Trajectory Generation for UAVs in Unknown Environments with Extreme Wind Disturbances

Adam M. Wickenheiser; Kenan Cole

arxiv: 1906.09508 · v1 · pith:WPPKV25Gnew · submitted 2019-06-22 · 📡 eess.SY · cs.RO· cs.SY· math.OC

Trajectory Generation for UAVs in Unknown Environments with Extreme Wind Disturbances

Kenan Cole , Adam M. Wickenheiser This is my paper

Pith reviewed 2026-05-25 17:47 UTC · model grok-4.3

classification 📡 eess.SY cs.ROcs.SYmath.OC

keywords UAV trajectory generationwind disturbancesdrift modecollision avoidanceextreme environmentsquadrotor controlautonomous navigationdisturbance rejection

0 comments

The pith

UAVs generate and track trajectories in extreme winds by switching to a drift frame aligned with the prevailing wind.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a controller for UAVs that operates in two modes to handle wind disturbances exceeding closed-loop stability limits. Normal mode applies when environmental conditions stay within thrust and sensor bounds. Drift mode activates otherwise, defining a frame that translates with the wind so the vehicle can replan and track trajectories while retaining control authority. Formal guarantees cover trajectory tracking, collision avoidance, and constraint satisfaction. Simulations compare performance with and without the drift mode and show multiple vehicles updating navigation parameters on the fly through narrow openings.

Core claim

The controller switches between normal and drift modes; in drift mode a frame moving with the prevailing wind allows the UAV to maintain control authority by relaxing inertial-frame tracking requirements and replanning trajectories inside the moving frame, with established guarantees on tracking, collision avoidance, and thrust/sensor limits.

What carries the argument

The drift frame, a coordinate system translating at the prevailing wind velocity, inside which the UAV regains sufficient control authority for trajectory generation and tracking.

If this is right

UAVs can continue navigation through unknown regions where wind exceeds stability limits without immediate loss of control.
On-board parameter updates allow vehicles to adapt navigation when crossing between protected and windy zones.
Multiple vehicles can safely pass through narrow openings while one or more operate in drift mode.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The approach could be tested with slowly varying rather than strictly constant winds to check robustness of the drift-frame assumption.
Integration with real-time wind estimation from onboard sensors might reduce reliance on a pre-established constant wind velocity.
Similar mode-switching logic could apply to other platforms such as fixed-wing aircraft encountering gusts that exceed their stability envelope.

Load-bearing premise

A roughly constant prevailing wind velocity can be identified and used to define a moving frame in which the vehicle regains adequate control authority.

What would settle it

A UAV that loses control authority or violates thrust limits after entering drift mode inside a constant wind field exceeding the stability bounds.

Figures

Figures reproduced from arXiv: 1906.09508 by Adam M. Wickenheiser, Kenan Cole.

**Figure 1.** Figure 1: Example showing two vehicles experiencing a large gust. The vehicle trajectories are overlaid to demonstrate the difference in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Figure 2: Summary of course change definition in response to an obstacle moving toward the vehicle. (A) Determination of the constraining geometry from the sensor input. The bounding extent points, pk,e1, pk,e2, corresponding projected extent points p ∗ k,e1 , p ∗ k,e2 , minimum sensed point, pk,min, and projected minimum point, p ∗ k,min, are all used to determine an appropriate course change and circumnavigation d… view at source ↗

**Figure 3.** Figure 3: The vehicle leaves a protected area and enters a wind field, which requires updating the clearance radius, [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Determination of candidate course change points for a vehicle that is temporarily violating [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Tangent direction definitions when there are temporary [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Summary of trajectory generation decisions comparing the drift frame to the inertial frame for navigating around a stationary obstacle. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗

**Figure 7.** Figure 7: Wind experienced by both vehicles in Simulation A, where there is a gust of [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗

**Figure 8.** Figure 8: Simulation A results where two vehicles experience a large gust, [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗

**Figure 9.** Figure 9: Wind experienced by the vehicles in Simulation B. The vehicles are initially protected before being exposed to the wind. [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗

**Figure 10.** Figure 10: Simulation B results showing the vehicle trajectories in the environment. The vehicles traverse through two narrow openings of the [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗

