Autonomous Soaring for Improved Endurance of a Small Uninhabitated Air Vehicle

Allen, Michael J.

doi:10.2514/6.2005-1025

Cited by 88 publications

(73 citation statements)

References 4 publications

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“…2. Although this model is less accurate than others [2] [8], it lends itself well to mathematical analysis [3]. None of the existing thermal distributions found meet what is felt to be a suitable starting distribution.…”

Section: A Thermal Profilesmentioning

confidence: 84%

“…The autopilot that is fitted in the XCalibur is capable of accepting bank angle commands. This facilitates a more straightforward implementation of the Reichmann method than previous possible [2] [3].…”

Section: Choice Of and Evaluation Of Thermalsmentioning

confidence: 99%

“…The X-Models XCalibur is a self launch electric glider with a 3.2m span and a typical take-off weight of 3.5kg, giving a wing loading of approximately 120Pa. The XCalibur was developed from X-Models F3J competition aircraft, and as such has a performance comparable with larger gliders used by other researchers [2] [3]. An accurate model of the aircraft in use also facilitates the stabilisation and control algorithms to be validated in conjunction with the high-level trajectory generation algorithms.…”

Section: Aircraft Dynamics Modelmentioning

confidence: 99%

“…Since Wharington first proposed that static soaring was a viable option guidance algorithms have been developed using reinforcement learning and a neural-based thermal locator to detect and utilize thermals [1]. The results showed that both heuristic controllers [2] [3] and reinforcement learning could be effectively combined with a thermal locating algorithm to improve UAV performance. Algorithms utilising reinforcement learning have proved too computationally expensive for real-world application, leaving robust but heuristic algorithms the only option with current processing power.…”

mentioning

confidence: 99%

“…Algorithms utilising reinforcement learning have proved too computationally expensive for real-world application, leaving robust but heuristic algorithms the only option with current processing power. These simple heuristic algorithms have been successfully employed [2][3] but there still is a desire to further optimise the aircraft trajectories.…”

mentioning

confidence: 99%

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Trajectory generation for autonomous soaring UAS

Clarke

Chen

2012

Int. J. Autom. Comput.

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Abstract-As unmanned aerial vehicles are expected to do more and more advanced tasks, improved range and persistence is required. This paper presents a method of using shallow layer cumulus convection to extend the range and duration of small UAVs. A simulation model of an XModels XCalubur electric motor-glider is used in combination with a refined 4D parametric thermal model to simulate soaring flight. The parametric thermal model builds on previous successful models with refinements to more accurately describe the weather in northern Europe. The implementation of the variation of the MacCready setting is discussed. Methods for generating efficient trajectories are evaluated and recommendations are made regarding implementation.

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Section: A Thermal Profilesmentioning

confidence: 84%

Section: Choice Of and Evaluation Of Thermalsmentioning

confidence: 99%

Section: Aircraft Dynamics Modelmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Trajectory generation for autonomous soaring UAS

Clarke

Chen

2012

Int. J. Autom. Comput.

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The AutoSOAR autonomous soaring aircraft part 2: Hardware implementation and flight results

Depenbusch

Bird

Langelaan

2017

Journal of Field Robotics

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Autonomous soaring has the potential to greatly improve both the range and endurance of small robotic aircraft. This paper describes the results of a test flight campaign to demonstrate an autonomous soaring system that generates a dynamic map of lift sources (thermals) in the environment and uses this map for on‐line flight planning and decision making. The aircraft is based on a commercially available radio‐controlled glider; it is equipped with an autopilot module for low‐level flight control and on‐board computer that hosts all autonomy algorithms. Components of the autonomy algorithm include thermal mapping, explore/exploit decision making, navigation, optimal airspeed computation, thermal centering control, and energy state estimation. A finite state machine manages flight behaviors and switching between behaviors. Flight tests at Aberdeen Proving Ground resulted in 7.8 h flight time with the autonomous soaring system engaged, with three hours spent climbing in thermals. Postflight computation of energy state and frequent observations of groups of birds thermalling with our aircraft indicate that it was effectively exploiting available energy.

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