贾永楠 , 田似营 , 李擎 . 无人机集群研究进展综述 [J ] . 航空学报 , 2020 , 41 ( 增刊1 ): 4 - 14 .

JIA Y N , TIAN S Y , LI Q . Recent development of unmanned aerial vehicle swarms [J ] . Acta Aeronautica et Astronautica Sinica , 2020 , 41 ( S1 ): 4 - 14 . (in Chinese)

贾高伟 , 王建峰 . 无人机集群任务规划方法研究综述 [J ] . 系统工程与电子技术 , 2021 , 43 ( 1 ): 99 - 111 .

JIA G W , WANG J F . Research review of UAV swarm mission planning method [J ] . Systems Engineering and Electronics , 2021 , 43 ( 1 ): 99 - 111 . (in Chinese)

AFONS R , MAXIMO M , GALVAO R . Task allocation and trajectory planning for multiple agents in the presence of obstacle and connectivity constraints with mixed-integer linear programming [J ] . International Journal of Robust and Nonlinear Control , 2020 , 30 ( 14 ): 5464 - 5491 . DOI: 10.1002/rnc.v30.14 http://doi.org/10.1002/rnc.v30.14 https://onlinelibrary.wiley.com/toc/10991239/30/14 https://onlinelibrary.wiley.com/toc/10991239/30/14

ZHU Q , ZENG H B , ZHENG W , et al. Optimization of task allocation and priority assignment in hard real-time distributed systems [J ] . Acm Transactions on Embedded Computing Systems , 2013 , 11 ( 4 ): 1 - 30 .

ZHU D , HUANG H , YANG S X . Dynamic task assignment and path planning of multi-AUV system based on an improved self-organizing map and velocity synthesis method in three-dimensional underwater workspace [J ] . IEEE Transactions on Cybernetics , 2013 , 43 ( 2 ): 504 - 514 . DOI: 10.1109/TSMCB.2012.2210212 http://doi.org/10.1109/TSMCB.2012.2210212 For a 3-D underwater workspace with a variable ocean current, an integrated multiple autonomous underwater vehicle (AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. The goal is to control a team of AUVs to reach all appointed target locations for only one time on the premise of workload balance and energy sufficiency while guaranteeing the least total and individual consumption in the presence of the variable ocean current. First, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in 3-D ocean environment. The working process involves special definition of the initial neural weights of the SOM network, the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan the shortest path for each AUV to visit the corresponding target in a dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.

杜永浩 , 邢立宁 , 姚锋 , 等 . 航天器任务调度模型、算法与通用求解技术综述 [J ] . 自动化学报 , 2021 , 47 ( 12 ): 2715 - 2741 .

DU Y H , XING L N , YAO F , et al. Survey on models, algorithms and general techniques for spacecraft mission scheduling [J ] . Acta Automatica Sinica , 2021 , 47 ( 12 ): 2715 - 2741 . (in Chinese)

ZHAO B , ZHANG Y W , LIU D R . Adaptive dynamic programming-based cooperative motion/force control for modular reconfigurable manipulators: a joint task assignment approach [J/OL ] . IEEE Transactions on Neural Networks and Learning Systems , 2022 (2022-05-11)[2022-10-21 ] . https://doi.org/ 10.1109/TNNLS.2022.3171828 https://dx.doi.org/10.1109/TNNLS.2022.3171828 .

沈林成 , 陈璟 , 王楠 . 飞行器任务规划技术综述 [J ] . 航空学报 , 2014 , 35 ( 3 ): 593 - 606 . DOI: 10.7527/S1000-6893.2013.0500 http://doi.org/10.7527/S1000-6893.2013.0500 任务规划是各类飞行器尤其是军用飞行器成功遂行任务的有效支撑和重要保证。首先，介绍了飞行器任务规划的基本概念；其次，系统地分析和梳理了任务规划技术的体系结构，从研究方法及对象的角度，将其归纳为面向多飞行器任务分配及协同的行动规划、面向飞行器战术动作实施方法设计的战术动作规划和面向飞行路径生成的航线/轨迹规划3个层次；随后，阐述了国内外飞行器任务规划各层次的研究现状，在问题建模与求解上所形成的代表性方法及其特点，以及飞行器任务规划在军事领域的应用现状；最后，论述了飞行器任务规划的关键技术及发展趋势。

