Autopilot for Landing Small Fixed-Wing Unmanned Aerial Vehicles with Optical Sensors

Trittler, Martin; Rothermel, Thomas; Fichter, Walter

doi:10.2514/1.g000261

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…First, the following experiment is used to verify the IAEC algorithm. Five desired waypoints, [2,1,2], [3,5,3], [5,2,5], [10,8,4], and [12,2,5], are given in this experiment. In addition, the velocities and accelerations at the start and end points are zero.…”

Section: Landmarksmentioning

confidence: 99%

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Autonomous Landing of a Micro Aerial Vehicle on a Moving Platform Using a Composite Landmark

Xing

Pan

Feng

et al. 2019

International Journal of Aerospace Engineering

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In the existing vision-based autonomous landing systems for micro aerial vehicles (MAVs) on moving platforms, the limited range of landmark localization, the unknown measurement bias of the moving platform (such as wheel-slip or inaccurate calibration of encoders), and landing trajectory knotting seriously affect system performance. To overcome the above shortcomings, an autonomous landing system using a composite landmark is proposed in this paper. In the proposed system, a notched ring landmark and two-dimensional landmark are combined as an R2D landmark to provide visual localization over a wide range. In addition, the wheel-slip and imprecise calibration of encoders are modeled as the unknown measurement bias of the encoders and estimated online via an extended Kalman filter. The landing trajectory is planned by a solver as a convex quadratic programming problem in each control cycle. Meanwhile, an iterative algorithm for adding equality constraints is proposed and used to verify whether the planned trajectory is feasible or not. The simulation and actual landing experiment results verify the following: the visual localization with the R2D landmark has the advantages of wide localization range and high localization accuracy, the pose estimation result of the moving platform with unknown encoder measurement bias is continuous and accurate, and the proposed landing trajectory planning algorithm provides a continuous trajectory for reliable landing.

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Section: Landmarksmentioning

confidence: 99%

“…Several similar systems are presented in references [2][3][4]. Vision-based landing for MAVs has been an actively studied field in recent years [5]. Some examples are the work presented in [6], where the visual system is used to estimate a vehicle position relative to a ring landmark.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Landing of a Micro Aerial Vehicle on a Moving Platform Using a Composite Landmark

Xing

Pan

Feng

et al. 2019

International Journal of Aerospace Engineering

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“…Beyond off-the-shelf autopilots, linearization of the UAV dynamics around one or multiple trimming points [28], [29] has been adopted for multivariable robust [30], [31] or optimal [32], [33] control. Alternatively, nonlinear (e.g., Euler-Lagrange) UAV dynamics have been used for reference generation, feedforward control [34]- [36], or feedback linearization.…”

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confidence: 99%

An Underactuated Control System Design for Adaptive Autopilot of Fixed-Wing Drones

Baldi

Roy

Yang

et al. 2022

IEEE/ASME Trans. Mechatron.

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Effective design of autopilots for fixed-wing unmanned aerial vehicles (UAVs) is still a great challenge, due to unmodeled effects and uncertainties that these vehicles exhibit during flight. Unmodeled effects and uncertainties comprise longitudinal/lateral cross-couplings, as well as poor knowledge of equilibrium points (trimming points) of the UAV dynamics. The main contribution of this article is a new adaptive autopilot design, based on uncertain Euler-Lagrange dynamics of the UAV and where the control can explicitly take into account under-actuation in the dynamics, reduced structural knowledge of cross-couplings and trimming points. This system uncertainty is handled via appropriately designed adaptive laws: stability of the controlled UAV is analyzed. Hardware-in-the-loop tests, comparisons with an Ardupilot autopilot and with a robustified autopilot validate the effectiveness of the control design, even in the presence of strong saturation of the UAV actuators.

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Research on Evaluation of Sensor Deviations During Flight

Xiong

Liu

Gao

et al. 2018

Human Centered Computing

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Autopilot for Landing Small Fixed-Wing Unmanned Aerial Vehicles with Optical Sensors

Cited by 9 publications

References 13 publications

Autonomous Landing of a Micro Aerial Vehicle on a Moving Platform Using a Composite Landmark

Autonomous Landing of a Micro Aerial Vehicle on a Moving Platform Using a Composite Landmark

An Underactuated Control System Design for Adaptive Autopilot of Fixed-Wing Drones

Research on Evaluation of Sensor Deviations During Flight

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