面向无人机集群的通信感知异步分布式轨迹优化
Communication-Aware Asynchronous Distributed Trajectory Optimization for UAV Swarm
AI总结：
针对通信受限环境下无人机集群分布式轨迹规划部署难的问题，提出融合PDDP与async-ADMM的CA-ADTO双层框架，可降低通信开销，有效处理非线性动力学与时空耦合问题。
摘要：
分布式优化为无人机（UAV）集群的轨迹规划提供了极具前景的范式，但由于链路不可靠、数据交换受限，其在通信受限环境中的部署仍面临挑战。本文通过专为通信约束场景下运行设计的双层架构解决该问题。我们提出了通信感知异步分布式轨迹优化（CA-ADTO）框架，该框架将用于单架无人机本地轨迹优化的参数化微分动态规划（PDDP）与用于集群级协同的异步交替方向乘子法（async-ADMM）相结合。所提架构可实现完全分布式优化，同时大幅降低通信开销，适用于无法保障可靠连接的真实场景，在处理通信约束下的非线性动力学与时空耦合问题时效果尤为突出。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
