Distributed optimization offers a promising paradigm for trajectory planning in Unmanned Aerial Vehicle (UAV) swarms, yet its deployment in communication-constrained environments remains challenging due to unreliable links and limited data exchange. This paper addresses this issue via a two-tier architecture explicitly designed for operation under communication constraints. We develop a Communication-Aware Asynchronous Distributed Trajectory Optimization (CA-ADTO) framework that integrates Parameterized Differential Dynamic Programming (PDDP) for local trajectory optimization of individual UAVs with an asynchronous Alternating Direction Method of Multipliers (async-ADMM) for swarm-level coordination. The proposed architecture enables fully distributed optimization while substantially reducing communication overhead, making it suitable for real-world scenarios in which reliable connectivity cannot be guaranteed. The method is particularly effective in handling nonlinear dynamics and spatio-temporal coupling under communication constraints.

翻译：分布式优化为无人机集群的轨迹规划提供了一种前景广阔的范式，然而在通信受限环境中，由于链路不可靠和数据交换有限，其实际部署仍面临挑战。本文通过一种专为通信约束下运行而设计的两层架构来解决这一问题。我们开发了一种通信感知异步分布式轨迹优化框架，该框架将参数化微分动态规划用于单个无人机的局部轨迹优化，并结合异步交替方向乘子法实现集群层面的协同。所提出的架构支持完全分布式优化，同时显著降低了通信开销，使其适用于无法保证可靠连接的实际场景。该方法在通信约束下处理非线性动力学和时空耦合方面尤为有效。