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- W2754080827 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." @default.
- W2754080827 created "2017-09-25" @default.
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- W2754080827 date "2017-01-01" @default.
- W2754080827 modified "2023-09-27" @default.
- W2754080827 title "Autonomous Unmanned Aerial Vehicle (UAV) landing in windy conditions with MAP-Elites" @default.
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- W2754080827 doi "https://doi.org/10.1017/s0269888917000121" @default.
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