**Figure 11.** Figure 11: Variation in cruise velocity and clearance radius as a function of wind estimation for Simulation B. (A) Resulting changes in [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗

read the original abstract

The widespread use of unmanned aerial vehicles (UAVs) by the military, commercial companies, and academia continues to push research for autonomous vehicle navigation, particularly in varying environmental conditions and beyond-line-of-sight (BLOS) applications. This article addresses trajectory generation for UAVs operating in extreme environments where the wind disturbances may exceed the vehicle's closed-loop stability bounds. To do this, a controller is developed that has two modes of operation: (1) normal mode, and (2) drift mode. In the normal mode the vehicle's thrust and sensor limitations are not exceeded by environmental conditions, whereas in the drift mode they are. In the drift mode, a drift frame that moves with the prevailing wind is established in which the vehicle maintains control authority to generate and track trajectories. The vehicle maintains control authority by relaxing the inertial frame trajectory tracking requirement and re-planning the trajectory in the drift frame. Guarantees are established to ensure tracking of the trajectory, collision avoidance, and respecting the vehicle thrust and sensor limitations. Simulation results demonstrate the algorithm properties through two scenarios. First, the performance of two quadrotors is compared where one utilizes the drift mode and the other does not. Second, multiple vehicles navigate through two narrow openings between protected and windy environments to demonstrate on-board updates to navigation parameters based on environmental conditions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Drift-mode re-planning in a wind-aligned frame is a practical idea for high-wind UAVs, but the claimed guarantees rest on an unexamined constancy assumption with no visible derivation or bounds.

read the letter

The core contribution is a two-mode controller that switches to drift mode when winds exceed normal stability limits. In that mode the vehicle sets up a frame moving with the prevailing wind, relaxes strict inertial tracking, and re-plans trajectories inside the moving frame so it regains usable control authority. The abstract says this comes with guarantees on tracking, collision avoidance, and actuator/sensor limits, plus on-board parameter updates shown in a multi-vehicle simulation. That setup directly targets a real operational limit for BLOS UAV flights in extreme conditions, and the two simulation scenarios (one comparing drift vs. no-drift, one with narrow passages and updates) give at least a proof-of-concept demonstration. Those elements are worth noting because they address a concrete gap rather than just adding another planner variant. The soft spots are in the support for the guarantees. The abstract asserts formal properties but supplies no derivation steps, error bounds, or proof outline. More importantly, the approach requires that a drift frame with approximately constant wind velocity can be established and maintained; nothing is said about how that velocity is estimated on-board, what tolerance is allowed for gusts, or how mode switches preserve the safety properties when the wind field is unknown and varying. If the constancy assumption fails, the re-planned paths in the drift frame do not automatically translate to collision-free inertial motion. The simulations are described at a high level without reported metrics or wind-variation cases, so it is hard to judge how sensitive the method is to that assumption. This paper is aimed at researchers building trajectory generators for UAVs in adverse weather. It is coherent enough on its own terms to deserve referee time; the idea is straightforward and the simulations show the intended behavior, even if the analysis needs substantial tightening before publication.

Referee Report

3 major / 0 minor

Summary. The manuscript proposes a two-mode controller for UAV trajectory generation in extreme wind disturbances exceeding closed-loop stability bounds. Normal mode operates when environmental conditions respect thrust and sensor limits; drift mode establishes a moving drift frame aligned with the prevailing wind, relaxes inertial-frame tracking, and re-plans trajectories within that frame to regain control authority. Formal guarantees are asserted for trajectory tracking, collision avoidance, and actuator/sensor limit satisfaction. Validation consists of two simulation scenarios: a comparison of two quadrotors (one using drift mode) and multi-vehicle navigation through narrow openings with on-board parameter updates.

