Quadrotor Control

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Overview

Quadrotor control concerns keeping these agile aerial robots stable while tracking trajectories, avoiding obstacles, and handling disturbances, changing payloads, or physical interaction. Common approaches include geometric and PID controllers, incremental nonlinear dynamic inversion, and model predictive control, often paired with control barrier functions for safety; learning methods such as reinforcement learning and learned dynamics are increasingly used for agile flight and adaptation. Applications range from racing and cluttered navigation to inspection, landing, formation flight, and transporting or manipulating payloads. A central challenge is achieving reliable sim-to-real transfer and safe performance under uncertain dynamics while meeting the tight computation and sensing limits of small onboard platforms.

Latest papers

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127 papers19 papers in the last four weeks, down 14% on the four weeks before. 0.1% of all new papers.

Sep 17, 2026

Integrated Guidance and Control of a Mother-Child UAV-UGV System for Cooperative Missions

Autonomous recovery of a small multirotor onto a hovering multirotor carrier differs from recovery onto ground or shipborne platforms because the recovery surface is itself an actively controlled, thrust-limited aerial vehicle. This paper presents a field-validated autonomy framework for a heterogeneous rover-mothership-child system executing rover supervision, mothership transit, child deployment and sortie, autonomous return, aerial recovery, and synchronized descent. The recovery stack combines jerk-bounded reference generation, disturbance-observer-augmented planar tracking, feasibility-aware vertical control, a discrete-time barrier-based safety filter for relative vertical geometry, and communication-aware carrier-state prediction. The contribution is the coordinated system-level integration of these methods for recovery onto a hovering multirotor and its full-scale outdoor validation. The framework is implemented on a PX4-ROS 2 architecture using RTK-enabled GNSS, IMU, and barometric fusion, with mothership-side 1D lidar used only as an auxiliary near-contact cue. RTK-fixed positioning was maintained throughout testing. Across 20 outdoor cooperative missions, 17 successfully completed deployment, sortie, and recovery, giving an observed mission success rate of 85%. For successful recoveries, mean terminal-alignment time was 6.3 s, mean planar alignment error at acceptance was 0.18 m, maximum terminal planar deviation was 0.32 m within a 0.40 m capture radius, and minimum logged relative vertical separation during coupled descent was 0.41 m. Mothership planar station-keeping RMS error was 0.25 m. The three unsuccessful trials occurred at different mission stages and are analyzed separately. Results demonstrate practical autonomous aerial recovery within the tested outdoor operating envelope.

Aashish Sahu, R. Prasanth Kumar

Department of Mechanical & Aerospace Engineering, Indian Institute of Technology Hyderabad, Telangana, India | Department of Artificial Intelligence, Indian Institute of Technology Hyderabad, Telangana, India

Aerial RoboticsMulti-Robot CoordinationQuadrotor Control
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Sep 16, 2026

Feasibility and Singularity in High-Order Safety-Critical Control for Quadrotor UAVs

We study high-order safety-critical control of quadrotor teams under bounded inputs and pairwise collision-avoidance constraints. Squared-distance barriers may lose thrust effectiveness when the relative displacement is orthogonal to the available thrust directions, while nonsingular constraints may still be jointly infeasible under shared bounds. We characterize both phenomena through pairwise effectiveness and aggregate feasibility measures. A torque-aware dynamic extension exposes attitude torques in a fourth-order barrier and prevents the extended-input row from vanishing under positive thrust. Gaussian processes directly learn the fourth-order HOCBF residual, providing robust margins without differentiating unknown perturbations. Under residual-bound and persistent-feasibility assumptions, the resulting QP guarantees collision avoidance and recovers the nominal input whenever it satisfies the robust safety and actuator constraints.

Omayra Yago Nieto, Leonardo Colombo

Universidad Polit´ecnica de Madrid, Spain · Centre for Automation and Robotics (CSIC-UPM), Ctra. M300 Campo Real, Km 0,200, Arganda del Rey - 28500 Madrid, Spain

Safety-Critical ControlControl Barrier FunctionsRobust ControlQuadrotor ControlRobot Collision Avoidance
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Sep 15, 2026

CALOS: Control-Affine Lyapunov On-manifold Safety Layer for Safe Deep Reinforcement Learning for Quadrotors

Deep Reinforcement Learning has demonstrated remarkable capability in quadrotor control, yet learned policies offer no guarantee of respecting safety constraints during training or deployment. We present CALOS (Control-Affine Lyapunov On-manifold Safety), a runtime safety layer that enforces attitude constraints on a quadrotor without modifying the underlying learning algorithm. CALOS formulates four tilt-angle inequalities and a Lyapunov descent condition as a single quadratic program whose solution is the minimum-norm correction to the nominal torque output of the policy. The quadratic program is solved exactly via active-set enumeration over the three-dimensional torque space, with a computational cost low enough to enforce constraints in real time across thousands of parallel simulation environments, as required by modern massively parallel Deep Reinforcement Learning training. Evaluated on trajectory-tracking tasks in NVIDIA Isaac Lab, CALOS reduces lateral tracking error by 55-60% relative to an unconstrained Proximal Policy Optimization baseline while achieving zero attitude-constraint violations on the training trajectory. By restricting exploration to safe regions of the state space, the safety layer also accelerates training convergence and improves data efficiency without producing suboptimal policies.

