Communication-Aware Asynchronous Distributed Trajectory Optimization for UAV Swarm

UAV swarms have emerged as transformative systems for complex missions including wildfire surveillance ( Julian and Kochenderfer 2019), intelligence surveillance and reconnaissance ( Kolar 2020), situational awareness ( Scharre 2018), and cooperative interception ( Balhance et al. 2017). In these applications, trajectory optimization is the cornerstone for ensuring both mission success and operational s afety ( Sezer 2022; Qian et al. 2020; Sanchez-Lopez et al. 2020). Over the past decade, trajectory optimization techniques hav e evolved from sophisticated single-agent formulations to distributed multi-agent frameworks, driven by the increasing scale and complexity of swarm-based missions ( Saravanos et al. 2023). For individual UAV trajectory optimization, a variety of numerical m ethods have demonstrated strong performance. Pseudospectral methods achieve high-accuracy solution s by discretizing continuous-time problems ( Chai et al. 2017), while sequential quadratic programming (SQP) ( Hong et al. 2021) and sequential convex programming (SCP) ( Deligiannis et al. 2019) provide flexible tools for handling nonlinear dynamics and constraint s.