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

Zimao Sheng; Zirui Yu; Hong'an Yang

cs.ROMar 25, 2026

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

Authors: Zimao Sheng, Zirui Yu, Hong'an Yang

Organizations: School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi, China · Tianjin University of Science and Technology, Tianjin, China

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.

Figures & tables

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.

Panel of Figure 6 — Figure 6: Robust cooperative path-following and local path replanning process of the multi-UAV system. (a): Simulation of the cooperative path-following process from 0s to 260s; (b): Simulation of the local path replanning at 75s.

Figure 7: Tracking performance of the autopilot for the reference commands generated by the guidance law during 75s – 100s. (a): Signal tracking curves of

\phi_{c,i}

for each UAV

i

; (b): Signal tracking curves of

\gamma_{c,i}

for each UAV

i

; (c): Signal tracking curves of

V_{g,c,i}

for each UAV

i

Number of UAVs	$\bar{\text{AE}}(\text{m})$	$\bar{\text{RMSE}}(\text{m})$	MD(s)	RT(s)
4	9.7817	26.7499	13.9428	3.7138
7	4.3812	11.4767	25.5534	3.653
10	3.1742	9.1567	9.8762	2.9872
13	4.5678	12.3907	15.5623	2.5940

Table 1: The comparison of

\bar{\text{AE}}

\bar{\text{RMSE}}

, MD and replanning time for different number of UAVs from 0s to 100s.

Distributed Control

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Multi-UAV Path Following using Vector-Field Guidance

Gautam Kumar, Amit Shivam, Ashwini Ratnoo

Department of Aerospace Engineering, Indian Institute of Science Bangalore, India 560012 · Department of Electrical and Computer Engineering, University of Porto, Porto, Portugal

Multi-Robot Formation Control Collision Avoidance Multi-Robot Coordination

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This paper presents a decentralized, collision-free framework for path following guidance of multiple uncrewed aerial vehicles (UAVs), while maintaining uniform spacing along a reference path. A vector field-based guidance law is employed to drive each UAV toward the reference path. A rotational repulsion mechanism, utilizing relative distance and bearing between UAVs, is proposed to avoid collisions during convergence to the path, and an inter-UAV spacing error-based velocity control law is presented to achieve uniform separation along the path. Analytical guarantees are established for collision avoidance and convergence of the inter-UAV spacing errors to zero, ensuring uniform separation along the path. Numerical simulations demonstrate the efficacy of the proposed method.

Abstract

Distributed Control

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