Path planning and obstacle avoidance with ISS framework for a UAV swarm under unified wind, sensor noise and delay disturbances.

Abstract

Multi-UAV swarm systems have attracted significant attention in recent years due to their wide range of applications, including surveillance, disaster management, search and rescue, agriculture, infrastructure inspection, and autonomous transportation. In such systems, maintaining formation integrity, achieving accurate trajectory tracking, and ensuring safe obstacle avoidance under environmental and communication disturbances remain challenging research problems. Existing studies generally investigate wind effects, sensor noise, or communication delays separately and often lack a unified stability framework capable of characterizing their combined influence on formation performance. This study aims to develop a unified formation control and obstacle avoidance framework for multi-UAV systems operating under simultaneous wind disturbances, sensor noise, and communication delays. The proposed framework seeks to ensure stable formation regulation, centroid trajectory tracking, inter-agent collision prevention, and obstacle avoidance while providing formal robustness guarantees through an input-to-state stability (ISS) analysis. A consensus-based Laplacian formation controller was integrated with centroid tracking and artificial-potential-field-based obstacle and collision avoidance mechanisms. The Crazyflie 2.0 Nano-Quadrotor model was employed, and the six-degree-of-freedom dynamics were simplified into planar motion for swarm-level analysis. Stability of the nominal system was investigated using Lyapunov theory, while the disturbed system was analyzed within an ISS framework to derive explicit steady-state tracking error bounds under bounded disturbances. Numerical simulations were conducted in MATLAB for different swarm formations and disturbance scenarios. Simulation results demonstrated successful formation acquisition, centroid tracking, obstacle avoidance, and collision-free navigation under unified wind, sensor noise, and delay disturbances. Both hexagonal and line formations maintained stability while navigating toward desired target positions in the presence of static obstacles. Temporary formation deformations caused by avoidance maneuvers were effectively corrected, and the swarm recovered the desired geometry after disturbance effects diminished. Furthermore, the steady-state tracking errors remained within the theoretical ISS bounds, confirming consistency between the analytical results and simulation outcomes. The proposed framework provides a robust and unified solution for formation control and obstacle avoidance in disturbed multi-UAV systems. The integration of consensus-based control, artificial potential fields, and ISS-based robustness analysis enables reliable trajectory tracking and safe swarm coordination under realistic operating conditions. The obtained results indicate that the framework can serve as an effective foundation for future studies involving dynamic obstacles, three-dimensional swarm coordination, and real-world experimental validations.