Fabrizio Cesareo, Sebastiano Mengozzi, Nicola Mimmo +1

Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” — DEI University of Bologna, Italy

RL ControlLyapunov-Based ControlQuadratic ProgrammingQuadrotor ControlSafe RL
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Sep 15, 2026

Escape-Aware Control Barrier Functions for Quadrotor Safety under Body-Rate Limits

Control barrier functions for input-constrained systems place the admissible input set inside the definition of the safe set, yet the resulting barrier is almost always a function of the state alone; On a quadrotor this is not cosmetic: because the thrust vector must be reoriented before it can decelerate an approach, and reorientation is limited by the attainable body rate, a state-only barrier certifies states from which no escape is reachable in time; We characterize the certification gap in closed form and show its width is proportional to closing speed and inversely proportional to the body-rate limit; We then define an escape barrier on the augmented pair of state and previously applied input, with escape authority measured over the one-step reachable thrust cap; It admits a closed form and an analytic inverse for the maximum certifiable closing speed, and embeds in a predictive controller at no additional state cost; Across 550 paired closed-loop episodes on a 13-state quadrotor, the proposed controller completes every tested scenario, whereas the stopping-distance barrier enforced over the same horizon fails 15% and 25% of episodes in exactly the two scenarios that enter the predicted gap; Against an online backup-CBF baseline enforcing the same escape condition at the reached state, it holds a 29-74 degree larger directional margin and 3-18 times the clearance, and an independent conservative rollout referee finds no certified state from which escape fails.

Lei Shi, Haosong Wen, Qichao Liu

University of Wisconsin–Madison · Southeast University · The Hong Kong Polytechnic University

Safety-Critical ControlControl Barrier FunctionsRobot SafetyQuadrotor Control
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Sep 14, 2026

DuctAM: A Duct-Assisted Quadrotor-Based Aerial Manipulator Enabling High-Force Push-and-Pull Interactions

Uncrewed Aerial Manipulators (UAMs) extend the capabilities of Uncrewed Aerial Vehicles (UAVs) from perception to physical interaction. Among various aerial interactions, push-and-pull operations are fundamental manipulation primitives that require sustained horizontal forces while maintaining stable flight. In this paper, we propose DuctAM, a compact aerial manipulation platform that enhances horizontal force capability for push-and-pull interactions using two ducted fans integrated along the quadrotor interaction axis. An attitude-force decoupled control scheme enables controllable horizontal forces without requiring large attitude changes. Extensive real-world experiments are conducted to validate the DuctAM. Figure-eight trajectory tracking experiments demonstrate stable flight and accurate motion control in both quad and duct modes. Force-measurement experiments quantify the decoupled longitudinal force capability of DuctAM. Finally, representative push-and-pull interaction tasks, including cart pushing, door closing, and drawer opening, verify the practical effectiveness of DuctAM. The results show that DuctAM achieves significantly improved horizontal interaction force capability while maintaining stable flight compared with conventional UAVs.

Yi Wang, Rui Jin, Xinhang Xu +4

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 · NTU–VinUni Joint Research Laboratory for Embodied AI and Robotics, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, and VinUniversity, Hanoi, Vietnam

Aerial RoboticsQuadrotor ControlAerial Manipulation
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Sep 14, 2026

Learning to Exploit Passive Dynamics for Energy-Efficient Target Hopping of a Spring-Legged Quadcopter

Combining aerial thrust with spring-loaded hopping makes monopedal quadcopters promising for locomotion over complex terrain, but heuristic proportional-integral-derivative (PID) tuning limits coordination between active thrust and passive contact dynamics. We present a direct estimated-state-to-motor Proximal Policy Optimization (PPO) policy that commands four motors without an explicit hopping state machine or low-level attitude PID. Its reward combines Energy-Manifold Shaping for mass-normalized vertical-energy tracking and apex-state anchoring with Efficiency Shaping, which uses a history-aware power estimator to penalize general power use, impose an additional airborne-power cost, and penalize airborne near-stationarity. In representative hardware runs, the PPO-based control stack reduced cycle-averaged measured electrical power by 30.7% and mean total normalized thrust by 49.8% relative to the tuned PID-based control stack, while retaining repeatable commanded-height hopping and more concentrated landings. These observations are consistent with improved use of passive dynamics and reduced measured electrical demand.

Ruigang Chen, Qi Zhang, Zhicheng Zhong +3

Department of Mechanical Engineering and Robotics, Guangdong Technion - Israel Institute of Technology, Shantou, 515063, Guangdong, China. · Department of Mechanical Engineering, Technion-Israel Institute of Technology, Haifa, 3200003, Israel.

RL ControlEnergy-Efficient Robot LocomotionRobot LocomotionPassivity-Based ControlQuadrotor Control
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Sep 14, 2026

Dynamics-Informed Reinforcement Learning for Agile and Energy-Efficient Locomotion of a Monopedal Hopping Quadcopter

Although aerial-legged robots offer combined agility and efficiency, controlling high-speed hopping under complex hybrid dynamics is challenging. Reinforcement Learning (RL) is promising but prone to energy-inefficient "reward hacking". We propose a Dynamics-Informed RL framework for a monopedal hopping quadcopter. By embedding a target Specific Energy into the reward, we constrain the optimization to a physically viable energy manifold, ensuring stable hopping behaviour. By rewarding the phase-consistent behavior, it can encourage bio-inspired stance-phase impulse. Furthermore, penalizing the electro-mechanical power waste induces the motors generate an efficient impulse. This enables the policy to inject energy strictly during spring restitution without heuristic state machines. MuJoCo simulations validate robust height regulation and forward velocity tracking up to 2.0 m/s despite severe attitude-contact coupling. Ultimately, our approach yields a highly agile hopping gait, reducing energy consumption by 82% and 73% compared to hovering baselines and inefficiency baseline, respectively.

