Abstract
Many aerial tasks involving quadrotors demand both instant reactivity and long-horizon planning. High-fidelity models enable accurate control but are too slow for long horizons; low-fidelity planners scale but degrade closed-loop performance. We present Unique, a unified MPC that cascades models of different fidelity within a single optimization: a short-horizon, high-fidelity model for accurate control, and a long-horizon, low-fidelity model for planning. We align costs across horizons, derive feasibility-preserving thrust and body-rate constraints for the point-mass model, and introduce transition constraints that match the different states, thrust-induced acceleration, and jerk-body-rate relations. To prevent local minima emerging from nonsmooth clutter, we propose a 3D progressive smoothing schedule that morphs norm-based obstacles along the horizon. In addition, we deploy parallel randomly initialized MPC solvers to discover lower-cost local minima on the long, low-fidelity horizon. In simulation and real flights, under equal computational budgets, Unique improves closed-loop position or velocity tracking by up to 75% compared with standard MPC and hierarchical planner-tracker baselines. Ablations and Pareto analyses confirm robust gains across horizon variations, constraint approximations, and smoothing schedules.
Abstract (translated)
许多涉及四旋翼飞行器的空中任务都需要即时反应和长期规划的能力。高保真模型能够实现精确控制，但计算速度较慢，不适于长时间范围内的操作；而低保真度计划者虽然可以扩展使用，但在闭环性能方面却有所下降。我们提出了一种名为Unique的方法，这是一种统一的MPC（模型预测控制），它在一个单一优化过程中串联不同精度级别的模型：短时间范围内采用高保真模型以实现精确控制，在长时间范围内则采用低保真模型进行规划。 为了使不同的时间范围内的成本一致，我们制定了保持可行性的推力和体速率约束，并引入了匹配不同状态、由推力引起的加速度以及急动-角速度关系的转换约束。为防止从非光滑障碍物中产生的局部极小值问题，我们提出了一种三维渐进平滑时间表，该时间表将范数基障碍物沿着时域进行变形处理。此外，我们在长且低保真的时间内部署了并行随机初始化的MPC求解器以发现更低成本的局部极小值。 在仿真和真实飞行测试中，在同等计算预算下，与标准MPC和分层规划-跟踪基准相比，Unique可以使闭环位置或速度追踪性能提高多达75%。拆解分析和帕累托分析进一步证实了其在时间范围变化、约束近似以及平滑时间表方面的一致性和稳定性。
URL
https://arxiv.org/abs/2512.12427
