面向应急响应群智感知的异构群体协作任务分配方法

路文浩; 赵勇; 季雅泰; 张琪; 许凯; 朱正秋

doi:10.11959/j.issn.2096-3750.2025.00488

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面向应急响应群智感知的异构群体协作任务分配方法

理论与技术 | 更新时间：2026-01-04

- 面向应急响应群智感知的异构群体协作任务分配方法
- Task allocation method for collaborative crowdsensing of heterogeneous entities in emergency response
- 物联网学报 2025年9卷第4期页码：77-90
- 作者机构：
  
  国防科技大学系统工程学院，湖南长沙 410000
- 作者简介：
  
  [ "路文浩（2002‒ ），男，国防科技大学系统工程学院博士生，主要研究方向为移动群智感知、深度强化学习、具身智能。" ]
  [ "赵勇（1997‒ ），男，国防科技大学系统工程学院博士生，主要研究方向为群智感知、人机交互、具身智能。" ]
  [ "季雅泰（1998‒ ），男，国防科技大学系统工程学院博士生，主要研究方向为具身智能、气体源搜索、无人机目标搜索。" ]
  [ "张琪（1988‒ ），男，博士，国防科技大学系统工程学院助理研究员，主要研究方向为仿真智能兵力行为建模、行为树。" ]
  [ "许凯（1990‒ ），男，博士，国防科技大学系统工程学院讲师，主要研究方向为复杂系统建模与仿真、人工智能和认知建模。" ]
  [ "朱正秋（1994‒ ），男，博士，国防科技大学系统工程学院讲师，主要研究方向为群智计算、具身智能、大模型智能体。" ]
- 基金信息：
  
  国防科技大学青年自主创新基金资助项目(ZK-2023-21)
- DOI：10.11959/j.issn.2096-3750.2025.00488
  中图分类号：
- 收稿：2025-01-27，
  
  修回：2025-03-18，
  
  纸质出版：2025-12-10
- 稿件说明：
移动端阅览
路文浩,赵勇,季雅泰等.面向应急响应群智感知的异构群体协作任务分配方法[J].物联网学报,2025,09(04):77-90.

LU Wenhao,ZHAO Yong,JI Yatai,et al.Task allocation method for collaborative crowdsensing of heterogeneous entities in emergency response[J].Chinese Journal on Internet of Things,2025,09(04):77-90.
路文浩,赵勇,季雅泰等.面向应急响应群智感知的异构群体协作任务分配方法[J].物联网学报,2025,09(04):77-90. DOI： 10.11959/j.issn.2096-3750.2025.00488.

LU Wenhao,ZHAO Yong,JI Yatai,et al.Task allocation method for collaborative crowdsensing of heterogeneous entities in emergency response[J].Chinese Journal on Internet of Things,2025,09(04):77-90. DOI： 10.11959/j.issn.2096-3750.2025.00488.

摘要

近年来，异构群体协作感知成为群体智能领域的重要研究方向，主要探讨不同类型的智能体（如人类、无人机和无人车）如何协同工作以感知和理解环境，在应急救援等活动中被广泛应用。现有针对异构群体协作感知的任务分配算法大多难以平衡任务分配效果和求解效率，且未能实现异构群体的深度协作。针对应急救援活动中环境状态的部分可观条件，提出了一种“硬协作”异构群体协作模式，并建立了部分可观异构群体协作感知任务分配问题模型。为求解该问题，构建了多智能体协作框架，并在此基础上提出了环境状态部分可观条件下的异构群体协作感知任务分配算法。实验结果表明，相较于基线算法，所提方法在任务完成率上更具优势，4个场景中的平均任务完成率为84.40% ± 4.74%，远高于目前最优基线算法的65.98% ± 4.97%。此外，所提方法展现出良好的鲁棒性，即使在感知场景变化时仍能保持较高的任务完成率，显示出在动态环境中的应用潜力。

Abstract

Collaborative crowdsensing of heterogeneous entities has emerged as a pivotal research area in the field of collective intelligence in recent years

primarily focusing on how diverse agents

such as humans

drones

and unmanned vehicles

can collaboratively perceive and interpret their environments

which holds significant promise for applications in the emergency rescue and urban operations. However

existing task allocation algorithms for collaborative crowdsensing often struggle to balance allocation effectiveness with solution efficiency

and they frequently fail to facilitate deep collaboration among diverse entities. To address the partially observable conditions of environmental states during emergency rescue operations

a “hard collaboration” model for heterogeneous entities was introduced and a model for task allocation problem was formulated. To address this challenge

a multi-agent collaboration framework was developed and a task allocation algorithm for heterogeneous entities with partially observable environment states was proposed. Extensive experiments across four scenarios show that the proposed method outperforms the baseline algorithms in task completion rates

achieving an average of 84.40% ± 4.74% compared with the best baseline′s 65.98% ± 4.97%. Moreover

the proposed method exhibits strong robustness

maintaining commendable task completion rates even amid changes in perception scenarios and underscoring its potential for deployment in dynamic environments.

关键词

Keywords

references

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