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BAE J , PARK M . A heuristic for efficient coordination of multiple heterogeneous mobile robots considering workload balance [J ] . IEEE Robotics and Automation Letters , 2021 , 6 ( 2 ): 4064 - 4070 . DOI: 10.1109/LRA.2021.3067286 http://doi.org/10.1109/LRA.2021.3067286 https://ieeexplore.ieee.org/document/9381577/ https://ieeexplore.ieee.org/document/9381577/

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张云飞 , 林德福 , 郑多 , 等 . 多目标时空同步协同攻击无人机任务分配与轨迹优化 [J ] . 兵工学报 , 2021 , 42 ( 7 ): 1482 - 1495 .

ZHANG Y F , LIN D F , ZHENG D , et al . Task allocation and trajectory optimization of UAV for multi-target time-space synchronization cooperative attack [J ] . Acta Armamentarii , 2021 , 42 ( 7 ): 1482 - 1495 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.07.016 http://doi.org/10.3969/j.issn.1000-1093.2021.07.016 A task allocation and trajectory optimization algorithm for multi-target attack is proposed for the coordinated attack task of distributed UAVs attacking multiple targets in a complex battlefield environment. The typical multi-target strike mission scenarios and UAV models are established. A search map with no-fly zones as nodes is constructed based on Delaunay triangle theory. A^* algorithm is used to achieve the least threatening single-aircraft path search. In terms of UAV dynamics constraints and minimum energy loss, the time adjustment factor is introduced and the distributed UAV time-space synchronization trajectory optimization method based on Bezier curve is adopted to obtain the optimized trajectory for simultaneous attack on multiple targets. A trajectory tracking controller was designed for simulation track of pre-planned trajectory. The simulated results show that the proposed multi-target attack task allocation and trajectory optimization method can achieve multi-angle, time-space synchronization, and distributed coordinated attack on multiple targets, and it has strong robustness against interference such as noise and wind gusts.

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CHEN L , LIU W L , ZHONG J . An efficient multi-objective ant colony optimization for task allocation of heterogeneous unmanned aerial vehicles [J ] . Journal of Computational Science , 2022 , 58 : 101545 . DOI: 10.1016/j.jocs.2021.101545 http://doi.org/10.1016/j.jocs.2021.101545 https://linkinghub.elsevier.com/retrieve/pii/S1877750321002015 https://linkinghub.elsevier.com/retrieve/pii/S1877750321002015

王晶 , 夏鲁宁 , 荆继武 . 一种基于密度最大值的聚类算法 [J ] . 中国科学院大学学报 , 2009 , 26 ( 4 ): 539 - 548 . DOI: 10.7523/j.issn.2095-6134.2009.4.016 http://doi.org/10.7523/j.issn.2095-6134.2009.4.016 提出了一种结合了基于密度聚类思想的划分聚类方法——"密度最大值聚类算法(MDCA)",以最大密度对象作为起始点,通过考察最大密度对象所处空间区域的密度分布情况来划分基本簇,并合并基本簇获得最终的簇划分.实验表明,MDCA能够自动确定簇数量,并有效发现任意形状的簇,对于未知数据集的处理能力和聚类准确度都优于传统的基于划分聚类算法.

WANG J , XIA L N , JING J W . Maximum density clustering algorithm [J ] . Journal of University of Chinese Academy of Sciences , 2009 , 26 ( 4 ): 539 - 548 . (in Chinese) DOI: 10.7523/j.issn.2095-6134.2009.4.016 http://doi.org/10.7523/j.issn.2095-6134.2009.4.016 This paper proposes a new clustering algorithm named maximum density clustering algorithm(MDCA). In MDCA the concept of density is introduced to identify the count of clusters automatically.By selecting the densest object as the threshold, densities of those objects around the densest object are reviewed to decide the partition of basic blocks. Then the basic blocks are merged to form clusters of arbitrary shape. Experiments show that the ability and validity of MDCA in processing unknown datasets are all better than traditional partition-based clustering algorithms.