Collaborative Sensing-oriented Formation Control Method for UAVs in Advanced Air Mobility

Xiong Kai; Yang Jinyue; Leng Supeng

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Collaborative Sensing-oriented Formation Control Method for UAVs in Advanced Air Mobility

更新时间：2026-04-10

- Collaborative Sensing-oriented Formation Control Method for UAVs in Advanced Air Mobility
- Chinese Journal on Internet of Things (2026)
- 作者机构：
  
  1.电子科技大学信息与通信工程学院，四川省成都市邮编61173
  2.中国航空工业集团公司成都飞机设计研究所
- 作者简介：
  
  spleng@uestc.edu.cn
- 基金信息：
- DOI：
  CLC：
- Received：13 January 2026，
  
  Revised：2026-04-08，
  
  Accepted：10 April 2026，
- 稿件说明：
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Xiong Kai, Yang Jinyue, Leng Supeng. Collaborative Sensing-oriented Formation Control Method for UAVs in Advanced Air Mobility[J/OL]. Chinese Journal on Internet of Things, 2026.
DOI：

Xiong Kai, Yang Jinyue, Leng Supeng. Collaborative Sensing-oriented Formation Control Method for UAVs in Advanced Air Mobility[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

低空经济作为国家战略性新兴产业，正加速推动先进空中交通系统（Advanced Air Mobility，AAM）从传统地面交通向全域化、高密度低空运行模式转型。然而，复杂城市场景中的通信干扰、感知精度不足、飞行安全隐患及能耗约束等因素，严重制约了AAM系统的实际应用效能。本文针对AAM场景下无人机编队协同感知与控制的关键挑战，提出一种融合感知优化与能耗控制的协同编队方法。首先，创新性地构建了以编队飞行能耗最小化为目标函数、以面向协同感知的克拉美罗下界（CRLB）和通信信噪比为约束条件的联合优化模型；基于Lyapunov稳定性理论设计梯度控制算法，求解满足感知精度要求的最优编队方位构型。其次，提出一种改进型势场控制方法，通过优化斥力生效范围、引入相对速度动态调节机制、设计补偿力突破局部极小值陷阱，并融合空气阻力物理模型，显著提升了编队在复杂环境中的避障能力与飞行稳定性。仿真实验表明，所提方法能在满足协同感知精度阈值的前提下，实现无人机编队能耗的显著优化，同时确保复杂干扰环境下的协同感知能力，保障安全飞行。

Abstract

The low-altitude economy

as a national strategic emerging industry

is accelerating the transformation of Advanced Air Mobility (AAM) systems from traditional ground-based transportation toward comprehensive

high-density low-altitude operational modes. However

factors such as communication interference in complex urban scenarios

insufficient perception accuracy

flight safety hazards

and energy consumption constraints severely limit the practical application effectiveness of AAM systems. This paper addresses the critical challenges of cooperative perception and control for unmanned aerial vehicle (UAV) formations in AAM scenarios by proposing an integrated cooperative formation method that combines perception optimization with energy consumption control. First

we innovatively construct a joint optimization model with the objective function of minimizing formation flight energy consumption

constrained by the Cramér-Rao Lower Bound (CRLB) for cooperative perception and communication signal-to-noise ratio requirements. Based on Lyapunov stability theory

we design a gradient control algorithm to solve for the optimal formation orientation configuration that satisfies perception accuracy requirements. Second

we propose an improved potential field control method that significantly enhances obstacle avoidance capability and flight stability in complex environments by optimizing the effective range of repulsive forces

introducing a relative velocity dynamic adjustment mechanism

designing compensatory forces to escape local minimum traps

and incorporating physical air resistance models. Simulation experiments demonstrate that the proposed method achieves significant optimization of UAV formation energy consumption while meeting collaborative perception accuracy thresholds

simultaneously ensuring cooperative perception capabilities and safe flight operations in complex interference environments.

关键词

Keywords

references

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