用时间级联替代传统分层设计,提升四旋翼的实时规划与控制性能。
Temporal Cascading of Planning and Control for Quadrotor MPC
- 将规划作为多阶段MPC的后段时域,实现规划与控制一体化。
- 在相同计算资源下,闭环跟踪精度最高提升75%。
- 适合需要高精度实时飞行的无人机任务,如复杂避障与轨迹跟踪。
涉及四旋翼的许多空中任务要求兼具即时响应能力和长时程规划,以实现障碍物避让、能耗优化或轨迹追踪。高保真模型虽能精准控制,但难以用于长时程规划;低保真规划器虽可扩展,却无法直接控制飞行器,因而需采用级联架构。现有层级方法通常使用简化模型进行规划,并用高保真控制器进行跟踪,但这种分解本质上次优:控制器受限于粗糙的规划,而传统MPC方案则通过缩短时域来维持实时性。本文提出UNIQUE,一种将层级堆叠替换为时间级联的MPC架构。规划问题被建模为单一多阶段MPC的第二尾段,而非独立求解。我们对不同时域成本进行对齐,推导点质量规划模型的可行性约束,并引入状态转换约束,将高保真状态转化为有意义的低保真状态。并行的点质量与混合整数求解器处理非凸性,并在规划时域内逐步实现3D障碍物平滑。在仿真与真实飞行中,相同计算预算下,UNIQUE相比标准MPC和层级基线,闭环跟踪性能提升最高达75%。消融实验与帕累托分析验证了其在时域长度、约束近似和光滑调度变化下的持续优势。
原文摘要 · Abstract (English)
Many aerial tasks involving quadrotors demand both instant reactivity and long-horizon planning for obstacle avoidance, energy efficiency, or trajectory tracking. High-fidelity models enable accurate control but are too slow for long horizons. Low-fidelity planners scale but cannot directly control the system, necessitating cascaded architectures. Prevailing hierarchical approaches plan with a simplified model and use a high-fidelity controller for tracking, yet this decomposition is inherently suboptimal. The controller is limited by the coarse plan, and conventional MPC alternatives shorten the horizon to stay real-time feasible. We present UNIQUE, an MPC architecture that replaces this hierarchical stacking with temporal cascading. The planning problem is formulated as the second-tail horizon of a single multi-phase MPC, rather than being solved separately. We align costs across horizons, derive feasibility constraints for the point-mass planning model, and introduce transition constraints that convert high-fidelity states into meaningful low-fidelity states. Parallel point-mass and mixed-integer solvers address nonconvexities while incorporating progressive 3D obstacle smoothing over the planning horizon. In simulations and real flights, under equal computational budgets, UNIQUE improves closed-loop tracking by up to 75% compared with standard MPC and hierarchical baselines. Ablations and Pareto analyses confirm performance gains across variations in horizon, constraint approximations, and smoothing schedules.
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