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2017 Volume 32
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ORIGINAL RESEARCH   Open Access    

Autonomous Unmanned Aerial Vehicle (UAV) landing in windy conditions with MAP-Elites

  • 1.

    Box 352400, Seattle

  • 2.

    Department of Mechanical Engineering

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  • Abstract: With the recent increase in the use of Unmanned Aerial Vehicles (UAVs) comes a surge of inexperienced aviators who may not have the requisite skills to react appropriately if weather conditions quickly change while their aircraft are in flight. This creates a dangerous situation, in which the pilot cannot safely land the vehicle. In this work we examine the use of the MAP-Elites algorithm to search for sets of weights for use in an artificial neural network. This neural network directly controls the thrust and pitching torque of a simulated 3-degree of freedom (2 linear, 1 rotational) fixed-wing UAV, with the goal of obtaining a smooth landing profile. We then examine the use of the same algorithm in high-wind conditions, with gusts up to 30 knots.Our results show that MAP-Elites is an effective method for searching for control policies, and by evolving two separate controllers and switching which controller is active when the UAV is near-ground level, we can produce a wider variety of phenotypic behaviors. The best controllers achieved landing at a vertical speed of <1 m s−1 and at an angle of approach of <1° degree.
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  • Cite this article

    Sierra A. Adibi, Scott Forer, Jeremy Fries, Logan Yliniemi. 2017. Autonomous Unmanned Aerial Vehicle (UAV) landing in windy conditions with MAP-Elites. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888917000121
    Sierra A. Adibi, Scott Forer, Jeremy Fries, Logan Yliniemi. 2017. Autonomous Unmanned Aerial Vehicle (UAV) landing in windy conditions with MAP-Elites. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888917000121
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ORIGINAL RESEARCH   Open Access    

Autonomous Unmanned Aerial Vehicle (UAV) landing in windy conditions with MAP-Elites

  • 1.

    Box 352400, Seattle

  • 2.

    Department of Mechanical Engineering

  • Published online: 15 September 2017
The Knowledge Engineering Review  32,  2017, 32(1)  |  Cite this article

Abstract: Abstract: With the recent increase in the use of Unmanned Aerial Vehicles (UAVs) comes a surge of inexperienced aviators who may not have the requisite skills to react appropriately if weather conditions quickly change while their aircraft are in flight. This creates a dangerous situation, in which the pilot cannot safely land the vehicle. In this work we examine the use of the MAP-Elites algorithm to search for sets of weights for use in an artificial neural network. This neural network directly controls the thrust and pitching torque of a simulated 3-degree of freedom (2 linear, 1 rotational) fixed-wing UAV, with the goal of obtaining a smooth landing profile. We then examine the use of the same algorithm in high-wind conditions, with gusts up to 30 knots.Our results show that MAP-Elites is an effective method for searching for control policies, and by evolving two separate controllers and switching which controller is active when the UAV is near-ground level, we can produce a wider variety of phenotypic behaviors. The best controllers achieved landing at a vertical speed of <1 m s−1 and at an angle of approach of <1° degree.

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    Acknowledgments

    • This material is based upon work supported by the National Aeronautics and Space Administration (NASA) Space Grant College and Fellowship Training Program Cooperative Agreement #NNX15AI02H, issued through the Nevada Space Grant. The authors would also like to thank the Nevada Advanced Autonomous Systems Innovation Center (NAASIC) for their continued support.

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    Cite this article
    Sierra A. Adibi, Scott Forer, Jeremy Fries, Logan Yliniemi. 2017. Autonomous Unmanned Aerial Vehicle (UAV) landing in windy conditions with MAP-Elites. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888917000121
    Sierra A. Adibi, Scott Forer, Jeremy Fries, Logan Yliniemi. 2017. Autonomous Unmanned Aerial Vehicle (UAV) landing in windy conditions with MAP-Elites. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888917000121
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