SHEN L C , CHEN J , WANG N . Overview of air vehicle mission planning techniques [J ] . Acta Aeronautica et Astronautica Sinica , 2014 , 35 ( 3 ): 593 - 606 . (in Chinese) DOI: 10.7527/S1000-6893.2013.0500 http://doi.org/10.7527/S1000-6893.2013.0500 Mission planning provides effective support and insurance for aircraft, especially military aircraft in mission success. First, the concept of air vehicle mission planning is introduced. Second, according to the research methodology and objects, the system architecture of mission planning is analyzed, which is divided into three levels: operation planning for multi-vehicle task allocation and coordination, tactic activity planning for the design of air-vehicle tactic activity employment, and route/trajectory planning for flight route or trajectory generation. Then, advances in the three levels of air vehicle mission planning are discussed, and the characteristics of the representative problem formulation and solution approaches are analyzed. Besides, their military applications are discussed. Finally, the key technique and future research trends on air vehicle mission planning are summarized.

LIU J , ANAVATTI S , GARRATT M , et al. Modified continuous ant colony optimisation for multiple unmanned ground vehicle path planning [J ] . Expert Systems with Applications , 2022 , 196 : 116605 . DOI: 10.1016/j.eswa.2022.116605 http://doi.org/10.1016/j.eswa.2022.116605 https://linkinghub.elsevier.com/retrieve/pii/S0957417422000987 https://linkinghub.elsevier.com/retrieve/pii/S0957417422000987

彭鹏菲 , 于钱 , 李启元 . 基于优先排序与粒子群优化的装备保障任务规划方法 [J ] . 兵工学报 , 2016 , 37 ( 6 ): 1082 - 1088 . DOI: 10.3969/j.issn.1000-1093.2016.06.016 http://doi.org/10.3969/j.issn.1000-1093.2016.06.016 针对装备保障任务规划过程中保障资源占用冲突及保障任务逻辑关系约束的实际问题，构建基于任务优先排序的解空间模型，并进一步提出基于优先排序与改进粒子群优化的装备保障任务规划方法。该方法将分解后的保障任务按照其重要性和逻辑顺序进行优先排序，并根据排序结果对粒子群优化算法得到的任务与资源匹配解空间进行调整，以解决资源占用冲突和逻辑顺序问题。为保证每次迭代后的粒子为可行解，提出不可行粒子的多维异步处理机制，提高了粒子群优化算法的搜索效率。通过实例应用仿真分析，验证了该方法在装备保障任务规划中的有效性和优越性。

PENG P F , YU Q , LI Q Y . A planning method of equipment support task based on priority ordering and particle swarm optimization algorithm [J ] . Acta Armamentarii , 2016 , 37 ( 6 ): 1082 - 1088 . (in Chinese)

WU G H , PEDRYCZ W , LI H F , et al . Coordinated planning of heterogeneous earth observation resources [J ] . IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2015 , 46 ( 1 ): 109 - 125 . DOI: 10.1109/TSMC.2015.2431643 http://doi.org/10.1109/TSMC.2015.2431643 http://ieeexplore.ieee.org/document/7163602/ http://ieeexplore.ieee.org/document/7163602/

苏菲 , 陈岩 , 沈林成 . 基于蚁群算法的无人机协同多任务分配 [J ] . 航空学报 , 2008 , 29 ( 增刊1 ): 184 - 191 .

SU F , CHEN Y , SHEN L C . UAV Cooperative multi-task assignment based on ant colony algorithm [J ] . Acta Aeronautica et Astronautica Sinica , 2008 , 29 ( S1 ): 184 - 191 . (in Chinese)

范博洋 , 赵高鹏 , 薄煜明 , 等 . 多目标空地异构无人系统协同任务分配方法 [J ] . 兵工学报 , 2023 , 44 ( 6 ): 1564 - 1575 . DOI: 10.12382/bgxb.2022.0095 http://doi.org/10.12382/bgxb.2022.0095 针对由地面无人车与多无人机组成的空地异构无人系统面向大范围、多目标的协同任务分配问题,以无人系统完成任务时间为优化目标,同时考虑无人机收放、续航能力以及任务时序等约束条件,建立空地异构无人系统的任务分配模型,提出一种多目标空地异构无人系统任务分配方法。结合密度值最大聚类和混合粒子群优化算法,对空地异构无人系统的任务分配问题进行求解,从而得到满足约束条件的全局任务分配结果;通过仿真实验对所提方法进行验证。实验结果表明,该方法能够有效地求解在不同作战环境中的空地异构无人系统的任务分配问题。

FAN B Y , ZHAO G P , BO Y M , et al . Collaborative task allocation method for multi-target aerial-ground heterogeneous unmanned system [J ] . Acta Armamentarii , 2023 , 44 ( 6 ): 1564 - 1575 . (in Chinese)

