Unifying Quadrotor Motion Planning and Control by Chaining Different Fidelity Models

2025-12-13

AI Summary

To address the challenge of simultaneously achieving real-time responsiveness and long-horizon global optimization in quadrotor motion planning and control, this paper proposes a unified multi-fidelity Model Predictive Control (MPC) framework. The framework integrates, within a single nonlinear optimization, a high-fidelity dynamical model (short horizon) and a low-dimensional point-mass model (long horizon). It introduces three novel components: (i) transition constraints enforcing state, acceleration, and angular acceleration consistency; (ii) a 3D progressive obstacle smoothing strategy; and (iii) a parallel stochastic initialization solver. A theoretically grounded, feasibility-preserving mapping from point-mass constraints to full-state constraints is derived. Extensive simulation and real-world flight experiments demonstrate that, under identical computational budgets, the proposed method reduces position and velocity tracking errors by up to 75%. Ablation studies and Pareto analysis further validate its robust advantages in horizon length flexibility, constraint approximation fidelity, and obstacle handling capability.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Unifying high-fidelity control and low-fidelity planning for quadrotor motion
• Aligning costs and constraints across different model horizons
• Preventing local minima in cluttered environments with smoothing techniques

Innovation

• Cascaded MPC with high and low fidelity models
• Progressive 3D smoothing for obstacle avoidance
• Parallel solvers for lower-cost local minima