分布式空间-时间轨迹优化用于无人机群
Distributed Spatial-Temporal Trajectory Optimization for Unmanned-Aerial-Vehicle Swarm
AI总结：
本文提出了一种分布式空间-时间轨迹优化框架，利用ADMM和DDP实现无人机群的多智能体共识与快速局部规划，通过参数化DDP和自适应调节准则提升大规模无人机群的轨迹优化效率。
摘要：
无人机群轨迹优化问题是一类已知的多智能体最优控制问题，具有很强的非线性。然而，需要事先设定最终时间的启发式性质以及大量迭代次数带来的时间消耗，限制了现有方法在大规模无人机群中的实际应用。本文提出了一种空间-时间轨迹优化框架，该框架基于交替方向乘子法（ADMM）实现多无人机共识，并利用微分动态规划（DDP）进行单个无人机的快速局部规划。所引入的框架是一种两级架构，每个无人机使用参数化DDP（PDDP）作为轨迹优化器，并利用ADMM满足局部约束并实现所有无人机之间的空间-时间参数共识。这导致了一个完全分布式算法，称为分布式参数化DDP（D-PDDP）。此外，还提出了一种基于谱梯度法的自适应调节准则，用于惩罚参数，以减少算法迭代次数。几个仿真示例被提出以验证所提出算法的有效性。
英文摘要：
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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