[1]赵 坤,随旭东,梁 静,等.双无人飞行平台的多传感器规划调度[J].郑州大学学报(工学版),2023,44(04):67-73.[doi:10.13705/j.issn.1671-6833.2023.04.007]
　ZHAO Kun,SUI Xudong,LIANG Jing,et al.Multi-Sensor Planning and Scheduling of Dual Unmanned Flight Platforms[J].Journal of Zhengzhou University (Engineering Science),2023,44(04):67-73.[doi:10.13705/j.issn.1671-6833.2023.04.007]
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双无人飞行平台的多传感器规划调度()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年04期
页码:
67-73
栏目:
出版日期:
2023-06-01

文章信息/Info

Title:
Multi-Sensor Planning and Scheduling of Dual Unmanned Flight Platforms
作者:
赵 坤1; 随旭东2;  梁 静2; 岳彩通2; 李功平2; 于坤杰2
1.中国电子科技集团公司 第二十七研究所,河南 郑州 450001, 2.郑州大学 电气与信息工程学院,河南 郑州450001
Author(s):
ZHAO Kun1;  SUI Xudong2;  LIANG Jing2;  YUE Caitong2;  LI Gongping2;  YU Kunjie2
1.27th Institute of China Electronics Technology Group Corporation, Zhengzhou 450001, Henan, 2.School of Electrical and Information Engineering, Zhengzhou University, 450001, Zhengzhou, Henan
关键词:
双无人飞行平台;  传感器调度;  协同工作;  目标捕获;  任务迁移
Keywords:
dual unmanned flight platforms;  sensor scheduling;  collaborative work;  capture target;  task migration
分类号:
TP212；O231
DOI:
10.13705/j.issn.1671-6833.2023.04.007
文献标志码:
A
摘要:
双无人飞行平台的多传感器调度问题具有复杂耦合关系,如何合理匹配航线、传感器及目标是问题的难 点。 为解决双无人飞行平台的多传感器调度中存在的问题,首先,提出了一种航线任务分配机制,通过航线与目标 的位置关系进行任务分配,可以有效分割任务并解决两平台间任务耦合的问题。 其次,对目标及传感器进行预处 理,提前甄别稀缺传感器资源与独立目标以引导后续调度匹配。 再次,进行传感器与目标的匹配,以任务收益为基 础对传感器进行规划调度,根据传感器资源空闲情况对调度冲突航段进行调整。 最后,对未完成既定分配任务的 目标进行任务迁移,根据目标任务剩余情况匹配相应航段的可用空闲传感器进行任务的迁移,提高任务收益。 为 了验证所提出算法的有效性,通过仿真实验与现有算法在 10 个测试问题上进行了比较,实验结果表明,所提算法 在减少大量运行时间的基础上任务收益与稳定性均优于现有算法。
Abstract:
The multi-sensor scheduling problem of dual unmanned flight platforms has a complex coupling relationship, and how to reasonably match flight segments, sensors and targets is an intricate part of the problem. To solve the multi-sensor scheduling problem of dual unmanned flight platforms, firstly, this paper proposes a flight segment task allocation mechanism, which assigns tasks through the position relationship between the flight segment and the target. This mechanism can effectively split the tasks and solve the task coupling problem of the two platforms. Secondly, pre-processing targets and sensors are carried out to identify scarce sensor resources and independent targets in advance to guide subsequent scheduling matching. Then, sensor-target matching is carried out to plan the scheduling of sensors based on the task revenue. The scheduling of conflicting segments is adjusted according to the availability of sensor resources. Finally, task migration is carried out for targets who still need to complete their assigned tasks. The available free sensors in the corresponding flight segments are matched according to the target task remaining to improve the task revenue. In order to verify the effectiveness of the proposed algorithm in this paper, existing algorithms were compared through simulation experiments on 10 test problems. The experimental results show that this algorithm outperforms existing algorithms in task revenue and stability with a significant reduction in running time.

参考文献/References:

[1] JI B F, LI Y Q, CAO D, et al. Secrecy performance analysis of UAV assisted relay transmission for cognitive network with energy harvesting[J]. IEEE Transactions on Vehicular Technology, 2020, 69(7): 7404-7415.

