Geometric path following control for multirotor vehicles using nonlinear model predictive control and 3D spline paths

Niermeyer, Philipp; Akkinapalli, Venkata S.; Pak, Mikhail; Holzapfel, Florian; Lohmann, Boris

doi:10.1109/icuas.2016.7502541

Cited by 15 publications

(9 citation statements)

References 24 publications

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“…In [62] a cascaded control structure applying the Non-linear MPC technique for the PF outer-loop control is presented. The inner-loop control for the acceleration tracking is based on non-linear dynamic inversion.…”

Section: Model Predictive Controlmentioning

confidence: 99%

A Survey of Path Following Control Strategies for UAVs Focused on Quadrotors

Rubí

Pérez

Morcego

2019

J Intell Robot Syst

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The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.

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Section: Model Predictive Controlmentioning

confidence: 99%

A Survey of Path Following Control Strategies for UAVs Focused on Quadrotors

Rubí

Pérez

Morcego

2019

J Intell Robot Syst

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“…Path tracking is usually referred to as the task to complete a path as fast as possible under the vehicle physical constraints . In contrast, trajectory tracking classically requires that the vehicle is at a certain time at a certain position.…”

Section: Motion Planningmentioning

confidence: 99%

“…Path tracking is usually referred to as the task to complete a path as fast as possible under the vehicle physical constraints. 52 In con- has to be reached (see Figs. 13 and 15).…”

Section: Trajectory Trackingmentioning

confidence: 99%

An architecture for robust UAV navigation in GPS‐denied areas

Pérez-Grau

Ragel

Caballero

et al. 2017

Journal of Field Robotics

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This article presents a software architecture for safe and reliable autonomous navigation of aerial robots in GPS‐denied areas. The techniques employed within key modules from this architecture are explained in detail, such as a six‐dimensional localization approach based on visual odometry and Monte Carlo localization, or a variant of the Lazy Theta* algorithm for motion planning. The aerial robot used to demonstrate this approach has been extensively tested over the past 2 years for localization and state estimation without any external positioning systems, autonomous local obstacle avoidance, and local path planning among other tasks. This article describes the architecture and main algorithms used to achieve these goals to build a robust autonomous system.

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“…A drawback of this predictive path-following problem, highlighted in simulation for a crane in [13] and quadrotor in [14], is that the resulting optimal control problem is subject to two nonlinear models and system constraints making efficient implementation online in a high-frequency feedback control loop difficult.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Path-Following for Constrained Differentially Flat Systems

Greeff,

Schoellig

2017

Preprint

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For many tasks, predictive path-following control can significantly improve the performance and robustness of autonomous robots over traditional trajectory tracking control. It does this by prioritizing closeness to the path over timed progress along the path and by looking ahead to account for changes in the path. We propose a novel predictive pathfollowing approach that couples feedforward linearization with path-based model predictive control. Our approach has a few key advantages. By utilizing the differential flatness property, we reduce the path-based model predictive control problem from a nonlinear to a convex optimization problem. Robustness to disturbances is achieved by a dynamic path reference, which adjusts its speed based on the robot's progress. We also account for key system constraints. We demonstrate these advantages in experiment on a quadrotor. We show improved performance over a baseline trajectory tracking controller by keeping the quadrotor closer to the desired path under nominal conditions, with an initial offset and under a wind disturbance.

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Geometric path following control for multirotor vehicles using nonlinear model predictive control and 3D spline paths

Cited by 15 publications

References 24 publications

A Survey of Path Following Control Strategies for UAVs Focused on Quadrotors

A Survey of Path Following Control Strategies for UAVs Focused on Quadrotors

An architecture for robust UAV navigation in GPS‐denied areas

Model Predictive Path-Following for Constrained Differentially Flat Systems

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