探索与打击任务中异构智能体动态分组策略

陈晨; 吴啸尘; 陈杰; Panos M. PARDALOS; 丁舒忻

doi:10.1631/FITEE.2000352

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探索与打击任务中异构智能体动态分组策略

常规文章 | Updated：2022-04-19

- 探索与打击任务中异构智能体动态分组策略
  Enhanced Publication
- Dynamic grouping of heterogeneous agents for exploration and strike missions
- 信息与电子工程前沿（英文） 2022年23卷第1期页码：86-100
- Affiliations：
  
  1.School of Automation, Beijing Institute of Technology, Beijing100081, China
  2.Department of Control Science and Engineering, Tongji University, Shanghai200092, China
  3.Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL32611, USA
  4.Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing100081, China
- Author bio：
  
  E-mail: xiaofan@bit.edu.cn;
  ‡Corresponding authors
- Funds：
  
  National Natural Science Foundation of China(61773066);Foundation of China Academy of Railway Sciences Corporation Limited(2019YJ071)
- DOI：10.1631/FITEE.2000352
  中图分类号： TP391.9
- 收稿：2020-07-17，
  
  录用：2020-11-16，
  
  纸质出版：2022-01-0
- Accepted：
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陈晨, 吴啸尘, 陈杰, 等. 探索与打击任务中异构智能体动态分组策略[J]. 信息与电子工程前沿（英文）, 2022,23(1):86-100.

Chen CHEN, Xiaochen WU, Jie CHEN, et al. Dynamic grouping of heterogeneous agents for exploration and strike missions[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(1): 86-100.
陈晨, 吴啸尘, 陈杰, 等. 探索与打击任务中异构智能体动态分组策略[J]. 信息与电子工程前沿（英文）, 2022,23(1):86-100. DOI： 10.1631/FITEE.2000352.

Chen CHEN, Xiaochen WU, Jie CHEN, et al. Dynamic grouping of heterogeneous agents for exploration and strike missions[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(1): 86-100. DOI： 10.1631/FITEE.2000352.

摘要

多变的环境和复杂的作战任务对无人作战智能体任务群组的构建提出了新的要求。本文旨在解决新需求下的任务群组动态构建问题。针对智能体的异构性，在不断探索任务的情况下群体形成方法需满足能动态形成新的群组。提出一种融合了启发式规则和响应阈值模型的群组形成策略，用于动态调整任务群组的成员以适应新的任务需求。将任务需求与群组能力的匹配程度以及群组的组网开销作为评价团队素质的指标。选取响应阈值法和蚁群算法作为仿真实验中的对比算法。结果表明所提方法在解决动态任务组形成问题时具备一定优势。

Abstract

The ever-changing environment and complex combat missions create new demands for the formation of mission groups of unmanned combat agents. This study aims to address the problem of dynamic construction of mission groups under new requirements. Agents are heterogeneous

and a group formation method must dynamically form new groups in circumstances where missions are constantly being explored. In our method

a group formation strategy that combines heuristic rules and response threshold models is proposed to dynamically adjust the members of the mission group and adapt to the needs of new missions. The degree of matching between the mission requirements and the group's capabilities

and the communication cost of group formation are used as indicators to evaluate the quality of the group. The response threshold method and the ant colony algorithm are selected as the comparison algorithms in the simulations. The results show that the grouping scheme obtained by the proposed method is superior to those of the comparison methods.

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