Ruigang Chen, Qi Zhang, Zhicheng Zhong +3

Department of Mechanical Engineering and Robotics, Guangdong Technion - Israel Institute of Technology, Shantou, 515063, Guangdong, China. · Department of Mechanical Engineering, Technion-Israel Institute of Technology, Haifa, 3200003, Israel.

Energy-Efficient Robot LocomotionRobot LocomotionRL for Legged LocomotionQuadrotor ControlPhysics-Informed RL
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Sep 11, 2026

Differential Realizability of Static Control Allocation in Multirotors: An Impossibility under Nonredundant Full Actuation and a Pseudoinverse Obstruction under Redundant Actuation

Control allocation for multirotors with bidirectional propellers is commonly formulated in signed-thrust variables, where the wrench map is linear. The signed-quadratic map from physical rotor speed to thrust, however, is not a local diffeomorphism at zero speed. This work derives two distinct consequences. Under nonredundant full actuation, a single-propeller reversal removes one instantaneous task direction; hence, no global continuously differentiable exact static allocator exists over the complete task space. Under redundant actuation, the physical task map may remain regular, yet a transverse pseudoinverse zero crossing requires an unbounded rotor-speed derivative. We define differential realizability as regularity of the physical lift of an actuator-output section, derive exact and first-order validity conditions, and distinguish structural rank loss from an allocator- induced rate singularity. A local nullspace deformation repairs isolated pseudoinverse reversals, while a global fixed-orthant construction establishes existence of regular sections at the cost of persistent task-preserving internal actuation.

Antonio Franchi, Mirko Mizzoni

Robotics and Mechatronics Department, Electrical Engineering, Mathematics, and Computer Science (EEMCS) Faculty, University of Twente, 7500 AE Enschede, The Netherlands · Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy

Quadrotor Control
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Sep 11, 2026

Curriculum-Based Adversarial Heterogeneous Agent Reinforcement Learning for Autonomous Quad-Copter Landing in Maritime Settings

Recovering unmanned aerial vehicles (UAVs) in maritime environments is challenging due to wind turbulence and ship-deck motion, making it a valuable test case for alternative control and learning approaches as conventional landing approaches often become unreliable. We study simulated mid-air capture of quadrotor UAVs by a ship-mounted robotic arm, learning robust cooperative control policies with Heterogeneous-Agent Proximal Policy Optimization (HAPPO) Reinforcement Learning. We train with HAPPO using a curriculum and an adversarial wind agent (HARL-AC) in NVIDIA Isaac Lab, and compare the obtained control policies against those generated through curriculum-based domain randomization and a benchmark trained on a single sea state. In-distribution evaluation on sea states 0/4/50/4/5 shows comparable success for HARL-AC and domain randomization of up to 97.5%97.5\%. On out-of-distribution sea states 7/8/107/8/10, HARL-AC generalizes better, achieving up to 16%16\% higher median success rate at sea state 10, and substantially lower crash rates of up to 14%14\% compared to the domain randomization policy. Furthermore, we show that the adversarially trained policy shows more cautious behavior, slightly increasing timeouts by <3%<3\%, but yields safer recovery behavior in severe, unseen conditions.

Allan Minh-Tam Nguyen, Sree Showrya Kotala, Stefan Banioi-Crijman +2

Department of Advanced Computing Sciences, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, Netherlands

Adversarial TrainingAerial RoboticsUAV Landing on Moving PlatformsQuadrotor ControlRobust RLHeterogeneous MARL
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Sep 10, 2026

Battery-Aware Predictive Trajectory Planning and Control for Multirotors Under Disturbances

This paper presents a battery-aware predictive trajectory-planning and control framework for multirotors operating under spatially localized disturbances. Candidate trajectories are evaluated through closed-loop vehicle--motor--battery propagation, allowing disturbance-induced control demand, electrical energy, battery evolution, and terminal-voltage-dependent actuator capability to enter the planning process. % A reduced-order battery model is numerically benchmarked against an independently implemented Simscape equivalent-circuit reference, with a power NRMSE of 0.64%0.64\% and a cumulative-energy discrepancy below 0.7%0.7\%. % In a 150150-s, 640640-m mission containing three disturbance regions, the selected trajectory reduces electrical energy consumption by 7.46%7.46\% and position-tracking RMSE by approximately 72%72\% relative to the disturbance-aware fixed-reference baseline. % Planner ablations show that battery-dependent terms are nonbinding at nominal SOC but alter the selected trajectory under a depleted-battery stress condition. % Execution with multiple feedback controllers further demonstrates that controller selection changes the tradeoff among tracking accuracy, energy consumption, and actuator utilization. % The results demonstrate the benefit of accounting for predicted closed-loop energetic and battery--actuator consequences during trajectory selection.

Krishna Bhavithavya Kidambi

department of Mechanical and Aerospace Engineering at University of Dayton, Dayton, OH.

