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

Ondřej Procházka; Juraj Pauko; Robert Pěnička; Martin Saska

cs.ROOct 6, 2026

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

Authors: Ondřej Procházka, Juraj Pauko, Robert Pěnička, Martin Saska

Organizations: Multi-robot Systems Group, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic

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.

Figures & tables

Fig. 1 : Illustrative images from real-world experiments: (a) UAV used in the experiments, (b) flown path visualization in RViz, and (c) top view of the course with the flown path.

Fig. 2 : Whole nmpp system pipeline.

Profile	$v_{xy}$	$a_{xy}$	$j_{xy}$	$v_{z}$	$a_{z}$ $\uparrow$ / $\downarrow$
	[ $\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ ]	[ $\mathrm{m}\text{\,}{\mathrm{s}}^{-2}$ ]	[ $\mathrm{m}\text{\,}{\mathrm{s}}^{-3}$ ]	[ $\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ ]	[ $\mathrm{m}\text{\,}{\mathrm{s}}^{-2}$ ]
slow	1.0	1.0	20	1.0	1.0/1.0
medium	4.0	2.0	40	2.0	1.0/1.0
fast	8.0	4.0	60	4.0	2.0/2.0
super fast	10.0	10.0	120	3.0	3.0/2.0
agile	13.0	15.0	120	3.0	4.0/2.0

TABLE I : The constraint profiles. The snap limit equals the jerk limit in every profile.

Profile	$v_{\text{peak}}$	NMPP	MPC	Poly	Red.	Red.
		(proposed)	(baseline)	(baseline)	vs MPC	vs Poly
slow	1.0–1.2	$\mathbf{0.032}$	$0.077$	$0.055$	58.2	41.3
medium	3.9–4.1	$\mathbf{0.078}$	$0.238$	$0.192$	67.3	59.5
fast	5.9–7.2	$\mathbf{0.122}$	$0.373$	$0.307$	67.4	60.3
super fast	7.3–8.8	$\mathbf{0.178}$	$0.497$	$0.398$	64.1	55.2
agile	9.5–10.0	$\mathbf{0.186}$	$0.545$	$0.624$	65.9	70.2

TABLE II : Mean 3D rmse (

\mathrm{m}

) of a

20\text{\,}\mathrm{m}

flight in free space for varying constraint profiles.

v_{\text{peak}}

is the range of peak speeds reached across the three planners in (

\mathrm{m}\text{\,}{\mathrm{s}}^{-1}

) while Reduction denotes the percentage decrease in error relative to each baseline. No standard deviation exceeded

0.045\text{\,}\mathrm{m}

Profile	RMSE [ $\mathrm{m}$ ]	$v_{\max}$ [ $\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ ]	$t_{\mathrm{mean}}$ [ $\mathrm{ms}$ ]	$t_{\mathrm{p95}}$ [ $\mathrm{ms}$ ]
slow	0.041	1.47	5.51	7.88
medium	0.096	4.29	5.86	8.66
fast	0.143	7.08	5.95	9.57
super fast	0.172	9.51	5.74	8.92
agile	0.198	11.38	6.91	9.79

TABLE III : Tracking and computational performance of nmpp in the simulated forest. The rmse is pooled over the ten flights of each profile.

t_{\mathrm{mean}}

and

t_{\mathrm{p95}}

cover the nmpc update only.

Fig. 3 : nmpp solution time over the ten simulated forest flights under the super fast profile.

Fig. 4 : Predicted trajectories obtained during the ten super fast simulation runs. Obstacles are shown in black with red-dashed safety margins.

Profile	NMPP (proposed)		SUPER
	succ. [ $\mathrm{\%}$ ]	min. clr. [ $\mathrm{m}$ ]	succ. [ $\mathrm{\%}$ ]	min. clr. [ $\mathrm{m}$ ]
slow	100	+1.243	100	-0.041
medium	100	+0.800	100	+0.004
fast	100	+0.270	100	+0.183
super fast	100	+0.264	70	+0.158
agile	86	+0.131	26	-0.145

TABLE IV : Success rate and clearance in the forest. Min. clr. is the smallest airframe-to-trunk gap over the successful flights, with the propeller tips reaching

0.231\text{\,}\mathrm{m}

from the vehicle centre, so a negative value is a propeller strike the flight survived. The agile row is from fifty flights per method.

$N_{\mathrm{flights}}$	RMSE [ $\mathrm{m}$ ]	$v_{\max}$ [ $\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ ]	$t_{\mathrm{mean}}$ [ $\mathrm{ms}$ ]	$t_{\mathrm{p95}}$ [ $\mathrm{ms}$ ]
10	$0.328\pm 0.070$	5.55	13.52	20.03

TABLE V : Aggregated performance over the ten real-world flights under the fast profile. The rmse is the mean and standard deviation over the individual flights,

v_{max}

denotes maximum achieved flight speed and

t_{mean}

and

t_{\mathrm{p95}}

denote the mean and 95th-percentile nmpp computation times, respectively.

Fig. 5 : Real uav trajectories with corresponding speeds.

Explore similar work

OA-MPPI: Occlusion-Aware Model Predictive Path Integral Control for UAV Flight
Sep 23, 2026Vittorio Palladino, Teaya Yang, Ruiqi Zhang +1Model Predictive Path Integral Unmanned Aerial Vehicles
High Performance Robotics Laboratory, Department of Mechanical Engineering, University of California, Berkeley, CA 94720, United States
Robust Integrated Planning and Control for Quadrotors in Dynamic Environments via NMPC with CBF Penalties
May 31, 2026Zeinab Shayan, Mohammadreza Izadi, Reza FaieghiQuadrotor Model Predictive Control
Autonomous Vehicles Laboratory, Department of Aerospace Engineering, Toronto Metropolitan University, Toronto, Canada.
RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight
Dec 9, 2025Vit Kratky, Robert Penicka, Parakh M. Gupta +2Multi-Uav Collision Avoidance
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic

Sep 23, 2026, cs.ROJ/K move · Enter open · S save

OA-MPPI: Occlusion-Aware Model Predictive Path Integral Control for UAV Flight

Vittorio Palladino, Teaya Yang, Ruiqi Zhang, Mark W. Mueller

High Performance Robotics Laboratory, Department of Mechanical Engineering, University of California, Berkeley, CA 94720, United States

Model Predictive Path Integral Unmanned Aerial Vehicles Obstacle Avoidance Dynamic Environments Path

Open paper AskPDF arXiv

Autonomous UAV flight through cluttered and partially unknown environments requires reasoning not only about observed obstacles but also about occluded regions that the sensor cannot observe. We present OA-MPPI, an obstacle- and occlusion-aware extension of Model Predictive Path Integral (MPPI) control for quadrotor flight that accounts for potential moving agents emerging from these regions into the vehicle's path. At every planning step, we extract a 3D occlusion boundary from the online occupancy map and use it to model the regions that hidden agents could reach over the prediction horizon. We penalize trajectories that enter these expanding regions within MPPI rollouts generated using nonlinear quadrotor dynamics and accounting for individual rotor thrust limits. We validate the proposed approach in simulation and hardware flight experiments, with the complete pipeline running onboard the vehicle in real time. Results show increased clearance from occlusion boundaries compared to baseline MPPI in both settings, as well as avoidance of an agent emerging from occlusion in simulation.

Abstract

Explore similar work

OA-MPPI: Occlusion-Aware Model Predictive Path Integral Control for UAV Flight

Robust Integrated Planning and Control for Quadrotors in Dynamic Environments via NMPC with CBF Penalties

RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight