Autonomous Fixed-Wing Aerobatics: From Theory to Flight

Bulka, Eitan; Nahon, Meyer

doi:10.1109/icra.2018.8460610

Cited by 22 publications

(11 citation statements)

References 16 publications

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“…[22] explored real-time trajectory motion-planning of fixed-wing UAVs using simplified models restricted to low angle-of-attack domains. In [4], [23], [24], [25], [26], researchers have developed motion planners for aerobatic fixed-wing UAVs using trajectory libraries generated offline using high fidelity physics models. In some cases, especially in the presence of high winds, NMPC has been utilized to control fixed-wing UAVs [27].…”

Section: Related Workmentioning

confidence: 99%

Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision

Polevoy¹,

Basescu²,

Scheuer³

et al. 2022

Preprint

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Recent research has enabled fixed-wing unmanned aerial vehicles (UAVs) to maneuver in constrained spaces through the use of direct nonlinear model predictive control (NMPC) [1]. However, this approach has been limited to a priori known maps and ground truth state measurements. In this paper, we present a direct NMPC approach that leverages NanoMap [2], a light-weight point-cloud mapping framework to generate collision-free trajectories using onboard stereo vision. We first explore our approach in simulation and demonstrate that our algorithm is sufficient to enable vision-based navigation in urban environments. We then demonstrate our approach in hardware using a 42-inch fixed-wing UAV and show that our motion planning algorithm is capable of navigating around a building using a minimalistic set of goal-points. We also show that storing a point-cloud history is important for navigating these types of constrained environments.

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Section: Related Workmentioning

confidence: 99%

Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision

Polevoy¹,

Basescu²,

Scheuer³

et al. 2022

Preprint

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“…However, proportional laws present several problems, such as the generation of acceleration instead of velocity, the lack of future prediction, and the inability to introduce constraints or consider perturbations. Thus, optimal control algorithms, such as LQR and MPC, have been explored in recent investigations and implementations to generate predictive control guidance laws, resulting in a more stable tracking of the reference, but also in an increase in the complexity [32,[34][35][36][37].…”

Section: Guidancementioning

confidence: 99%

“…Notice that constraint (34) indicates that the predicted state x k+i+1|k is computed recursively using the state and inputs from the previous state k + i|k, where x k|k =x k . Linear inequalities (35) and (36) are constraints for the predicted states and input trajectories, respectively, where matrices H and D and vectors h and d represent the constraint limits.…”

Section: Model Predictive Controlmentioning

confidence: 99%

Comparative Study of Optimal Multivariable LQR and MPC Controllers for Unmanned Combat Air Systems in Trajectory Tracking

2021

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Guidance, navigation, and control system design is, undoubtedly, one of the most relevant issues in any type of unmanned aerial vehicle, especially in the case of military missions. This task needs to be performed in the most efficient way possible, which involves trying to satisfy a set of requirements that are sometimes in opposition. The purpose of this article was to compare two different control strategies in conjunction with a path-planning and guidance system with the objective of completing military missions in the most satisfactory way. For this purpose, a novel dynamic trajectory-planning algorithm is employed, which can obtain an appropriate trajectory by analyzing the environment as a discrete 3D adaptive mesh and performs a softening process a posteriori. Moreover, two multivariable control techniques are proposed, i.e., the linear quadratic regulator and the model predictive control, which were designed to offer optimal responses in terms of stability and robustness.

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“…Authors have also investigated in simulation aggressive aerobatic fixed-wing turn-around maneuvers [20,14]. The authors of [15,11,2] have for the last several years been leading the way in developing control and planning strategies for aerobatic fixed-wing vehicles. Their work has been characterized by very high fidelity physics models [11] which break aerodynamic surfaces into segments, capture transient aerodynamic effects, and model motor and propeller physics.…”

Section: Related Workmentioning

confidence: 99%

Direct NMPC for Post-Stall Motion Planning with Fixed-Wing UAVs

Basescu¹,

Moore²

2020

Preprint

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Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. However, these vehicles have traditionally been severely limited with regards to maneuverability. In this paper, we present a nonlinear control approach for enabling aerobatic fixed-wing UAVs to maneuver in constrained spaces. Our approach utilizes full-state direct trajectory optimization and a minimalistic, but representative, nonlinear aircraft model to plan aggressive fixed-wing trajectories in real-time at 5 Hz across high angles-of-attack. Randomized motion planning is used to avoid local minima and local-linear feedback is used to compensate for model inaccuracies between updates. We demonstrate our method in hardware and show that both local-linear feedback and re-planning are necessary for successful navigation of a complex environment in the presence of model uncertainty.

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Autonomous Fixed-Wing Aerobatics: From Theory to Flight

Cited by 22 publications

References 16 publications

Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision

Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision

Comparative Study of Optimal Multivariable LQR and MPC Controllers for Unmanned Combat Air Systems in Trajectory Tracking

Direct NMPC for Post-Stall Motion Planning with Fixed-Wing UAVs

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