针对无人机群通信受限问题，提出低通信开销的异步协同轨迹优化框架。
Communication-Aware Asynchronous Distributed Trajectory Optimization for UAV Swarm
- 分层架构结合局部PDDP与异步ADMM实现分布式优化
- 通信中断下仍能保持轨迹规划性能，降低90%以上通信量
- 适合真实复杂环境中的无人机群协同任务
分布式优化为无人机群轨迹规划提供了有前景的范式，但在通信受限环境下部署仍具挑战性，主要源于链路不可靠和数据交换受限。本文提出一种两层架构，专为通信受限场景设计。开发了通信感知的异步分布式轨迹优化（CA-ADTO）框架，将参数化微分动态规划（PDDP）用于单机局部轨迹优化，结合异步交替方向乘子法（async-ADMM）实现群体层面协调。该架构支持完全分布式优化，显著降低通信开销，适用于难以保证可靠连接的实际场景。方法在处理非线性动力学及时空耦合关系方面表现优异。
原文摘要 · Abstract (English)
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.
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