The Navigation and Control technology inside the AR.Drone micro UAV

Bristeau, Pierre-Jean; Callou, François; Vissière, David; Petit, Nicolas

doi:10.3182/20110828-6-it-1002.02327

Cited by 279 publications

(167 citation statements)

References 23 publications

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“…Stabilizing controllers based on optical flow from a monocular camera were presented e.g. in [17,18], and similar methods are integrated in commercially available hardware [19]. These systems however make strong assumptions about the environment such as a flat, horizontal ground plane.…”

Section: Related Workmentioning

confidence: 99%

Scale-aware navigation of a low-cost quadrocopter with a monocular camera

Engel

Sturm

Cremers

2014

Robotics and Autonomous Systems

186

118

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We present a complete solution for the visual navigation of a small-scale, low-cost quadrocopter in unknown environments. Our approach relies solely on a monocular camera as the main sensor, and therefore does not need external tracking aids such as GPS or visual markers. Costly computations are carried out on an external laptop that communicates over wireless LAN with the quadrocopter. Our approach consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion, and a PID controller. In this paper, we (1) propose a simple, yet effective method to compensate for large delays in the control loop using an accurate model of the quadrocopter's flight dynamics, and (2) present a novel, closed-form method to estimate the scale of a monocular SLAM system from additional metric sensors. We extensively evaluated our system in terms of pose estimation accuracy, flight accuracy, and flight agility using an external motion capture system. Furthermore, we compared the convergence and accuracy of our scale estimation method for an ultrasound altimeter and an air pressure sensor with filtering-based approaches. The complete system is available as open-source in ROS. This software can be used directly with a low-cost, off-the-shelf Parrot AR.Drone quadrocopter, and hence serves as an ideal basis for follow-up research projects.

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Section: Related Workmentioning

confidence: 99%

Scale-aware navigation of a low-cost quadrocopter with a monocular camera

Engel

Sturm

Cremers

2014

Robotics and Autonomous Systems

186

118

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“…In their research the vision group at TUM have employed a PID controller successfully on the Parrot Drone [9]. Control techniques similar to those demonstrated by TUM have been shown in Altu & Taylor [10], as well as Bristeau et al [11]. Furthermore, as researchers at Cornell University demonstrate, a particular class of drones known as micro aerial vehicles (MAV) have been able to be autonomously move in an indoor environment using algorithms based on feature detection.…”

Section: Introductionmentioning

confidence: 99%

Development of a Control and Vision Interface for an AR.Drone

Cheema

Luo

Gibbens

2016

MATEC Web of Conferences

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Abstract. The AR.Drone is a remote controlled quadcopter which is low cost, and readily available for consumers. Therefore it represents a simple test-bed on which control and vision research may be conducted. However, interfacing with the AR.Drone can be a challenge for new researchers as the AR.Drone's application programming interface (API) is built on low-level, bit-wise, C instructions. Therefore, this paper will demonstrate the use of an additional layer of abstraction on the AR.Drone's API via the Robot Operating System (ROS). Using ROS, the construction of a high-level graphical user interface (GUI) will be demonstrated, with the explicit aim of assisting new researchers in developing simple control and vision algorithms to interface with the AR.Drone. The GUI, formally known as the Control and Vision Interface (CVI) is currently used to research and develop computer vision, simultaneous localisation and mapping (SLAM), and path planning algorithms by a number of postgraduate and undergraduate students at the school of Aeronautical, Mechanical, and Mechatronics Engineering (AMME) in The University of Sydney.

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“…This value goes along with the sensing value R s calculated from the input tension received from the sensor. The following formulas (4) show how to calculate R S depending on input value received from the sensor and the ratio between R S and R 0 giving gas concentration in ppm: …”

Section: Gas Sensor Unitmentioning

confidence: 99%

“…Then a cross-compilation on ARM processor of this code can be performed directly on the drone. It is necessary to be aware of the whole inside technology and sensor that can be accessed ( [4] and [5]) to use the correct data exchange.…”

Section: Ar Drone 20 Platformmentioning

confidence: 99%

Autonomous Measurement Drone for Remote Dangerous Source Location Mapping

Croizé¹,

Archez²,

Boisson³

et al. 2015

IJESD

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Abstract-This paper presents research project conducted to realize a system able to measure autonomously a gas concentration in dangerous or hardly reachable places and locate the toxic source origin using commercialized micro Unmanned Aerial Vehicle (UAV), the AR Drone 2.0 from Parrot. The system relies upon the use of this UAV, an Arduino Nano board interfacing the gas sensor, and a GPS chip. A computer software also helps interfacing the hardware with user data.

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The Navigation and Control technology inside the AR.Drone micro UAV

Cited by 279 publications

References 23 publications

Scale-aware navigation of a low-cost quadrocopter with a monocular camera

Scale-aware navigation of a low-cost quadrocopter with a monocular camera

Development of a Control and Vision Interface for an AR.Drone

Autonomous Measurement Drone for Remote Dangerous Source Location Mapping

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