KIM D Y , LEE J W . Joint mission assignment and topology management in the mission critical FANET [J ] . IEEE Internet of Things Journal , 2019 , 7 ( 3 ): 2368 - 2385 . DOI: 10.1109/JIoT.6488907 http://doi.org/10.1109/JIoT.6488907 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6488907 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6488907

张祥银 , 夏爽 , 张天 . 基于自适应遗传学习粒子群算法的多无人机协同任务分配 [J/OL ] . 控制与决策 , 2022 ( 2022-07-12 )[ 2022-10-21 ] . https://doi.org/ 10.13195/j.kzyjc.2022.0240 https://dx.doi.org/10.13195/j.kzyjc.2022.0240 .

ZHANG X Y , XIA S , ZHANG T . Adaptive genetic learning particle swarm optimization based cooperative task allocation for multi-UAV [J ] . Control and Decision , 2022 (2022-07-12)[2022-10-21 ] . https://doi.org/ 10.13195/j.kzyjc.2022.0240 https://dx.doi.org/10.13195/j.kzyjc.2022.0240 . (in Chinese)

彭雅兰 , 段海滨 , 张岱峰 , 等 . 仿灰狼合作捕食行为的无人机集群动态任务分配 [J ] . 控制理论与应用 , 2021 , 38 ( 11 ): 1855 - 1862 .

PENG Y L , DUAN H B , ZHANG D F , et al . Unmanned aerial vehicle swarm dynamic mission planning inspired by cooperative predation of wolf-pack [J ] . Control Theory & Applications , 2021 , 38 ( 11 ): 1855 - 1862 . (in Chinese)

龙涛 , 沈林成 , 朱华勇 , 等 . 面向协同任务的多UCAV分布式任务分配与协调技术 [J ] . 自动化学报 , 2007 , 33 ( 7 ): 731 - 737 .

LONG T , SHEN L C , ZHU H Y , et al. Distributed task allocation & coordination technique of multiple UCAVs for cooperative tasks [J ] . Acta Automatica Sinica , 2007 , 33 ( 7 ): 731 - 737 . (in Chinese)

张梦颖 , 王蒙一 , 王晓东 , 等 . 基于改进合同网的无人机群协同实时任务分配问题研究 [J ] . 航空兵器 , 2019 , 26 ( 4 ): 38 - 46 .

ZHANG M Y , WANG M Y , WANG X D , et al. Cooperative real-time task assignment of uAV group based on improved contract net [J ] . Aero Weaponry , 2019 , 26 ( 4 ): 38 - 46 . (in Chinese)

李鑫滨 , 郭力争 , 韩松 . 一种分布式异构多AUV任务分配鲁棒拍卖算法 [J ] . 北京航空航天大学学报 , 2022 , 48 ( 5 ): 736 - 746 .

LI X B , GUO L Z , HAN S . robust auction algorithm for distributed heterogeneous multi-AUV task assignment [J ] . Journal of Beijing University of Aeronautics and Astronautics , 2022 , 48 ( 5 ): 736 - 746 . (in Chinese)

DENG L , YU D . Deep learning: methods and applications [J ] . Foundations and Trends in Signal Processing , 2014 , 7 ( 3/4 ): 197 - 387 . DOI: 10.1561/2000000039 http://doi.org/10.1561/2000000039 http://nowpublishers.com/articles/foundations-and-trends-in-signal-processing/SIG-039 http://nowpublishers.com/articles/foundations-and-trends-in-signal-processing/SIG-039

竺殊荣 . 动态环境下多无人机协同任务规划方法 [D ] . 南京 : 南京邮电大学 , 2019 : 28 - 40 .

ZHU S R . Cooperative task planning method of multi-UAV in dynamic environment [D ] . Nanjing : Nanjing University of Posts and Telecommunications , 2019 : 28 - 40 . (in Chinese)

LIU J Y , WANG G , FU Q , et al . Task assignment in ground-to-air confrontation based on multiagent deep reinforcement learning [J ] . Defence Technology , 2023 , 19 ( 1 ): 210 - 219 . DOI: 10.1016/j.dt.2022.04.001 http://doi.org/10.1016/j.dt.2022.04.001 The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events. Aiming at the problems where existing task assignment methods are applied to ground-to-air confrontation, there is low efficiency in dealing with complex tasks, and there are interactive conflicts in multiagent systems. This study proposes a multiagent architecture based on a one-general agent with multiple narrow agents (OGMN) to reduce task assignment conflicts. Considering the slow speed of traditional dynamic task assignment algorithms, this paper proposes the proximal policy optimization for task assignment of general and narrow agents (PPO-TAGNA) algorithm. The algorithm based on the idea of the optimal assignment strategy algorithm and combined with the training framework of deep reinforcement learning (DRL) adds a multihead attention mechanism and a stage reward mechanism to the bilateral band clipping PPO algorithm to solve the problem of low training efficiency. Finally, simulation experiments are carried out in the digital battlefield. The multiagent architecture based on OGMN combined with the PPO-TAGNA algorithm can obtain higher rewards faster and has a higher win ratio. By analyzing agent behavior, the efficiency, superiority and rationality of resource utilization of this method are verified. © 2022 China Ordnance Society