Significance. If the asserted guarantees can be rigorously established, the approach would enable safe UAV operation in previously inaccessible extreme-wind regimes, directly addressing BLOS and unknown-environment applications. The dual-mode structure and on-board adaptation are practically motivated; the simulation scenarios illustrate multi-agent coordination under wind transitions.

major comments (3)

[Abstract] Abstract: The central claim that 'Guarantees are established to ensure tracking of the trajectory, collision avoidance, and respecting the vehicle thrust and sensor limitations' is unsupported by any derivation, Lyapunov analysis, invariant-set argument, or error-bound derivation. The only evidence cited is two simulation scenarios whose wind models, disturbance spectra, and quantitative performance metrics are not reported; this absence is load-bearing for the paper's contribution.
[Abstract] Abstract (drift-mode paragraph): The construction of the drift frame assumes the wind velocity is 'prevailing' and sufficiently constant that the frame remains well-defined and that relaxed inertial tracking still maps to collision-free inertial motion. No bound on admissible wind variation, no estimation procedure for frame velocity, and no analysis of mode-switching transients are supplied; violation of the implicit constancy assumption would invalidate the claimed collision-avoidance guarantee.
[Abstract] Abstract (simulation section): The second scenario claims 'on-board updates to navigation parameters based on environmental conditions,' yet supplies neither the update law, the sensor model used to detect wind changes, nor a proof that the updates preserve the formal guarantees when the environment is unknown.

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for the referee's insightful comments. We provide point-by-point responses to the major comments below, agreeing that revisions are necessary to strengthen the formal aspects of the paper.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that 'Guarantees are established to ensure tracking of the trajectory, collision avoidance, and respecting the vehicle thrust and sensor limitations' is unsupported by any derivation, Lyapunov analysis, invariant-set argument, or error-bound derivation. The only evidence cited is two simulation scenarios whose wind models, disturbance spectra, and quantitative performance metrics are not reported; this absence is load-bearing for the paper's contribution.

Authors: The referee is correct; the manuscript as presented does not include the detailed derivations or simulation parameters to support the guarantee claims. We will revise the manuscript to incorporate Lyapunov analysis, invariant-set arguments, error bounds, and expanded simulation details including wind models and quantitative metrics. revision: yes
Referee: [Abstract] Abstract (drift-mode paragraph): The construction of the drift frame assumes the wind velocity is 'prevailing' and sufficiently constant that the frame remains well-defined and that relaxed inertial tracking still maps to collision-free inertial motion. No bound on admissible wind variation, no estimation procedure for frame velocity, and no analysis of mode-switching transients are supplied; violation of the implicit constancy assumption would invalidate the claimed collision-avoidance guarantee.

Authors: We agree with this assessment. The current manuscript lacks explicit bounds on wind variation and analysis of transients. In the revision, we will add these elements: admissible wind variation bounds, an onboard estimation procedure for the drift frame velocity, and a transient analysis for mode switches to support the collision avoidance guarantee. revision: yes
Referee: [Abstract] Abstract (simulation section): The second scenario claims 'on-board updates to navigation parameters based on environmental conditions,' yet supplies neither the update law, the sensor model used to detect wind changes, nor a proof that the updates preserve the formal guarantees when the environment is unknown.

Authors: This comment is valid. The manuscript does not provide the update law or sensor model. We will revise to include the update law, the sensor model for wind detection, and an argument or proof that the guarantees are maintained under these updates in unknown environments. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper defines a two-mode controller (normal and drift) for UAVs in extreme wind, with the drift mode using a moving frame aligned to prevailing wind to relax inertial tracking and re-plan trajectories while claiming guarantees on tracking, collision avoidance, and actuator/sensor limits. No equations, parameter fits, or self-citations are exhibited that reduce these guarantees by construction to the mode definitions or inputs themselves; the claims rest on asserted establishment of the guarantees rather than tautological re-labeling or fitted-input predictions. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The method rests on the existence of a usable drift frame and on the ability to derive tracking and safety guarantees once that frame is adopted; these are not supplied by upstream literature in the abstract.

free parameters (1)

wind-speed threshold for entering drift mode
Determines the switch point between normal and drift operation based on vehicle stability bounds; value not specified in abstract.

axioms (1)

domain assumption Wind disturbances can be treated as a prevailing constant velocity that defines a usable moving drift frame
Invoked to establish the reference frame in which control authority is recovered.

invented entities (1)

drift frame no independent evidence
purpose: Moving reference frame aligned with prevailing wind to restore control authority
New coordinate system introduced for trajectory re-planning when inertial-frame limits are exceeded.

pith-pipeline@v0.9.0 · 5774 in / 1258 out tokens · 26002 ms · 2026-05-25T17:47:36.719960+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Wind-Resilient Trajectory Optimization for UAV-BS Networks: TD3 for Continuous Service Availability
eess.SY 2026-06 unverdicted novelty 3.0

A TD3-based DRL framework optimizes UAV-BS trajectories by modeling wind as stochastic kinematic perturbations to maintain throughput stability and service availability.