Trajectory OptimizationModel Predictive ControlUAV Trajectory OptimizationQuadrotor ControlMotion Planning
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Sep 10, 2026

Contact-Aware Incremental Model Predictive Control for an Underactuated Aerial Manipulator

We present a robust contact-aware control framework for aerial writing on an underactuated platform. The framework combines nonlinear model predictive control (NMPC) for accurate end-effector position and normal-force tracking at small reference penetration depths, with consistent performance across controller tunings, with whole-body incremental nonlinear dynamic inversion (INDI) for robustness to frictional and aerodynamic disturbances during contact. The proposed controllers are validated on a quadrotor-based aerial manipulator with a rigid, single-link, one-degree-of-freedom (DoF) arm in simulation and real-world experiments. The aerial writing experiments span vertical and inclined surfaces, multiple reference forces, different friction conditions, and wind disturbances. The results demonstrate that robust simultaneous five-DoF end-effector pose and contact-force tracking is achievable on a standard underactuated quadrotor with a simple, rigid, single-link arm, without requiring a fully actuated platform, a complex arm, or dedicated force/torque sensing.

Darwin Liu, Tamas Keviczky, Sihao Sun

Faculty of Mechanical Engineering, Delft University of Technology

Nonlinear MPCForce-Controlled Robotic ManipulationQuadrotor ControlAerial Manipulation
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Sep 9, 2026

Statistical Turbulence and High-Fidelity Disturbance Fields for Quadrotor Flight Control

Reinforcement-learning quadrotor controllers are usually trained under simplified wind models, yet the impact of wind-field fidelity, as opposed to magnitude, on policy robustness remains unquantified. This paper compares five disturbance-fidelity levels, from wind-free flight and discrete 1-cosine gusts through statistical turbulence and synthetic coherent structures to large-eddy-simulation fields of the atmospheric boundary layer, in a full cross-fidelity train test evaluation of proximal policy optimization (PPO) agents, with cascaded PID and geometric SE(3) controllers as training-free references, over a 0-12 m/s wind sweep. Before any controller comparison is made, all disturbance data are validated: every synthetic generator is checked quantitatively against its analytical or certification-standard reference, and the large-eddy-simulation fields against the imposed log law. On a racing-class quadrotor in hover, the train test matrix is remarkably flat, and the cheapest structured training wind, which is discrete-gust domain randomization, ranks first in every test column, a ranking replicated on a wind-sensitive 27 g platform; a once-tuned geometric controller brackets the learned PPO policies at zero crash rate. Mechanism diagnostics show that control authority, not wind realism, bounds robustness, so wind-fidelity investment should scale with platform wind sensitivity.

Xun Huang

State Key Laboratory of Turbulence and Complex Systems, Department of Aeronautics and Astronautics, Peking University, Beijing, 100871, People’s Republic of China

Quadrotor ControlRobust RLDomain RandomizationProximal Policy Optimization
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Sep 8, 2026

AccelMPC: High-Rate, Low-Power FPGA-Accelerated Model Predictive Control for Tiny Drones

Unlocking the potential of tiny aerial robots requires order of magnitude improvements in the performance of embedded edge control. In particular, although recent cached model predictive control (MPC) solvers can handle the fast system dynamics and complex constraints required for agile drone flight, their computational demands remain prohibitive for resource-constrained robots, forcing prior implementations to operate at reduced control rates. AccelMPC overcomes this challenge through an end-to-end co-design approach that jointly optimizes the solver algorithm, numerical representation, hardware mapping, and physical integration. AccelMPC pairs a co-designed FPGA-accelerated alternating direction method of multipliers (ADMM)-based MPC solver with a custom 6g PCB, providing high-bandwidth communication for deployment on a 35g Crazyflie. Hardware experiments demonstrate 1 kHz onboard constrained MPC with dynamic obstacles, up to 15.6x faster solve times and 195.4x improvement in energy-delay product over state-of-the-art embedded microcontroller-based solvers, all while scaling to optimization problems with over 20,000 optimization variables and a comparable number of constraints. We release our PCB design files, firmware, and FPGA solver code open source.

Andrea Grillo, Brian Plancher

Dartmouth College · École Polytechnique Fédérale de Lausanne

Model Predictive ControlQuadrotor ControlFPGA Acceleration
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Sep 7, 2026

Learning to Fly: Stable Vision-Guided UAV Servoing with Compact Target-Centric Cues and Reinforcement Learning

Vision-guided reinforcement learning for Unmanned Aerial Vehicles (UAVs) remains challenging due to unstable policy optimisation, aggressive exploration, and the cost of high-dimensional visual perception. In this work, we investigate long-horizon UAV visual servoing using compact target-centric cues combined with low-dimensional sensor measurements. Rather than learning directly from RGB images, lightweight target segmentation provides image-space offsets and relative depth, which are combined with quadrotor velocity and projected-gravity measurements into a compact 12D policy observation. We compare Direct PPO with three matched-budget curriculum strategies: a Visual curriculum that progressively expands target placement difficulty, a Dynamics curriculum that gradually relaxes action constraints and smoothing, and a Joint curriculum that combines both progressions. All strategies reach comparable nominal performance, with complementary advantages across tracking metrics. Observation ablations show that proprioceptive measurements are critical for stable flight and image-space cues for target alignment, while explicit depth is not necessary for strong performance in the evaluated setting. Against tuned classical visual-servo controllers, learned policies show greater robustness to strong control and visual perturbations, while the Visual curriculum exhibits the smallest degradation under unseen target motion. Overall, the results demonstrate that compact target-centric representations can support robust long-horizon aerial visual servoing and that visual curriculum training can improve robustness to dynamic distribution shifts despite limited gains in nominal performance.

