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Robust Distributed Cooperative Path-Following and Local Replanning for Multi-UAVs Under Differentiated Low-Altitude Paths

Zimao Sheng, Zirui Yu, Hong'an Yang

Abstract

Multiple fixed-wing unmanned aerial vehicles (multi-UAVs) encounter significant challenges in cooperative path following over complex Digital Elevation Model (DEM) low-altitude airspace, including wind field disturbances, sudden obstacles, and requirements of distributed temporal synchronization during differentiated path tracking. Existing methods lack efficient distributed coordination mechanisms for time-consistent tracking of 3D differentiated paths, fail to quantify robustness against disturbances, and lack effective online obstacle avoidance replanning capabilities. To address these gaps, a cooperative control strategy is proposed: first, the distributed cooperative path-following problem is quantified via time indices, and consistency is ensured through a distributed communication protocol; second, a longitudinal-lateral look-ahead angle adjustment method coupled with a robust guidance law is developed to achieve finite-time stabilization of path following error to zero under wind disturbances; third, an efficient local path replanning method with minimal time cost is designed for real-time online obstacle avoidance.Experimental validations demonstrate the effectiveness and superiority of the $\ $proposed strategy.

Robust Distributed Cooperative Path-Following and Local Replanning for Multi-UAVs Under Differentiated Low-Altitude Paths

Abstract

Multiple fixed-wing unmanned aerial vehicles (multi-UAVs) encounter significant challenges in cooperative path following over complex Digital Elevation Model (DEM) low-altitude airspace, including wind field disturbances, sudden obstacles, and requirements of distributed temporal synchronization during differentiated path tracking. Existing methods lack efficient distributed coordination mechanisms for time-consistent tracking of 3D differentiated paths, fail to quantify robustness against disturbances, and lack effective online obstacle avoidance replanning capabilities. To address these gaps, a cooperative control strategy is proposed: first, the distributed cooperative path-following problem is quantified via time indices, and consistency is ensured through a distributed communication protocol; second, a longitudinal-lateral look-ahead angle adjustment method coupled with a robust guidance law is developed to achieve finite-time stabilization of path following error to zero under wind disturbances; third, an efficient local path replanning method with minimal time cost is designed for real-time online obstacle avoidance.Experimental validations demonstrate the effectiveness and superiority of the proposed strategy.
Paper Structure (16 sections, 1 theorem, 24 equations, 7 figures, 1 table, 2 algorithms)

This paper contains 16 sections, 1 theorem, 24 equations, 7 figures, 1 table, 2 algorithms.

Table of Contents

  1. INTRODUCTION
  2. Motivation
  3. Related Work
  4. Contributions
  5. PRELIMINARIES AND PROBLEM FORMULATION
  6. System Model
  7. Communication Topology
  8. Problem Formulation
  9. DISTRIBUTED COOPERATIVE ROBUST PATH-FOLLOWING AND LOCAL REPLANNING
  10. Robust Stabilization for Path-Following Error
  11. Efficient Local Path Replanning
  12. Distributed Temporal Coordination Protocol
  13. SIMULATION
  14. Validation of Effectiveness
  15. Validation of Scalability
  16. ...and 1 more sections

Key Result

THEOREM 1

If for any reference waypoint $y_{c,i}\in\mathcal{P}_i$ is constrained by $\sqrt{\dot{p}_{n,i,c}^{*2} + \dot{p}_{e,i,c}^{*2}+ \dot{h}_{i,c}^{*2} }\leq \mathcal{L}_c$, $\gamma_i\left( h_i - h_{i,c}^{*} \right)\leq 0$, and there exists $0\leq \delta^{lon},\delta^{lat} < \frac{\pi}{2}$, here $V_g\cos\d

Figures (7)

  • Figure 1: Communication topology between UAVs.
  • Figure 2: Schematic diagram of distributed cooperative path following for multi-UAVs system.
  • Figure 3: Schematic diagram of the $i$-th UAV sampling a new waypoint $\tilde{y}_{c,i}$ within the local region $\Gamma_i$
  • Figure 4: Starting coordinates, target coordinates $p_{target}$, and path $\mathcal{P}_i$ to be followed for each $\text{UAV}_i$.
  • Figure 5: Time-series curves of $\theta_i$ for each UAV $i$ during 0s - 260s.
  • ...and 2 more figures

Theorems & Definitions (1)

  • THEOREM 1