arXiv subjects

Ondřej Procházka

Publications and source records attributed to Ondřej Procházka.

11 recordsLinked to original sources

arXiv2026

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

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.

cs.RO↗

arXiv2026

L1-MPPI: L1 Adaptive Model Predictive Path Integral for Agile UAV Control

This work proposes the L1 Adaptive Model Predictive Path Integral (L1-MPPI). It cascades L1 adaptive control with the Model Predictive Path Integral (MPPI) to improve tracking of high-speed UAV trajectories. Thanks to the L1augmentation, the tracking remains accurate even under model uncertainties and external disturbances, such as an additional payload or a mismatch in the modeled aerodynamic drag. In contrast to existing MPPI approaches for UAV control that do not explicitly model aerodynamic effects, varying payloads, and typically neglect the dynamics of low-level motor controllers, our L1-MPPI approach enhances the dynamic model used in the MPPI by incorporating the low-level flight controller and motor dynamics, as well as an iterative mixing scheme that reflects the approach of the low-level controller. The proposed method demonstrates improved tracking performance in both simulation and the real world, even when the UAV is subjected to an unknown payload. In flight with 35% mass increase, our approach lowers the RMSE by 58.61% with respect to plain MPPI. Compared to the same MPPI using an online mass estimator in place of the L1 augmentation, the RMSE is lower by 38.59%. During the real-world experiments the UAV reaches speeds up to 13.50 m/s and accelerations up to 2.5 g.

cs.RO↗

arXiv2026

Geometric Model Predictive Path Integral for Agile UAV Control with Online Collision Avoidance

In this letter, we introduce Geometric Model Predictive Path Integral (GMPPI), a sampling-based controller capable of tracking agile trajectories while avoiding obstacles. In each iteration, GMPPI generates a large number of candidate rollout trajectories and then averages them to create a nominal control to be followed by the controlled Unmanned Aerial Vehicle (UAV). Classical Model Predictive Path Integral (MPPI) faces a trade-off between tracking precision and obstacle avoidance; high-noise random rollouts are inefficient for tracking but necessary for collision avoidance. To this end, we propose leveraging geometric SE(3) control to generate a portion of GMPPI rollouts. To maximize their benefit, we introduce a UAV-tailored cost function balancing tracking performance with obstacle avoidance. All generated rollouts are projected onto depth images for collision avoidance, representing, to our knowledge, the first method utilizing depth data directly in a UAV MPPI loop. Simulations show GMPPI matches the tracking error of an obstacle-blind geometric controller while exceeding the avoidance capabilities of state-of-the-art planners and learning-based controllers. Real-world experiments demonstrate flight at speeds up to 17 m/s and obstacle avoidance up to 10 m/s.

cs.RO↗

arXiv2025

LoL-NMPC: Low-Level Dynamics Integration in Nonlinear Model Predictive Control for Unmanned Aerial Vehicles

[Accepted to IROS 2025] In this paper, we address the problem of tracking high-speed agile trajectories for Unmanned Aerial Vehicles(UAVs), where model inaccuracies can lead to large tracking errors. Existing Nonlinear Model Predictive Controller(NMPC) methods typically neglect the dynamics of the low-level flight controllers such as underlying PID controller present in many flight stacks, and this results in sub-optimal tracking performance at high speeds and accelerations. To this end, we propose a novel NMPC formulation, LoL-NMPC, which explicitly incorporates low-level controller dynamics and motor dynamics in order to minimize trajectory tracking errors while maintaining computational efficiency. By leveraging linear constraints inside low-level dynamics, our approach inherently accounts for actuator constraints without requiring additional reallocation strategies. The proposed method is validated in both simulation and real-world experiments, demonstrating improved tracking accuracy and robustness at speeds up to 98.57 km/h and accelerations of 3.5 g. Our results show an average 21.97 % reduction in trajectory tracking error over standard NMPC formulation, with LoL-NMPC maintaining real-time feasibility at 100 Hz on an embedded ARM-based flight computer.

cs.RO↗

arXiv2025

CurviTrack: Curvilinear Trajectory Tracking for High-speed Chase of a USV

Heterogeneous robot teams used in marine environments incur time-and-energy penalties when the marine vehicle has to halt the mission to allow the autonomous aerial vehicle to land for recharging. In this paper, we present a solution for this problem using a novel drag-aware model formulation which is coupled with MPC, and therefore, enables tracking and landing during high-speed curvilinear trajectories of an USV without any communication. Compared to the state-of-the-art, our approach yields 40% decrease in prediction errors, and provides a 3-fold increase in certainty of predictions. Consequently, this leads to a 30% improvement in tracking performance and 40% higher success in landing on a moving USV even during aggressive turns that are unfeasible for conventional marine missions. We test our approach in two different real-world scenarios with marine vessels of two different sizes and further solidify our results through statistical analysis in simulation to demonstrate the robustness of our method.