Saurbh Singh Jamwal, Nived Chebrolu

Department of Computer Science and Engineering Indian Institute of Technology Bombay

Vision-Based Robot ControlAerial RoboticsVisual RLQuadrotor ControlCurriculum Learning
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Sep 7, 2026

Conflict-Predictive Variable Horizons in Multi-Drone Distributed Model Predictive Control

In distributed model predictive control for multi-drone collision avoidance, a fixed prediction horizon forces a compromise: a short horizon is inexpensive but reacts late to approaching neighbors, whereas a long one anticipates conflicts at a per-step cost that grows superlinearly with its length. We propose a conflict-predictive variable horizon that each drone sets locally, leaving the distributed model predictive control itself unchanged. From a short history of observed positions, a drone extrapolates the flight lines of its neighbors, tests each against its own using confidence funnels that narrow with prediction range, and obtains each time to conflict in closed form. The horizon is then the smallest admissible value whose planning window covers the farthest predicted conflict. It collapses to its minimum in clear airspace and grows only when a conflict lies ahead. Provided this minimum meets a single computable feasibility bound, we prove that recursive feasibility and asymptotic stability are preserved for every horizon the policy can select. These guarantees hold for a linear model, and a cascaded inner loop reduces each quadrotor's translational dynamics to a perturbed double integrator, so they carry over to the linearized quadrotor model and, as practical stability, to the full nonlinear one. In simulation on dense antipodal-swap benchmarks, the variable horizon reduces both per-step solver cost and total computation well below those of a long fixed horizon, and it maintains separation in every run, which a short fixed horizon of comparable per-step cost does not.

Linda Mümken, Michael Schwung, Stefan Lier +1

Department of Automation Technology, South Westphalia University of Applied Sciences, 59494 Soest, Germany · Institute of Automation and Computer Control, Ruhr University Bochum, 44801 Bochum, Germany · Department of Logistics and Supply Chain Management, South Westphalia University of Applied Sciences, 59872 Meschede, Germany

Model Predictive ControlCollision AvoidanceQuadrotor Control
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Aug 24, 2026

Interpreting Control Latents for System Identification via Conditional Flow Matching

Latent-conditioned adaptive policies can control robots across changing dynamics, but their learned latents remain internal representations of the policy rather than physical models that can be inspected, rolled out, or used by other control modules. This limits closed-loop analysis, diagnosis, and further improvement of a fixed policy. A direct mapping from latent to physical parameters is also under-specified, because multiple systems can induce similar closed-loop behavior. We therefore decode each operational latent into a distribution of quadrotor models using conditional flow matching. The decoded distribution enables two downstream uses without modifying the policy: online predictive tuning of a high-level controller around the fixed low-level policy, and robustness analysis under specified disturbances. Under perturbed actuator dynamics, decoded-model predictive tuning reduces position tracking RMSE by 23%23\% and heading RMSE by 45%45\% relative to fixed gains. Under Gaussian force disturbances, decoded-model ensembles closely predict the lateral tracking-error evolution. Together, these results show that control latents can be converted into physical model ensembles for tuning, robustness analysis, and diagnosis of frozen adaptive policies.

Dingqi Zhang, Ruiqi Zhang, Mark W. Mueller

High Performance Robotics Lab, Dept. of Mechanical Engineering, UC Berkeley

System IdentificationRobot Dynamics IdentificationQuadrotor Control
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Aug 24, 2026

Spinning Quadrotor: Hover Thrust Augmentation with Passive Lifting Surfaces

Conventional multirotor aerial vehicles actively suppress yaw rotation during hover, expending power to maintain a fixed heading despite the fact that yaw regulation is not required for force balance or altitude control. This paper challenges that paradigm by proposing a spinning quadrotor architecture that intentionally operates at a sustained yaw rate, converting power traditionally spent on yaw regulation into useful aerodynamic effects. A dynamic model of the spinning quadrotor is developed, analysis for low Re range is conducted to choose an airfoil for lifting surfaces. Preliminary hardware tests show a 22% reduction in thrust required. These findings suggest that intentional yaw rotation, rather than being suppressed, can be exploited as a design mechanism for efficient and robust multirotor flight.

Aniketh Parkala, Harikumar Kandath

Robotics Research Center (RRC), IIIT Hyderabad, India

Aerial RoboticsQuadrotor Control
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Aug 19, 2026

Payload Swing Estimation and Damping Without Payload Parameters for Multirotor UAVs

Cable-suspended payload transport by multirotor UAVs is flexible but generates periodic swing disturbance that degrades tracking and risks instability. Existing anti-swing methods require additional sensors or precise identification of cable length and payload mass, limiting field deployment. We propose a swing-estimation and damping method using only the onboard IMU and throttle command, requiring no payload parameters. An extended Kalman filter extracts the periodic disturbance with the unknown pendulum frequency as an estimated state, and an active damping controller adds a correction angle to the attitude loop to dissipate pendulum energy. Flight experiments confirm robust damping across a tested range of cable-length and mass variations.

