FF-MPCC：基于模型预测轮廓控制的高速敏捷编队飞行

中文摘要 AI 辅助

在无人机领域，以敏捷方式按照规定编队飞行仍是一个具有挑战性的问题，尤其是在跟随需要达到平台极限飞行的高要求轨迹时。针对该问题，本文提出一种新颖的编队飞行分散式方法，该方法将编队维护整合到MPCC（模型预测轮廓控制）框架中，使无人机能够在遵守各自动态约束、保持期望编队的同时，调整自身沿复杂路径的推进。为此，本文引入一种针对动态编队几何结构的新型重参数化与同步方法，以及一种用于确定各无人机期望位置的分散式方法。所提方法可使编队协调高速路径跟随，且不会损害编队完整性。本文通过大量仿真与真实实验对所提方法进行验证，实验涵盖路径复杂度各异、编队形状实时变化的场景。与时间参数化轨迹跟踪相比，本文证明所提方法在最高速度达21 m/s的高速飞行中，编队维护性能提升65%，同时达到目标所需时间相当。

英文摘要

Flying in a prescribed formation in an agile manner remains a challenging problem in the field of UAVs, particularly when following highly-demanding trajectories that require flight at platform limits. We address this problem by proposing a novel decentralized approach to formation flight along a given path that integrates formation maintenance into the MPCC framework, allowing UAVs to adapt their progression along complex paths while respecting individual dynamic constraints and maintaining the desired formation. To this end, we introduce a novel reparametrization and synchronization method for dynamic formation geometries together with a decentralized approach to determine the desired positions for the individual UAVs. The proposed approach allows the formation to coordinate high-speed path following without compromising formation integrity. The proposed approach is validated through extensive simulation and real-world experiments involving scenarios with varying complexity of paths and changes of required formation shape on the fly. In comparison to time-parameterized trajectory tracking, we demonstrate improved formation maintenance by 65% in high-speed flight with velocities up to 21 m/s, while achieving comparable times required to reach the goal.

发表机构

• Czech Technical University in Prague（布拉格捷克技术大学）
• Faculty of Electrical Engineering（电气工程学院）

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