cs.RO↗

arXiv2025

State estimation of marine vessels affected by waves by unmanned aerial vehicles

A novel approach for robust state estimation of marine vessels in rough water is proposed in this paper to enable tight collaboration between Unmanned Aerial Vehicles (UAVs) and a marine vessel, such as cooperative landing or object manipulation, regardless of weather conditions. Our study of marine vessel (in our case Unmanned Surface Vehicle (USV)) dynamics influenced by strong wave motion has resulted in a novel nonlinear mathematical USV model with 6 degrees of freedom (DOFs), which is required for precise USV state estimation and motion prediction. The proposed state estimation and prediction approach fuses data from multiple sensors onboard the UAV and the USV to enable redundancy and robustness under varying weather conditions of real-world applications. The proposed approach provides estimated states of the USV with 6 DOFs and predicts its future states to enable tight control of both vehicles on a receding control horizon. The proposed approach was extensively tested in the realistic Gazebo simulator and successfully experimentally validated in many real-world experiments representing different application scenarios, including agile landing on an oscillating and moving USV. A comparative study indicates that the proposed approach significantly surpassed the current state-of-the-art.

cs.RO↗

arXiv2025

Towards UAV-USV Collaboration in Harsh Maritime Conditions Including Large Waves

This paper introduces a system designed for tight collaboration between Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) in harsh maritime conditions characterized by large waves. This onboard UAV system aims to enhance collaboration with USVs for following and landing tasks under such challenging conditions. The main contribution of our system is the novel mathematical USV model, describing the movement of the USV in 6 degrees of freedom on a wavy water surface, which is used to estimate and predict USV states. The estimator fuses data from multiple global and onboard sensors, ensuring accurate USV state estimation. The predictor computes future USV states using the novel mathematical USV model and the last estimated states. The estimated and predicted USV states are forwarded into a trajectory planner that generates a UAV trajectory for following the USV or landing on its deck, even in harsh environmental conditions. The proposed approach was verified in numerous simulations and deployed to the real world, where the UAV was able to follow the USV and land on its deck repeatedly.

cs.RO↗

arXiv2024

Model predictive control-based trajectory generation for agile landing of unmanned aerial vehicle on a moving boat

This paper proposes a novel trajectory generation method based on Model Predictive Control (MPC) for agile landing of an Unmanned Aerial Vehicle (UAV) onto an Unmanned Surface Vehicle (USV)'s deck in harsh conditions. The trajectory generation exploits the state predictions of the USV to create periodically updated trajectories for a multirotor UAV to precisely land on the deck of a moving USV even in cases where the deck's inclination is continuously changing. We use an MPC-based scheme to create trajectories that consider both the UAV dynamics and the predicted states of the USV up to the first derivative of position and orientation. Compared to existing approaches, our method dynamically modifies the penalization matrices to precisely follow the corresponding states with respect to the flight phase. Especially during the landing maneuver, the UAV synchronizes attitude with the USV's, allowing for fast landing on a tilted deck. Simulations show the method's reliability in various sea conditions up to Rough sea (wave height 4 m), outperforming state-of-the-art methods in landing speed and accuracy, with twice the precision on average. Finally, real-world experiments validate the simulation results, demonstrating robust landings on a moving USV, while all computations are performed in real-time onboard the UAV.

cs.RO↗

arXiv2019

Hydrogen Emission in Type II White-light Solar Flares

Type II WLFs have weak Balmer line emission and no Balmer jump. We carried out a set of radiative hydrodynamic simulations to understand how the hydrogen radiative losses vary with the electron beam parameters and more specifically with the low energy cutoff. Our results have revealed that for low energy beams, the excess flare Lyman emission diminishes with increasing low energy cutoff as the energy deposited into the top chromosphere is low compared to the energy deposited into the deeper layers. Some Balmer excess emission is always present and is driven primarily by direct heating from the beam with a minor contribution from Lyman continuum backwarming. The absence of Lyman excess emission in electron beam models with high low energy cutoff is a prominent spectral signature of type II WLFs.

astro-ph.SR↗

arXiv2018

Reproducing Type II White-Light Solar Flare Observations with Electron and Proton Beam Simulations

We investigate the cause of the suppressed Balmer series and the origin of the white-light continuum emission in the X1.0 class solar flare on 2014 June 11. We use radiative hydrodynamic simulations to model the response of the flaring atmosphere to both electron and proton beams which are energetically constrained using RHESSI and Fermi observations. A comparison of synthetic spectra with the observations allow us to narrow the range of beam fluxes and low energy cut-off that may be applicable to this event. We conclude that the electron and proton beams that can reproduce the observed spectral features are those that have relatively low fluxes and high values for the low energy cut-off. While electron beams shift the upper chromosphere and transition region to greater geometrical heights, proton beams with a similar flux leave these areas of the atmosphere relatively undisturbed. It is easier for proton beams to penetrate to the deeper layers and not deposit their energy in the upper chromosphere where the Balmer lines are formed. The relatively weak particle beams that are applicable to this flare do not cause a significant shift of the $τ=1$ surface and the observed excess WL emission is optically thin.

astro-ph.SR↗

arXiv2017

Suppression of Hydrogen Emission in an X-class White-light Solar Flare

We present unique NUV observations of a well-observed X-class flare from NOAA 12087 obtained at Ondřejov Observatory. The flare shows a strong white-light continuum but no detectable emission in the higher Balmer and Lyman lines. RHESSI and Fermi observations indicate an extremely hard X-ray spectrum and gamma-ray emission. We use the RADYN radiative hydrodynamic code to perform two type of simulations. One where an energy of 3 x 10^11 erg/cm^2/s is deposited by an electron beam with a spectral index of ~3 and a second where the same energy is applied directly to the photosphere. The combination of observations and simulations allow us to conclude that the white-light emission and the suppression or complete lack of hydrogen emission lines is best explained by a model where the dominant energy deposition layer is located in the lower layers of the solar atmosphere rather than the chromosphere.

astro-ph.SR↗