李凯文 , 张涛 , 王锐 , 等 . 基于深度强化学习的组合优化研究进展 [J ] . 自动化学报 , 2021 , 47 ( 11 ): 2521 - 2537 .

LI K W , ZHANG T , WANG R , et al. Research reviews of combinatorial optimization methods based on deep reinforcement learning [J ] . Acta Automatica Sinica , 2021 , 47 ( 11 ): 2521 - 2537 . (in Chinese)

GOODFELLOW I J , POUGET-ABADIE J , MIRZA M , et al. Generative adversarial nets [C ] //Proceedings of International Conference on Neural Information Processing Systems. Cambridge, MA , US : MIT Press , 2014 : 2672 - 2680 .

ZHAO Y R , DENG B , HUANG J Q , et al. Stylized adversarial autoencoder for image generation [C ] // Proceedings of the 25th ACM international Conference on Multimedia. New York, NY , US : Association for Computing Machinery , 2017 : 244 - 251 .

FRID-ADAR M , DIAMANT I , KLANG E , et al . GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification [J ] . Neurocomputing , 2018 , 321 : 321 - 331 . DOI: 10.1016/j.neucom.2018.09.013 http://doi.org/10.1016/j.neucom.2018.09.013 https://linkinghub.elsevier.com/retrieve/pii/S0925231218310749 https://linkinghub.elsevier.com/retrieve/pii/S0925231218310749

JING Y C , YANG Y Z , FENG Z L , et al. Neural style transfer:a review [J ] . IEEE Transactions on Visualization and Computer Graphics , 2019 , 26 ( 11 ): 3365 - 3385 . DOI: 10.1109/TVCG.2945 http://doi.org/10.1109/TVCG.2945 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=2945 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=2945

林懿伦 , 戴星原 , 李力 , 等 . 人工智能研究的新前线:生成式对抗网络 [J ] . 自动化学报 , 2018 , 44 ( 5 ): 775 - 792 .

LIN Y L , DAI X Y , LI L , et al. The new frontier of AI research: generative adversarial networks [J ] . Acta Automatica Sinica , 2018 , 44 ( 5 ): 775 - 792 . (in Chinese)

MIRZA M , OSINDERO S . Conditional generative adversarial nets:arXiv:1411.1784 [R ] . Ithaca, NY , US : Cornell University , 2014 :1411.1784.

RADFORD A , METZ L , CHINTALA S . Unsupervised representation learning with deep convolutional generative adversarial networks:arXiv:1511.06434 [R ] . Ithaca, NY , US : Cornell University , 2015 :1511.06434.

ARJOVSKY M , BOTTOU L . Towards principled methods for training generative adversarial networks: arXiv:1701.04862 [R ] . Ithaca, NY , US : Cornell University , 2017 :1701.04862.

YU L T , ZHANG W N , WANG J , et al . SeqGAN: sequence generative adversarial nets with policy gradient: arXiv:1609.05473 [R ] //Ithaca, NY , US : Cornell University , 2016 :1609.05473.

孙秋野 , 胡旌伟 , 杨凌霄 , 等 . 基于GAN技术的自能源混合建模与参数辨识方法 [J ] . 自动化学报 , 2018 , 44 ( 5 ): 901 - 914 .

SUN Q Y , HU J W , YANG L X , et al . We-energy hybrid modeling and parameter identification with GAN technology [J ] . Acta Automatica Sinica , 2018 , 44 ( 5 ): 901 - 914 . (in Chinese)

