Disturbance-Aware Flight for Aerial Robots in Narrow Space
- URL: http://arxiv.org/abs/2607.17476v1
- Date: Mon, 20 Jul 2026 01:58:05 GMT
- Title: Disturbance-Aware Flight for Aerial Robots in Narrow Space
- Abstract summary: This paper presents a disturbance-aware planning and control framework that integrates online disturbance estimation into the planning-control loop for quadrotor flight in narrow space.<n> 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.
- Score: 18.124392684552998
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 0.39~m can traverse straight, sloped, and curved tunnels as narrow as 0.6~m, outperforming human pilots in both success rate and flight efficiency.
Related papers
- Nonlinear Model Predictive Control via Sequential Convex Programming for Drone-to-Drone Docking [0.0]
This work formulates the docking task as a finite-horizon optimal control problem based on a reduced-order nonlinear model augmented with disturbance states.<n>The resulting problem is solved using sequential convex programming within a receding-horizon framework to generate dynamically feasible docking trajectories.
arXiv Detail & Related papers (2026-08-11T06:27:45Z) - SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors [48.60897189520386]
We introduce the JEPA-style model for real-time quadrotor control.<n>We combine a latent dynamics model with a novel physics-inspired prober that maps frozen latents to interpretable state.<n>To reduce the dependence on expensive and unsafe real-world data collection, we develop a structured pipeline for automated dataset generation.
arXiv Detail & Related papers (2026-06-22T15:00:59Z) - Safe Polytope-in-Polytope Motion Planning and Control with Control Barrier Functions [45.68209588239261]
This work proposes a safe local motion planning and control method that guarantees that a polytopic robot footprint stays inside a convex free-space region.<n>The proposed free-space formulation does not need any obstacle detection or segmentation.<n>The approach is validated in simulation with an autonomous surface vehicle and on hardware with a non-holonomic mobile robot.
arXiv Detail & Related papers (2026-06-08T16:44:28Z) - Accurate Trajectory Tracking with MPCC for Flapping-Wing MAVs [4.066849397181078]
Flapping-wing micro aerial vehicles offer quieter and safer operation than rotary-wing drones.<n>Traditional cascaded controllers treat altitude, speed, and heading independently, producing persistent tracking errors.<n>We propose a compact, continuously differentiable model that captures the dominant couplings of bird-scale ornithopters.
arXiv Detail & Related papers (2026-05-07T11:31:34Z) - Perception-Aware Time-Optimal Planning for Quadrotor Waypoint Flight [25.084985306310827]
This paper introduces a unified time-optimal trajectory optimization framework for vision-based quadrotors.<n>It incorporates perception constraints alongside full nonlinear dynamics, rotor actuation limits, aerodynamic effects, camera field-of-view constraints, and convex geometric gate representations.<n>Experiments demonstrate real-world flight speeds up to 9.8 m/s with 0.07 m average tracking error, and closed-loop success rates improved from 55% to 100%.
arXiv Detail & Related papers (2026-03-04T17:22:16Z) - A 26-Gram Butterfly-Inspired Robot Achieving Autonomous Tailless Flight [61.21669716392821]
textitAirPulse is a 26-gram butterfly-inspired robot that achieves the first onboard, closed-loop controlled flight for a tailless two-winged platform at this scale.<n>It replicates key biomechanical traits of butterfly flight, utilizing low-aspect-ratio, compliant carbon-fiber-reinforced wings and low-frequency flapping that reproduces characteristic biological body undulations.
arXiv Detail & Related papers (2026-02-06T15:58:50Z) - Disturbance-Aware Flight Control of Robotic Gliding Blimp via Moving Mass Actuation [5.073162687197271]
Robotic blimps, as lighter-than-air (LTA) aerial systems, offer long endurance and inherently safe operation but remain highly susceptible to wind disturbances.<n>This paper addresses the lack of disturbance-aware control frameworks for LTA platforms by explicitly modeling and compensating for wind-induced effects.<n>A moving horizon estimator (MHE) infers real-time wind perturbations and provides these estimates to a model predictive controller (MPC), enabling robust trajectory and heading regulation under varying wind conditions.
arXiv Detail & Related papers (2026-01-29T02:36:26Z) - Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments [0.0]
This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100.
The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers.
arXiv Detail & Related papers (2024-10-03T17:50:19Z) - A Tricycle Model to Accurately Control an Autonomous Racecar with Locked
Differential [71.53284767149685]
We present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar.
We include a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation.
arXiv Detail & Related papers (2023-12-22T16:29:55Z) - Motion Planning and Control for Multi Vehicle Autonomous Racing at High
Speeds [100.61456258283245]
This paper presents a multi-layer motion planning and control architecture for autonomous racing.
The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
arXiv Detail & Related papers (2022-07-22T15:16:54Z) - Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning.
Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
arXiv Detail & Related papers (2022-05-13T21:55:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.