[2] TSOUROS D C, BIBI S, SARIGIANNIDIS P G. A review on UAV-based applications for precision agriculture[J]. Information, 2019, 10(11): 349.
[3] PANDA K G, DAS S, SEN D, et al. Design and deployment of UAV-aided post-disaster emergency network[J]. IEEE Access, 2019, 7: 102985-102999.
[4] 张方方, 张文丽, 王婷婷. 基于速度补偿算法的多机器人编队控制研究[J]. 郑州大学学报(工学版), 2022, 43(2): 1-6, 14.ZHANG F F, ZHANG W L, WANG T T. Research on multi-robot formation control based on speed compensation algorithm[J]. Journal of Zhengzhou University (Engineering Science), 2022, 43(2): 1-6, 14.
[5] 高岳林, 杨钦文, 王晓峰, 等. 新型群体智能优化算法综述[J]. 郑州大学学报(工学版), 2022, 43(3): 21-30.GAO Y L, YANG Q W, WANG X F, et al. Overview of new swarm intelligent optimization algorithms[J]. Journal of Zhengzhou University (Engineering Science), 2022, 43(3): 21-30.
[6] 樊娇, 雷涛, 韩伟, 等. 无人机航迹规划技术研究综述[J]. 郑州大学学报(工学版), 2021, 42(3): 39-46.FAN J, LEI T, HAN W, et al. A survey of UAV path planning[J]. Journal of Zhengzhou University (Enginee-ring Science), 2021, 42(3): 39-46.
[7] 史殿习, 洪臣, 康颖, 等. 面向多无人机协同飞行控制的云系统架构[J]. 计算机学报, 2020, 43(12): 2352-2371.SHI D X, HONG C, KANG Y, et al. Cloud-based control system architecture for multi-UAVs cooperative flight[J]. Chinese Journal of Computers, 2020, 43(12): 2352-2371.
[8] LI Y M, FU C H, DING F Q, et al. Augmented memory for correlation filters in real-time UAV tracking[C]∥2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE, 2020: 1559-1566.
[9] DANELLJAN M, BHAT G, KHAN F S, et al. ATOM: accurate tracking by overlap maximization[C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2019: 4655-4664.
[10] MA C, HUANG J B, YANG X K, et al. Hierarchical convolutional features for visual tracking[C]∥2015 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2015: 3074-3082.
[11] SKOROBOGATOV G, BARRADO C, SALAM E. Multiple UAV systems: a survey[J]. Unmanned Systems, 2020, 8(2): 149-169.
[12] 赵坤, 刘睿, 张龙龙, 等. 无人侦察机多传感器任务规划技术研究[J]. 电子技术与软件工程, 2018(7): 125-126.ZHAO K, LIU R, ZHANG L L, et al. Research on multisensor mission planning technology for unmanned reconnaissance aircraft[J]. Electronic Technology &Software Engineering, 2018(7): 125-126.
[13] 张龙龙, 刘睿, 赵坤, 等. 基于遗传算法的多传感器规划技术[J]. 电子技术与软件工程, 2019(6): 70-72.ZHANG L L, LIU R, ZHAO K, et al. Multi-sensor planning technology based on genetic algorithm[J]. Electronic Technology &Software Engineering, 2019(6): 70-72.
[14] 刘睿, 赵坤. 基于优化算法的多传感器自动规划技术[J]. 电子技术与软件工程, 2021(18): 90-93.LIU R, ZHAO K. Multi-sensor automatic planning technology based on optimization algorithm[J]. Electronic Technology &Software Engineering, 2021(18): 90-93.
[15] EMAMI Y, WEI B, LI K, et al. Joint communication scheduling and velocity control in multi-UAV-assisted sensor networks: a deep reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2021, 70(10): 10986-10998.
[16] KHODAPARAST S S, LU X, WANG P, et al. Deep reinforcement learning based energy efficient multi-UAV data collection for IoT networks[J]. IEEE Open Journal of Vehicular Technology, 2021, 2: 249-260.
[17] ZHENG H X, YUAN J P. An integrated mission planning framework for sensor allocation and path planning of heterogeneous multi-UAV systems[J]. Sensors (Basel, Switzerland), 2021, 21(10): 3557.
[18] FEI S, HASSAN M A, XIAO Y, et al. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat[J]. Precision Agriculture, 2022,8:1-26.
[19] 汪慎文, 张佳星, 褚晓凯, 等. 两阶段搜索的多模态多目标差分进化算法[J]. 郑州大学学报(工学版), 2021, 42(1): 9-14, 110.WANG S W, ZHANG J X, CHU X K, et al. Multimodal multi-objective differential evolution algorithm based on two-stage search[J]. Journal of Zhengzhou University (Engineering Science), 2021, 42(1): 9-14, 110.

更新日期/Last Update: 2023-07-01