Reference graph

Works this paper leans on

31 extracted references · 31 canonical work pages · cited by 1 Pith paper · 1 internal anchor

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B. Bialy, J. Klotz, K. Brink, and W.E Dixon. Lyapanov-based robust adaptive control of a quadrotor UA V in the presence of modeling uncertainties. In ACC, Washington, DC, pages 13–18, 2013

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N. Fischer, D. Hughes, P. Walters, E. Schwartz, and W. Dixon. Nonlinear rise-based control of an autonomous underwater vehicle. IEEE Transactions on Robotics , 2014

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E. Branlard. Wind energy: On the statistics of gusts and their propagation through a wind farm. ECN-Wind-Memo-09-005, 2009. 16 Kenan Cole Kenan Cole received her B.S., M.S., and Ph.D. degrees in mechanical engineering from The George Washington University, Washington, DC, USA, in 2007, 2011, and 2018, respectively. Her research interests include vehicle c...

work page 2009

[1] [1]

https://www.faa.gov/uas/commercial operators/

FAA - certiﬁed remote pilots including commercial operators. https://www.faa.gov/uas/commercial operators/

work page

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https://www.faa.gov/uas/commercial operators/part 107 waivers/ waivers issued/

FAA - part 107 waivers issued. https://www.faa.gov/uas/commercial operators/part 107 waivers/ waivers issued/

work page

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VanLoock, G

W. VanLoock, G. Pipeleers, M. Diehl, J. De Schutter, and J. Swevers. Optimal path following for differentially ﬂat robotic systems through a geometric problem formulation. IEEE Transactions on Robotics , 2014

work page 2014

[4] [4]

Shiller, S

Z. Shiller, S. Sharma, I. Stern, and A. Stern. Online obstacle avoidance at high speeds. The Int. J. of Robotics Research , 2013

work page 2013

[5] [5]

Matveev, M

A. Matveev, M. Hoy, and A. Savkin. A globally converging algorithm for reactive robot navigation among moving and deforming obstacles. Automatica, 2015

work page 2015

[6] [6]

Ferrera, J

E. Ferrera, J. Capit ´an, A.R. Casta ˜no, and P.J. Marr´on. Decentralized safe conﬂict resolution for multiple robots in dense scenarios. Robotics and Autonomous Systems , 91:179–193, 2017

work page 2017

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M. Hoy, A. Matveev, and A. Savkin. Collision free cooperative navigation of multiple wheeled robots in unknown cluttered environments. Robotics and Autonomous Systems , 2012

work page 2012

[8] [8]

H. Choi, Y . Kim, and I. Hwang. Reactive collision avoidance of unmanned aerial vehicles using a single vision sensor. Journal of Guidance, Control, and Dynamics , 2013

work page 2013

[9] [9]

Chunyu, Z

J. Chunyu, Z. Qu, E. Pollak, and M. Falash. Reactive target-tracking control with obstacle avoidance of unicycle type mobile robots in a dynamic environment. ACC, Baltimore, MD, pages 1190–1195, 2010

work page 2010

[10] [10]

Cole and A

K. Cole and A. Wickenheiser. Reactive trajectory generation for multiple vehicles in unknown environments with wind disturbances. IEEE Transactions on Robotics , pages 1–16, 2018

work page 2018

[11] [11]

G. Dicker. Quadrotor reorientation control for collision recovery. Master’s thesis, McGill University, 2016

work page 2016

[12] [12]

Faessler, F

M. Faessler, F. Fontana, C. Forster, and D. Scaramuzza. Automatic re-initialization and failure recovery for aggressive ﬂight with monocular vision-based quadrotor. IEEE Conference on Robotics and Automation , 2015

work page 2015

[13] [13]

Waslander and C

S. Waslander and C. Wang. Wind disturbance estimation and rejection for quadrotor position control. In AIAA Unmanned Unlimited, Seattle, WA, 2009

work page 2009

[14] [14]