薛灵芝 , 曾向阳 , 杨爽 . 基于生成对抗网络的水声目标识别算法 [J ] . 兵工学报 , 2021 , 42 ( 11 ): 2444 - 2452 . DOI: 10.3969/j.issn.1000-1093.2021.11.018 http://doi.org/10.3969/j.issn.1000-1093.2021.11.018 目标识别是水声探测领域的难题，也是研究热点。在水声目标识别实际应用中，标记样本数量不足是制约识别结果的主要因素之一。针对水声目标噪声数据具有的小样本特点，基于深度学习理论提出一种基于生成对抗网络的识别模型。该模型从生成模型与对抗模型的相互博弈中，学习更多有效的识别特征信息，并与深度自编码网络和深度置信网络模型进行对比。仿真实验结果表明：在样本数量有限的情况下，生成对抗网络模型的识别效果优于深度置信网络与深度自编码网络；3种深度学习模型的识别性能均优于先提取梅尔倒谱系数特征，再用Softmax分类的方法。为进一步测试所建模型的性能，研究了3种深度学习模型在不同信噪比下的鲁棒性，仿真实验结果表明：生成对抗网络模型对噪声具有更强的鲁棒性。

XUE L Z , ZENG X Y , YANG S . Underwater acoustic target recognition algorithm based on generative adversarial networks [J ] . Acta Armamentarii , 2021 , 42 ( 11 ): 2444 - 2452 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.11.018 http://doi.org/10.3969/j.issn.1000-1093.2021.11.018 In the practical application of underwater acoustic target recognition，one of the main factors restricting the recognition results is the insufficient quantity of labeled samples. For the small sample properties of underwater acoustic target noise，a generative adversarial networks(GAN)-based recognition algorithm is proposed based on deep learning theory. It can be used to learn more effective features with more discriminative information from the game between generated model and adversarial model，and it is compared with deep auto-encoder(DAE) network and deep belief network(DBN) models. The experimental results illustrate that the recognition performance of GAN network model is higher than those of DBN network and DAE network models when the number of samples is limited，and the recognition performances of the three deep learning models are better than the conventional approach of extracting Mel frequency cepstrum coefficient(MFCC) features and then classifying by Softmax. In addition，GAN network model is superior to DBN network and DAE network models in recognition rate when using training samples and test samples with different SNRs. The smulation experimental results indicate that the GAN network model is more robust to noise.

郑念祖 , 丁进良 . 基于Regression GAN的原油总氢物性预测方法 [J ] . 自动化学报 , 2018 , 44 ( 5 ): 915 - 921 .

ZHENG N Z , DING J L . Regression GAN based prediction for physical properties of total hydrogen in crude oil [J ] . Acta Automatica Sinica , 2018 , 44 ( 5 ): 915 - 921 . (in Chinese)

殷小静 , 胡晓峰 , 杨镜宇 . 面向作战体系的生成式对抗网络应用研究 [J ] . 火力与指挥控制 , 2019 , 44 ( 11 ): 11 - 15 .

YIN X J , HU X F , YANG J Y . Research of operation SoS oriented application of generative adversarial networks [J ] . Fire Control & Command Control , 2019 , 44 ( 11 ): 11 - 15 . (in Chinese)

GU J X , WANG Z H , KUEN J , et al . Recent advances in convolutional neural networks [J ] . Pattern Recognition , 2018 , 77 : 354 - 377 . DOI: 10.1016/j.patcog.2017.10.013 http://doi.org/10.1016/j.patcog.2017.10.013 https://linkinghub.elsevier.com/retrieve/pii/S0031320317304120 https://linkinghub.elsevier.com/retrieve/pii/S0031320317304120

CHUNG J Y , GUICEHRE C , CHO K H , et al . Empirical evaluation of gated recurrent neural networks on sequence modeling: arXiv:1412.3555 [R ] . Ithaca, NY , US : Cornell University , 2014 :1412.3555.

ZAREMBA W , SUTSKEVER I , VINYALS O . Recurrent neural network regularization: arXiv:1409.2329 [R ] . Ithaca, NY , US : Cornell University , 2014 :1409.2329.

MIKOLV T , CHEN K , CORRADO G , et al. Efficient estimation of word representations in vector space: arXiv:1301.3781 [R ] . Ithaca, NY , US : Cornell University , 2013 :1301.3781.

MA X B , DRIGGS-CAMPBELL K , ZHANG Z Z , et al . Monte-carlo tree search for policy optimization:arXiv . 1912 .10648[R ] . Ithaca, NY , US : Cornell University , 2019 : 1912.10648.

严飞 , 祝小平 , 周洲 , 等 . 考虑同时攻击约束的多异构无人机实时任务分配 [J ] . 中国科学:信息科学 , 2019 , 49 ( 5 ): 555 - 569 .

YAN F , ZHU X P , ZHOU Z , et al . Real-time task allocation for a heterogeneous multi-UAV simultaneous attack [J ] . Scientia Sinica(Informationis ), 2019 , 49 ( 5 ): 555 - 569 . (in Chinese)