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
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 y~c,i within the local region Γi
Figure 4: Starting coordinates, target coordinates ptarget , and path Pi to be followed for each UAVi .
Figure 5: Time-series curves of θi for each UAV i during 0s - 260s.
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 ϕc,i for each UAV i ; (b): Signal tracking curves of γc,i for each UAV i ; (c): Signal tracking curves of Vg,c,i for each UAV i .
Number of UAVs
AEˉ(m)
RMSEˉ(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 AEˉ , RMSEˉ , MD and replanning time for different number of UAVs from 0s to 100s.
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.
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
3D reconstruction of unknown environments is a key application in robotics but is severely limited by the computational and energy capabilities of current aerial platforms. Deploying multiple UAVs and providing efficient and scalable path planning strategies are common approaches, but effective online coordination among UAVs remains a significant challenge. To address this problem, we propose a quality-adaptive decentralized decision-making strategy to build a 3D map with user-defined degrees of fidelity. The approach integrates a quality-oriented criterion based on TSDF confidence into view generation and information gain estimation to produce viewpoints consistent with the desired fidelity target. Additionally, we employ two levels of coordination: a penalty factor in the viewpoint evaluation to encourage local dispersion among the UAVs and a global imbalance correction mechanism. The latter, based on regularized clustering and optimal task assignment, is only triggered when an unbalanced configuration relative to high-information regions is detected. Simulation results demonstrate that the proposed method improves path efficiency compared to state-of-the-art multi-UAV exploration approaches, while also achieving higher-fidelity reconstructions in terms of coverage and accuracy. We make our code publicly available to the community.
Benjamin Sportich, Kenza Boubakri, Olivier Simonin +1
Inria, INSA Lyon, CITI, 69621 Villeurbanne, France.
Aiming at the problem that obstacle avoidance flexibility and formation integrity are difficult to coexist in multi-UAV formation motion in complex obstacle environments , and that the traditional artificial potential field (APF) method easily falls into local optima, a cooperative obstacle avoidance algorithm for multi-UAV formations integrating BI-APF-RRT and affine transformation is proposed. First, abandoning the traditional APF centroid path planning method , a goal-biased Bidirectional Artificial Potential Field method RRT (BI-APF-RRT) algorithm is adopted to conduct global collision-free path planning for the centroid of the leader formation. By introducing an improved artificial potential field and cubic B-spline interpolation, the smoothness and rapid convergence of the global path are ensured. Secondly, using the generated global path as the guiding trajectory for the formation's centroid , combined with an affine transformation matrix (including non-uniform scaling and rotation) , the formation can adaptively deform based on the distance to obstacles while moving along the optimal path. Finally, the followers track the leaders through a distributed control law , enabling the entire formation to safely cross complex obstacle areas without disassembling.
Yiliang Wu, Weican Chen, Yendo Hu
College of Marine Information Engineering, Jimei University, Xiamen, China