K. Taki, K. Umemoto

Mechanical Design Engineering, Division of System and Infrastructure Engineering for Safe and Sustainable Society, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui, 910-8507, Fukui, Japan

Quadrotor ControlRobot State Estimation
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Aug 17, 2026

Readiness Barrier Functions: Forward-Invariant Control Authority for Overactuated Multirotor Allocation

Allocation schemes that greedily maximize a readiness metric over the actuator fiber bundle of an overactuated multirotor produce commands that jump between disconnected optimal strata, demanding actuator rates no motor can deliver; effort-minimizing schemes are continuous but cannot guarantee that wrench-rate authority stays above any certified level. We reconcile the two by treating authority as a forward-invariant quantity: a control barrier function on the log-determinant of the drag-aware actuator-authority co-metric, enforced at torque level by a quadratic program in the allocation null space. A single design inequality renders the certified set compact and strictly interior to the actuator box, with the readiness cost of any rotor deactivation given in closed form as ln⁡(n/(n−m))\ln(n/(n{-}m)) for symmetric designs. An explicit alignment ratio states when tracking can stay exact, otherwise the wrench error is bounded by O(ρ−1/2)\mathcal{O}(ρ^{-1/2}), and a robust variant handles motor-parameter uncertainty with a closed-form floor shift independent of the airframe matrix. On a hexarotor and a fully-actuated octorotor the closed-form gap matches simulation to machine precision. Over 300300 randomized missions, greedy maximization violates the certified floor in up to 51%51\% of authority-scarce missions, while the proposed filter never leaves the certified set and, in the hardest moment reversal, commits a 79×79\times smaller wrench error. The robust variant holds the floor on all 150150 perturbed plants. A high-fidelity simulation with measured propeller and motor data shows that the floor holds under unmodeled aerodynamics, electrical limits, and delay, and where it stops applying.

Giuseppe Silano

Ricerca sul Sistema Energetico, Milan, Italy, and Czech Technical University, Prague, Czech Republic

Control Barrier FunctionsQuadrotor ControlRobust Control
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Aug 13, 2026

FAM-DQ: A Dual-Quadrotor-Based Fully Actuated Aerial Manipulator for High-Torque Interaction

Aerial physical interaction requires aerial manipulation platforms to generate large interaction forces and torques while maintaining precise end-effector control. However, conventional underactuated aerial manipulators suffer from strong position-attitude coupling, whereas fully actuated platform designs often face structural complexity, limited payload capacity, and insufficient torque output. This paper presents FAM-DQ, a dual-quadrotor based fully actuated aerial manipulator designed for high-torque physical interaction tasks. By mounting two quadrotor propulsion modules at the ends of a central frame through passive joints, while using a gear-driven servo to regulate the pointing direction, FAM-DQ achieves decoupled 66-DoF end-effector control with omnidirectional manipulation capability and enhanced torque output. Experiments including trajectory tracking, attitude tracking, static torque measurement, and screw driving validate the proposed design. FAM-DQ achieves a maximum torque of 1.019 N⋅m1.019~\mathrm{N}\cdot\mathrm{m} with a total mass of 0.447 kg0.447~\mathrm{kg}, corresponding to a torque-to-mass ratio of 2.28 N⋅m/kg2.28~\mathrm{N}\cdot\mathrm{m/kg}.

Xuwei Yang, Ruoyu Ren, Ziqian Guo

Aerial RoboticsQuadrotor ControlAerial Manipulation
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Aug 3, 2026

RADAR Perception for Dynamic Obstacle Avoidance onboard small-scale Quadrotor UAVs

Fast dynamic obstacle avoidance (DOA) on uncrewed aerial vehicles (UAVs) demands not only low-latency control and actuation but also reliable perception with sufficient sensing range for accurate obstacle detection and speed estimation. This letter presents, to the best of our knowledge, the first mmWave RADAR-based perception-and-control system for fast onboard DOA. We derive and analyze latency and spatial bounds that relate sensing range, relative speed, and control delay, yielding sufficient conditions for successful avoidance. Our system adopts a lightweight tracker based on interacting multiple models and a controller based on control-barrier functions that directly outputs evasive accelerations. It achieves position errors of less than 0.15 m, 0.93 m, and 0.87 m in x, y, and z directions for 300 experiments with three different object sizes and varying visibility (light and dark), and a similar spread for 90 experiments in smoke. An onboard implementation on a Raspberry Pi 4B demonstrates real-time feasibility with an end-to-end sensing-to-command latency of approximately 14 ms. Code and the full dataset of 390 throws are available (https://tinyurl.com/radardoagit).

Dnyandeep Mandaokar, Bernhard Rinner

Institute of Networked and Embedded Systems, University of Klagenfurt, 9020 Klagenfurt, Austria

Millimeter-Wave RadarmmWave Radar SensingQuadrotor ControlDynamic Obstacle AvoidanceRobotic Perception
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Jul 28, 2026

Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics

Unmanned aerial vehicles (UAVs), particularly quadcopters, present unique challenges for autonomous control due to their underactuated dynamics: only four available control inputs must govern six degrees of freedom. This paper investigates a physics-aware, end-to-end deep reinforcement learning (DRL) approach that acts directly on low-level body inputs, total thrust and body torques (T,τx,τy,τz)(T, τ_x, τ_y, τ_z), and closes the loop through a high-fidelity Simulink environment. Our simulator integrates a 12-state rigid-body model (MATLAB Level-2 S-Function) with (i) an Action2RPM allocation based on the Moore-Penrose pseudo-inverse of a coefficient matrix derived from thrust and drag terms, and (ii) first-order actuator dynamics for each motor (time constant Tm=0.076T_m = 0.076 s), including rotor gyroscopic coupling. A shaped reward balances goal-reaching and stability using an exponential position well, attitude penalties, and quadratic velocity costs. Four DRL algorithms, DDPG, TD3, PPO, and SAC, are evaluated in two stages: (S1) thrust-only hover and (S2) hover with pitch torque and a translated goal. Results show that SAC and TD3 achieve superior stability and exploration efficiency, while PPO is less sample-efficient. The study highlights the significance of modeling actuator lags and aerodynamic moments for stable low-level control and provides a reproducible benchmark for quadcopter DRL.

