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Simulations

AEROS: Autonomous Wind Turbine Inspection

Company/Entity: 
Funded by Ministerio de Economía y Competitividad

  

 

Starting date: 
2014-02-03
Finishing date: 
2016-12-31

AR Drone Identification and Navigation Control at CVG-UPM

Publication
AR Drone Identification and Navigation Control at CVG-UPM
This article presents the proposal of the Computer Vision Group to the first phase of the international competition "Concurso de Ingeniería de Control 2012, Control Autónomo del seguimiento de trayectorias de un vehículo cuatrirrotor". This phase consists mainly of two parts: identifying a model and designing a trajectory controller for the AR Drone quadrotor. For the identification task, two models are proposed: a simplified model that captures only the main dynamics of the quadrotor, and a second model based on the physical laws underlying the AR Drone behavior. The trajectory controller design is based on the simplified model, whereas the physical model is used to tune the controller to attain a certain level of robust stability to model uncertainties. The controller design is simplified by the hypothesis that accurate positions sensors will be available to implement a feedback controller.
Congresses name: 

XXXIII Jornadas Nacionales de Automática, 2012

Place: 

Vigo, Spain

Date: 
September 5-7, 2012

This article presents the proposal of the Computer Vision Group to the first phase of the international competition "Concurso de Ingeniería de Control 2012, Control Autónomo del seguimiento de trayectorias de un vehículo cuatrirrotor". This phase consists mainly of two parts: identifying a model and designing a trajectory controller for the AR Drone quadrotor.

Quadrotor modeling and model parameters identification

Quadrotor modeling and model parameters identification
Two quadrotor models are proposed: a simplified model that captures only the main dynamics of the quadrotor, and a second model based on the physical laws underlying the AR Drone behavior. For each model a general methodology for the model parameters identification/estimation is proposed.
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In order to perform valuable controller design in a simulation environtment, it is important to develop both: rich and worthy quadrotor models, and parameter identification methodologies for these models. Two quadrotor models are proposed: a simplified model that captures only the main dynamics of the quadrotor, and a second model based on the physical laws underlying the AR Drone behavior. For each model a general methodology for the model parameters identification is proposed.
 

 

In order to perform valuable controller design in a simulation environtment, it is important to develop both: rich and worthy quadrotor models, and parameter identification methodologies for these models. Two quadrotor models are proposed: a simplified model that captures only the main dynamics of the quadrotor, and a second model based on the physical laws underlying the AR Drone behavior. For each model a general methodology for the model parameters identification is proposed.

See and Avoid with a Fuzzy controller optimised using Cross-Entropy method

See and Avoid: Using Cross-Entropy method for optimize the Fuzzy controller of the quadcopter heading.
A visual servoing system with a controller based on fuzzy logic has been implemented for avoid obstacle task. The 3 gains of this controller were optimized using the Cross-Entropy method working under the ROS-Gazebo simulation.
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