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.

翻译：许多涉及四旋翼飞行器的空中任务既需要瞬时反应能力，又需进行长时域规划。高保真度模型能够实现精确控制，但计算速度过慢，难以应用于长时域；低保真度规划器虽可扩展至长时域，但会降低闭环性能。本文提出Unique——一种在单一优化框架内级联不同保真度模型的统一模型预测控制方法：采用短时域高保真度模型实现精确控制，同时结合长时域低保真度模型进行规划。我们实现了跨时域代价函数对齐，推导了质点模型下保持可行性的推力与体轴角速率约束，并引入匹配不同状态、推力诱导加速度以及加加速度-体轴角速率关系的过渡约束。为避免非光滑障碍物环境导致的局部极小值问题，提出一种沿时域维度渐进平滑的三维障碍物形态变换策略，基于范数构建可变形障碍物模型。此外，部署并行随机初始化的模型预测控制求解器，以在长时域低保真度规划层中发现更低代价的局部极小值。仿真与真实飞行实验表明，在同等计算资源下，相较于标准模型预测控制及分层规划-跟踪基线方法，Unique将闭环位置或速度跟踪性能提升最高达75%。消融实验与帕累托分析证实，该方法在时域变化、约束近似及平滑策略等多种条件下均能保持稳健的性能提升。