Ya-Chia Shen, Woei-Leong Chan

Department of Computer Science and Information Engineering, National Cheng Kung University · Department of Aeronautics and Astronautics, National Cheng Kung University

RL ControlQuadrotor ControlModel-Free RLPhysics-Informed RL
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Jul 28, 2026

Critical slowing down for predicting controller induced loss of control in quadrotors

We develop a novel forecasting scheme to anticipate controller induced loss of control (LOC) in quadrotors and evaluate it on real LOC flight data from four different quadrotors. For this, early warning signals of LOC are derived using critical slowing down (CSD), a generic phenomenon shown to precede critical transitions across various complex ecological and biological systems. As such, our early warning indicators are generic in the sense that no system models are needed to facilitate forecasts of LOC. The approach is evaluated on real quadrotor flight data wherein LOC occurs due to unstable controller behavior arising from input-output delays. Our approach achieves a time-to-LOC forecast of up to 0.9 seconds before LOC occurs, outperforming state-of-the-art recurrent neural network quadrotor LOC forecasters in terms of detection accuracy and LOC data reliance. In particular, we leverage insights from CSD to accurately predict LOC without using data of the LOC event itself. Going further, we apply our forecasters without any re-parameterization to anticipate a different LOC scenario, quadrotor flyways, that occur on other quadrotors flying both indoors and outdoors. Despite these differences, our approach successfully detects LOC, demonstrating that it can generalize across controller architectures, quadrotors, and LOC scenarios.

Jasper J. van Beers, Prashant Solanki, Erik-Jan van Kampen +1

PhD Candidate, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands · PostDoc, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands · Associate professor, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands

Safety-Critical ControlEarly Warning DetectionCritical Slowing DownQuadrotor Control
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Jul 26, 2026

BC-NMPC: Battery-Constrained NMPC with Propulsion Prediction and Replanning for High-Speed Flight

Trajectory tracking performance of Uncrewed Aerial Vehicles (UAVs) degrades during an agile high-speed flight due to the depletion of the battery and subsequent loss of maximum available thrust. In applications such as drone racing, this leads to a failure to complete the race due to possible collisions with obstacles. In this paper, we present a novel method for integrating battery and propulsion system models into a Nonlinear Model Predictive Controller (NMPC) framework to enable real-time prediction of the voltage, current, power, and maximum available thrust of the platform. Our proposed approach achieves lower trajectory tracking error as a result of its real-time thrust awareness, and with the help of trajectory replanning, it allows the UAV to fly in time-optimal regime throughout the mission. The accuracy of this proposed model was verified in real-world flight experiments, while the effectiveness of the replanning algorithm was evaluated in simulation. By the end of the battery capacity, compared to an unaware controller, our novel controller achieved a 25% reduction in mean position error without replanning, and an 88 % reduction with replanning.

Parakh M. Gupta, Matej Mihulka, Matej Novosad +2

Multi-robot Systems Group, Czech Technical University in Prague, Czech Republic

Robot Trajectory TrackingNonlinear MPCModel Predictive ControlUAV Trajectory OptimizationQuadrotor Control
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Jul 26, 2026

Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter

Safe quadrotor navigation in cluttered and dynamic environments depends not only on instantaneous geometric perception, but more critically on anticipating collision risks induced by relative motion. Conventional modular pipelines frequently suffer from perception latency, while end-to-end learning methods relying on implicit scalar rewards often struggle to extract reliable spatio-temporal features without physics-grounded supervision. To address this, we propose an anticipatory risk-guided reinforcement learning framework. Leveraging privileged simulator states, we construct a directionally aligned future collision risk map based on the Closest Point of Approach (CPA). Through an asymmetric actor-critic architecture, the network is trained to self-predict this structured risk, which explicitly guides the visual policy during deployment. A lightweight spatio-temporal encoder extracts motion cues directly from onboard depth sequences, bypassing explicit object tracking or optical flow estimation. Extensive simulated and real-world experiments demonstrate that our method effectively improves safety margins and flight efficiency in dense dynamic clutters compared to existing baselines. Furthermore, the learned policy achieves robust zero-shot Sim-to-Real transfer on a physical quadrotor, relying purely on abstracted spatio-temporal depth sequences and its self-predicted risk priors, validating the effectiveness of our approach and its robust generalization from simulation to reality.

Yuchao Mei, Guohao Zhang, Luxia Ai +2

National Key Laboratory of Science and Technology on Multi-spectral Information Processing, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Collision AvoidanceRisk-Sensitive RLQuadrotor ControlVisual RLDynamic Obstacle Avoidance
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Jul 20, 2026

Disturbance-Aware Flight for Aerial Robots in Narrow Space

Autonomous flight of aerial robots in narrow space remains challenging due to strong aerodynamic disturbances and limited flying space. Existing approaches mainly address aerodynamic disturbances at the control level, while motion planning typically relies on geometric constraints and fixed speed limits, leading to conservative or unsafe behaviors in confined environments. This paper presents a disturbance-aware planning and control framework (DAPCF) that integrates online disturbance estimation into the planning-control loop for quadrotor flight in narrow space. First, the dual-loop observers estimate 6-degree-of-freedom disturbance forces and torques in real time based on odometry and motor speed measurements. Then, a disturbance risk function is introduced that adaptively modulates the reference speed of the planner based on disturbance estimation, reducing velocity when disturbances exceed a threshold and restoring it under low-disturbance conditions. Finally, a motor-dynamics-based nonlinear model predictive controller (MDNMPC) with disturbance compensation is designed to ensure robust trajectory tracking under perturbed conditions. Experiments demonstrate that a quadrotor with a diagonal length of 0.39m can traverse straight, sloped, and curved tunnels as narrow as 0.6m, outperforming human pilots in both success rate and flight efficiency.

