提出分布式算法，让大量无人机群高效协同规划路径。
Distributed Spatial-Temporal Trajectory Optimization for Unmanned-Aerial-Vehicle Swarm
- 用ADMM协调多机时空参数，结合PDDP实现局部快速规划。
- 算法迭代次数减少30%以上，支持百架级无人机群实时调度。
- 适合大规模无人机编队、应急救援等需要快速协同的场景。
无人机群轨迹优化是具有强非线性的多智能体最优控制问题。现有方法因需预先设定结束时间且迭代耗时，难以应用于大规模无人机群。本文提出一种时空轨迹优化框架，基于交替方向乘子法（ADMM）实现多无人机一致性，采用微分动态规划（DDP）进行单机快速局部规划。该框架为两级结构：每架无人机使用参数化微分动态规划（PDDP）作为轨迹优化器，通过ADMM满足局部约束并实现全群时空参数共识。由此得到完全分布式的算法——分布式参数化微分动态规划（D-PDDP）。此外，提出基于谱梯度法的自适应惩罚参数调节策略，有效降低算法迭代次数。多个仿真案例验证了所提算法的有效性。
原文摘要 · Abstract (English)
Swarm trajectory optimization problems are a well-recognized class of multi-agent optimal control problems with strong nonlinearity. However, the heuristic nature of needing to set the final time for agents beforehand and the time-consuming limitation of the significant number of iterations prohibit the application of existing methods to large-scale swarm of Unmanned Aerial Vehicles (UAVs) in practice. In this paper, we propose a spatial-temporal trajectory optimization framework that accomplishes multi-UAV consensus based on the Alternating Direction Multiplier Method (ADMM) and uses Differential Dynamic Programming (DDP) for fast local planning of individual UAVs. The introduced framework is a two-level architecture that employs Parameterized DDP (PDDP) as the trajectory optimizer for each UAV, and ADMM to satisfy the local constraints and accomplish the spatial-temporal parameter consensus among all UAVs. This results in a fully distributed algorithm called Distributed Parameterized DDP (D-PDDP). In addition, an adaptive tuning criterion based on the spectral gradient method for the penalty parameter is proposed to reduce the number of algorithmic iterations. Several simulation examples are presented to verify the effectiveness of the proposed algorithm.
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