in underwater weak information environment with obstacles[J]. Journal of marine science and engineering, 2022, 10(9): 1266.
YAN Zheping, ZHANG Chao, TIAN Weida, et al. Formation trajectory tracking control of discrete-time multi-AUV in a weak communication environment[J]. Oceanengineering, 2022, 245: 110495.
王晗. 基于互补码键控的水声通信关键技术研究[D]. 西安: 西北工业大学, 2020.
WANG Han. Study on the Key Techniques of Underwater Acoustic Communication based on Complementary Code Keying[D]. Xi'an: Northwestern Polytechnical University, 2020.
KILFOYLE D B, BAGGERER A B. Thestate of the art in underwater acoustic telemetry[J]. IEEE joumal of oceanic engineering, 25.1(2000): 4-27.
ROPKE S, PISINGER D. An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows[J]. Transportation science, 2006, 40(4): 455-472.
0
浏览量
127
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010602201714号