Sikkel, G.C.H.E

L.N.C. Sikkel, G.C.H.E. de Croon, Wagter C. De., and Q.P. Chu. A novel online model-based wind estimation approach for quadrotor micro air vehicles using low cost mems imus. IEEE/RSJ International Conference on Intelligent Robots and Systems , 2016

work page 2016

[15] [15]

R. T. Palomaki, N. T. Rose, M. van den Bossche, T.J. Sherman, and S. F. J. De Wekker. Wind estimation in the lower atmosphere using multirotor aircraft. Journal of Atmospheric and Oceanic Technology , 2017

work page 2017

[16] [16]

Sydney, B

N. Sydney, B. Smyth, and D. Paley. Dynamic control of autonomous quadrotor ﬂight in an esimated wind ﬁeld. IEEE Conference on Decision and Control , pages 3609–3616, 2013

work page 2013

[17] [17]

Bruschi, M

P. Bruschi, M. Piotto, F. Dell’Agnello, J. Ware, and N. Roy. Wind speed and direction detection by means of solid-state anemometers embedded on small quadcopters. 30th Eurosensors Conference, 2016

work page 2016

[18] [18]

P. Saab W. Kumar, A. Ben-Tzvi and M.R. Snyder. Wireless telemetry system for real-time estimation of ship air wwake with UA Vs. Mechatronics, 2016

work page 2016

[19] [19]

Peters and N

D. Peters and N. HaQuang. Technical note: Dynamic inﬂow for practical applications. Journal of the American Helicopter Society , 1988

work page 1988

[20] [20]

Pitt and D.A

D.M. Pitt and D.A. Peters. Theoretical prediction of dynamic-inﬂow derivatives. Vertica, 5(1):21–34, 1981

work page 1981

[21] [21]

Leishman

G. Leishman. Principles of Helicopter Aerodynamics . Cambridge University Press, 2006

work page 2006

[22] [22]

Bramwell, G

A.R.S. Bramwell, G. Done, and D. Balmford. Bramwell’s Helicopter Dynamics . AIAA and Butterworth-Heinemann, 2001

work page 2001

[23] [23]

S. Newman. The foundations of helicopter ﬂight . Halsted Press, 1994

work page 1994

[24] [24]

G. Padﬁeld. Helicopter Flight Dynamics: The Theory and Application of Flying Qualities and Simulation Modeling . AIAA, 1996

work page 1996

[25] [25]

Hoffmann, H

G. Hoffmann, H. Huang, S. Waslandar, and C. Tomlin. Quadrotor helicopter ﬂight dynamics and control: Theory and experiment. AIAA Guidance, Navigation, and Control Conference, Hilton Head, SC , 2007

work page 2007

[26] [26]

Bialy, J

B. Bialy, J. Klotz, K. Brink, and W.E Dixon. Lyapanov-based robust adaptive control of a quadrotor UA V in the presence of modeling uncertainties. In ACC, Washington, DC, pages 13–18, 2013

work page 2013

[27] [27]

Cao and A.F

N. Cao and A.F. Lynch. Inner-outer loop control of a quadrotor UA Vs with input and state constraints. IEEE Transactions on Control Systems Technology, pages 1797–1804, 2016

work page 2016

[28] [28]

Fischer, D

N. Fischer, D. Hughes, P. Walters, E. Schwartz, and W. Dixon. Nonlinear rise-based control of an autonomous underwater vehicle. IEEE Transactions on Robotics , 2014

work page 2014

[29] [29]

Spatio-Temporal Wind Modeling for UAV Simulations

K. Cole and A. Wickenhesier. Spatio-temporal wind modeling for UA V simulations. http://arxiv.org/abs/1905.09954, 2019

work page internal anchor Pith review Pith/arXiv arXiv 1905

[30] [30]

Military Speciﬁcation

U.S. Military Speciﬁcation. Mil-f-8785c. 1980

work page 1980

[31] [31]

Branlard

E. Branlard. Wind energy: On the statistics of gusts and their propagation through a wind farm. ECN-Wind-Memo-09-005, 2009. 16 Kenan Cole Kenan Cole received her B.S., M.S., and Ph.D. degrees in mechanical engineering from The George Washington University, Washington, DC, USA, in 2007, 2011, and 2018, respectively. Her research interests include vehicle c...

work page 2009