Atmospheric Thermal Location Estimation

Kahn, Aaron D.

doi:10.2514/1.g002782

Cited by 14 publications

(13 citation statements)

References 6 publications

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“…When the thermal center is known, he applies a circling strategy that harvests energy from the thermal while taking measurements to keep track of where the thermal may be moving. Kahn [5] used an energy variometer to get more detailed wind updraft measurements to aid in the locating and centering around thermals. Langelaan et al developed a few methods for estimating the wind field for small UAVs and tested the methods with a 3DOF model in simulation [6].…”

Section: Acknowledgmentsmentioning

confidence: 99%

Artificial Lumbered Flight for Autonomous Soaring

Powers

Silverberg

Gopalarathnam

2020

Journal of Guidance, Control, and Dynamics

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POWERS, THOMAS CORNELIUS. Artificial Lumbered Flight for Autonomous Soaring. (Under the direction of Drs. Larry Silverberg and Ashok Gopalarathnam).Soaring strategies are redefining the flight capabilities of small-class fixed-wing UAVs. This dissertation presents an autonomous soaring strategy that exploits updraft energy independent of the classification of an updraft. The strategy employs an artificial lumbered flight algorithm (ALFA) that weighs near-field updraft velocity estimates and mission priorities for navigation. This work raises the question of ALFA's ability to handle classified updrafts. Indeed, ALFA does not explicitly consider the classification of the updraft. Instead, ALFA measures updraft data along an aircraft's flight path, estimates updraft data ahead of the aircraft, generates candidate flightpaths ahead of the aircraft for evaluation, and then selects the best candidate flightpath based on a reward function. This dissertation describes the structure of ALFA and the tuning processes for the updraft estimator and the decision function.Flight results demonstrate the ability of artificial lumbered flight to harness atmospheric energy and complete its objectives. The flight results consider aircraft behavior in more detail, examining ALFA's effectiveness when flying among classified updrafts. They demonstrate the ability of artificial lumbered flight to navigate unclassified updrafts and harvest energy from thermal updrafts. Finally, this work highlights that autonomous flight design and control of small-class aircraft is maturing into its own flight regime that lies between the flapping flight and cruise flight regimes, and will be driven by the harvesting of energy from the atmosphere.

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Section: Acknowledgmentsmentioning

confidence: 99%

Artificial Lumbered Flight for Autonomous Soaring

Powers

Silverberg

Gopalarathnam

2020

Journal of Guidance, Control, and Dynamics

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“…The work by Allen (4) was extended by a series of studies (5,6,7,8,9,10) that proposed algorithm updates or the inclusion and validation of new features. Moreover, additional numerical and flight testing was pursued.…”

Section: Previous Workmentioning

confidence: 99%

“…Again, dedicated Fitgrid package subroutines are employed for the numerical implementation, namely surf1dp and surf2dp. Thus, the aircraft vertical velocity in calm atmosphere can be expressed as a function (with the spline based regression procedures embedded) of airspeed and turn rate as indicated by Equation (10).…”

Section: Steady Thermal Flight Optimisationmentioning

confidence: 99%

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Autonomous soaring using a simplified MPC approach

Pogorzelski¹,

Silvestre

2019

Aeronaut. j.

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The need for efficient propulsion systems allied to increasingly more challenging fixed-wing UAV mission requirements has led to recent research on the autonomous thermal soaring field with promising results. As part of that effort, the feasibility and advantages of model predictive control (MPC)-based guidance and control algorithms capable of extracting energy from natural occurring updrafts have already been demonstrated numerically. However, given the nature of the dominant atmospheric phenomena and the amplitude of the required manoeuvres, a non-linear optimal control problem results. Depending on the adopted prediction horizon length, it may be of large order, leading to implementation and real-time operation difficulties. Knowing that, an alternative MPC-based autonomous thermal soaring controller is presented herein. It is designed to yield a simple and small non-linear programming problem to be solved online. In order to accomplish that, linear prediction schemes are employed to impose the differential constraints, thus no extra variables are added to the problem and only linear bound restrictions result. For capturing the governing non-linear effects during the climb phase, a simplified representation of the aircraft kinematics with quasi-steady corrections is used by the controller internal model. Flight simulation results using a 3 degree-of-freedom model subjected to a randomly generated time varying thermal environment show that the aircraft is able to locate and exploit updrafts, suggesting that the proposed algorithm is a feasible MPC strategy to be employed in a practical application.

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“…ArduSoar uses Hazard's insights regarding thermal tracking using a KF variant. Another (extended) KF-based thermal tracker [13] uses a more sophisticated state dynamics model than ArduSoar, but is computationally more expensive as a result, and fails to capture uncertainty correlations between thermal position and strength estimates, reflected in the offdiagonal terms in the P matrix of our estimator.…”

Section: Related Workmentioning

confidence: 99%

ArduSoar: an Open-Source Thermalling Controller for Resource-Constrained Autopilots

Tabor¹,

Guilliard²,

Kolobov³

2018

Preprint

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Autonomous soaring capability has the potential to significantly increase time aloft for fixed-wing UAVs. In this paper, we introduce ArduSoar, the first soaring controller integrated into a major autopilot software suite for small UAVs. We describe ArduSoar from the algorithmic standpoint, outline its integration with the ArduPlane autopilot, discuss parameter tuning for it, and conduct a series of flight tests on real sUAVs that show ArduSoar's robustness even in highly nonideal atmospheric conditions.

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Atmospheric Thermal Location Estimation

Cited by 14 publications

References 6 publications

Artificial Lumbered Flight for Autonomous Soaring

Artificial Lumbered Flight for Autonomous Soaring

Autonomous soaring using a simplified MPC approach

ArduSoar: an Open-Source Thermalling Controller for Resource-Constrained Autopilots

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