NMPP: Nonlinear Model Predictive Planning for Agile UAV Flight in Cluttered Environments

2026-10-06

AI Summary

This study addresses the challenge of generating reference trajectories that simultaneously satisfy collision avoidance, dynamic feasibility, and actuator constraints for agile UAV flight in cluttered environments. To overcome the limitations of conventional soft-cost formulations and convex corridors, this work proposes a nonlinear model predictive planning framework that models obstacles as geometric hard constraints, yielding full-state dynamically feasible trajectories tracked via an SE(3) controller. Compared with baseline methods, the proposed approach reduces position tracking errors by 58%–70%. In simulation, it achieves collision-free forest traversal at 9.5 m/s with an 86% success rate in complex scenarios, while real-world experiments demonstrate stable operation at 5.5 m/s.

Abstract

Flying a quadrotor through a cluttered environment requires not only planning a collision-free reference trajectory based on perceived obstacles, but the reference also needs to be dynamically feasible and within the actuation limits of the vehicle, so that the controller can track it precisely. Existing methods either optimize a smooth polynomial inside a convex corridor, which limits agility, or treat obstacles as soft costs traded against tracking performance. We propose a Nonlinear Model Predictive Planning (NMPP) that imposes perceived obstacles as hard geometric constraints and hands a full-state reference to an obstacle-blind SE(3) controller. Our planner achieves a 58-67 % lower position RMSE than a linear Model Predictive Control trajectory planner and a 41-70 % lower RMSE than a polynomial trajectory planner. It also completes all forest flights with up to 9.5 m/s speed without collisions, and achieves 86 % flight success rate under a more aggressive speed profile where a state-of-the-art planner has only 26 % success rate. The real-world deployment showed reliable execution flying up to 5.5 m/s in an unknown cluttered environment.

Problem

• UAV flight planning
• cluttered environments
• collision avoidance
• dynamic feasibility
• agile flight

Innovation

• Nonlinear Model Predictive Planning
• Hard Geometric Constraints
• Agile UAV Flight
• SE(3) Controller
• Cluttered Environments