面向无人机集群的感知与控制闭环调度策略

平雨奇; 丁华昊; 梁天豪; 梁清泉; 王祎珍; 张霆廷

doi:10.11959/j.issn.2096-3750.2024.00417

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面向无人机集群的感知与控制闭环调度策略

理论与技术 | 更新时间：2025-03-13

- 面向无人机集群的感知与控制闭环调度策略
- A sensing and control closed-loop scheduling strategy for UAV swarms
- 物联网学报 2024年8卷第3期页码：46-54
- 作者机构：
  
  1.哈尔滨工业大学（深圳）广东省空天网络与智能感知重点实验室，广东深圳 518000
  2.哈尔滨工业大学（深圳）信息科学与技术学院，广东深圳 518000
- 作者简介：
  
  [ "平雨奇（2001‒ ），男，哈尔滨工业大学（深圳）博士生，主要研究方向为无人机集群控制、通信控制协同设计等。" ]
  [ "丁华昊（2000‒ ），男，哈尔滨工业大学（深圳）硕士生，主要研究方向为无人机编队控制、多智能体协同控制、网络控制系统等。" ]
  [ "梁天豪（1996‒ ），男，哈尔滨工业大学（深圳）博士生，主要研究方向为通信定位一体化、无人机通信、贝叶斯滤波、资源优化和信息时效性等。" ]
  [ "梁清泉（2002‒ ），男，哈尔滨工业大学（深圳）硕士生，主要研究方向为无人机控制、通信控制协同设计等。" ]
  [ "王祎珍（1993‒ ），女，哈尔滨工业大学（深圳）博士生，主要研究方向为通信控制一体化、自动驾驶汽车通信、信息时效性等。" ]
  [ "张霆廷（1980‒ ），男，哈尔滨工业大学（深圳）教授、博士生导师、信息科学与技术学院院长，主要研究方向为多智能体协同理论与实现、无线定位理论和脉冲超宽带技术等。" ]
- 基金信息：
  
  国家自然科学基金项目(62171160);哈尔滨工业大学原创前沿探索基金项目(HIT.OCEF.2022055);深圳市重大科技专项(KJZD20231023093055002;KJZD20230923114804009);空天网络与智能感知重点实验室(2024KSYS023);深圳市重点实验室项目(ZDSYS2021062309180825)
- DOI：10.11959/j.issn.2096-3750.2024.00417
  中图分类号：
- 收稿日期：2024-09-08，
  
  修回日期：2024-09-20，
  
  纸质出版日期：2024-09-10
- 稿件说明：
移动端阅览
平雨奇,丁华昊,梁天豪等.面向无人机集群的感知与控制闭环调度策略[J].物联网学报,2024,08(03):46-54.

PING Yuqi,DING Huahao,LIANG Tianhao,et al.A sensing and control closed-loop scheduling strategy for UAV swarms[J].Chinese Journal on Internet of Things,2024,08(03):46-54.
平雨奇,丁华昊,梁天豪等.面向无人机集群的感知与控制闭环调度策略[J].物联网学报,2024,08(03):46-54. DOI： 10.11959/j.issn.2096-3750.2024.00417.

PING Yuqi,DING Huahao,LIANG Tianhao,et al.A sensing and control closed-loop scheduling strategy for UAV swarms[J].Chinese Journal on Internet of Things,2024,08(03):46-54. DOI： 10.11959/j.issn.2096-3750.2024.00417.

摘要

在物联网、边缘计算、自主定位导航等技术的推动下，无人机集群被广泛应用于应急通信、数据收集、环境测绘、智能物流等场景。为了在有限通信资源下实现最优的系统性能，设计了一种面向无人机集群的远程控制框架。该框架考虑了无人机所处外部环境的差异，结合状态估计误差和控制误差构建了一个控制指令与调度决策联合优化问题。该问题可以分解为鲁棒控制器设计和更新决策调度两个子问题，分别通过二次规划与李雅普诺夫优化方法求解。仿真结果验证了先验情境信息、控制误差等因素在无人机集群调度中的重要性。与只考虑状态估计误差的传统策略相比，新方法可以使跟踪误差最多减小13.74%，同时在不同的状态转移噪声时均能展现出更好的性能。

Abstract

Driven by technologies such as the Internet of things (IoT)

edge computing

and autonomous positioning and navigation

UAV swarms have been widely applied in scenarios such as emergency communication

data collection

environmental mapping

and intelligent logistics. To achieve optimal system performance under limited communication resources

a remote control framework for UAV swarms was designed. The framework considered the differences in external environments where UAV operate and integrates state estimation errors and control errors to formulate a joint optimization problem for control commands and scheduling decisions. This problem was decomposed into two subproblems: robust controller design and update scheduling

which were solved using quadratic programming and Lyapunov optimization methods

respectively. Simulation results verify the importance of prior contextual information and control errors in UAV swarm scheduling. Compared with traditional strategies that only consider state estimation errors

the new method reduces tracking errors by up to 13.74% and demonstrates better performance under various state transition noise conditions.

关键词

Keywords

references

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