Lei Qiang, Tianyu He, Chenyang Sun +3

School of Robotics and Automation, Nanjing University, Suzhou 215163, China

Nonlinear MPCUAV NavigationQuadrotor ControlMotion Planning
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Jul 16, 2026

AeroAct: Action-Centered World-Action Models for Language-Conditioned Quadrotor Flight

Language-conditioned quadrotor flight requires a policy to ground semantic goals, anticipate the visual consequences of ego-motion, and output control references that remain smooth and dynamically executable under rapidly changing first-person views. Existing aerial vision-language navigation and vision-language-action methods commonly use discrete actions, high-level waypoints, or instantaneous velocity commands, which provide limited supervision about how flight actions change future observations. We present AeroAct, an action-centered world-action model (WAM) for quadrotor navigation. To the best of our knowledge, AeroAct is the first WAM instantiated and demonstrated for real-world aerial flight. The model adapts a pretrained video diffusion Transformer to predict local trajectory-action chunks from egocentric visual history, proprioception, and language. Future first-person frames are used during training as dense consequence supervision, while deployment directly decodes actions without generating future video. To obtain aligned visual, state, language, and dynamically feasible action data, we build a DiffAero-based pipeline with complementary Isaac Lab and 3D Gaussian splatting renderers. We further introduce a low-cost handheld collection device that couples camera observations with motion estimates to recreate flight-like egocentric trajectories, and a self-guidance procedure that improves temporal consistency across overlapping trajectory chunks. Closed-loop simulation and real-world experiments show that temporal visual context improves target tracking and object-search performance, and that WAM-based policies can be executed on a physical quadrotor.

Xinhong Zhang, Qiyuan Zhu, Yubo Huang +8

School of Automation, Beijing Institute of Technology · School of Mechanical Engineering, Beijing Institute of Technology · Independent researcher

Robotic ControlAerial RoboticsQuadrotor ControlWorld Action ModelsAerial VLNLanguage-Conditioned Robot Control
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Jul 15, 2026

Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept

We investigate conditional invertible neural networks (cINNs) as probabilistic inverse-dynamics models for multirotor control. For a planar X8 coaxial multicopter, we learn p(u∣st,ct)p(u \mid s_t, c_t) from an incremental nonlinear dynamic inversion (INDI) teacher using rational-quadratic spline coupling and invertible linear mixing. Open-loop reproduction reaches R2=0.944R^2 = 0.944, mean CRPS 0.0915, and log-probability-error correlation ρ=−0.60ρ= -0.60. Over 15 closed-loop scenarios, position RMSE matches INDI (9.7 vs. 9.5 m), with 47 percent tracking acceptably; failures separate into attitude divergence under aggressive steps and phase lag under high-frequency references, isolating command bandwidth and data coverage as dominant failure mechanisms.

Christian Wittke, Stephan Myschik, Oliver Niggemann

Computer Science in Mechanical Engineering, Helmut Schmidt University, Hamburg, Germany. · Institute for Aeronautical Engineering, University of the Bundeswehr Munich, Bavaria, Germany

Invertible Neural NetworksQuadrotor ControlInverse DynamicsNeural Network Control
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Jul 14, 2026

Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight

Quadrotors flying in tight formations are severely affected by turbulent aerodynamic interactions, such as downwash, that can cause catastrophic collisions if left unmodeled. To compensate for these effects, we propose a physics-informed residual dynamics learning framework that captures complex aerodynamic interactions while ensuring the joint multi-quadrotor system remains differentially flat. We leverage this preserved flatness to design a computationally efficient feedback linearization controller that is easily tunable with linear control techniques and cancels aerodynamic disturbances via feedforward compensation. Hardware experiments demonstrate our framework reduces average tracking errors by 31% compared to nominal baselines. Crucially, our lightweight approach matches the tracking performance of state-of-the-art nonlinear model predictive control (NMPC) while requiring an order of magnitude less computation. We are the first to show that stable, tight formation flight can be achieved with under 30 seconds of training data and a 5ms loop rate, unlocking high-fidelity aerodynamic compensation for compute-constrained flight stacks.

Pei-An Hsieh, Fengjun Yang, Nikolai Matni +1

1GRASP Laboratory, University of Pennsylvania, PA, USA

Multi-Robot Formation ControlResidual Dynamics LearningFeedback ControlQuadrotor ControlDifferential FlatnessFeedback Linearization Optimization
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Jul 13, 2026

High-level spatial Dubins airplane-based reference smoothing with low-level geometric tracking for quadrotor control

A method for the control of quadrotors is presented. It is composed of a high-level reference smoothing step and a low-level reference tracking step. The high-level step leverages the Dubins airplane model for dimensionality reduction and reduced computational complexity, and exploits its structure for decoupling, spatial modeling and the formulation of a small linear program. The low-level step leverages a geometric tracking controller, which is based on the full quadrotor model. The method is designed for the tracking of references subject to lateral constraints along the path. An example is the tracking of references along obstacle contours. It is differentiated between two different setups. Either the high-level planning step is conducted once and offline, or, alternatively, the high-level planning step is conducted recedingly online in closed-loop over a limited spatial prediction horizon.

Mogens Plessen

Findklein GmbH, Switzerland

Robot Trajectory TrackingQuadrotor ControlMotion Planning
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