Unifying Quadrotor Motion Planning and Control by Chaining Different Fidelity Models

TL;DR

This paper introduces Unique, a unified MPC framework that combines different fidelity models for quadrotor motion planning and control, achieving significant improvements in tracking performance in simulation and real flights.

Contribution

The paper presents a novel unified MPC approach that cascades models of different fidelities within a single optimization, with new constraints, smoothing schedules, and parallel solvers to enhance quadrotor control and planning.

Findings

Up to 75% improvement in tracking accuracy over baselines.

Effective model cascading and smoothing strategies enhance robustness.

Validated in both simulation and real-world flight experiments.

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.