Compatible with the AR Drone versions 1.0 and 2.0 Provides full access to the AR Drone's maneuvering capabilities from within NI LabVIEW Supports executing animations with the AR Drone 2.0 (including flips and dances) Includes VIs to stream video and navigation data from the AR Drone's sensors into LabVIEW NI Week 2016 is upon us! Click below to live stream the keynotes and make sure to test drive our Raspberry Pi and BeagleBone Black Mini Sumo Robots in the LabVIEW Zone! Live Stream NI Week Keynotes A 4WD all terrain robotic platform which runs on Arduino UNO using XBee wireless technology and LabVIEW. LINX Update - Web Services and Data Dashboard There has been a lot of discussion on the forums about how to add a user interface to your LabVIEW application running on BeagleBone Black or Raspberry Pi. While LINX 3.0 supports low level communication protocols like TCP and…Dear small niche audience of interested students, educators, hobbyists or confused blog readers who have stumbled onto this post.  

My name is Michael Mogenson, I’m a graduate student in Mechanical Engineering at Tufts University in Boston, MA.  I work at the Center for Engineering Education and Outreach and I’m releasing the first iteration of a project I’ve been working on: the AR Drone LabVIEW Toolkit. What is the AR Drone LabVIEW Toolkit?  Its a palette of VI’s for National Instruments LabVIEW that can can control the Parrot AR Drone quadrotor.  parrot ar drone dosahWith these VI’s you can fly the AR Drone around your living room with a USB joystick or gamepad.  parrot ar drone zasięgYou can write a program to autonomously fly your AR Drone.  tavik drone buy ukYou can view the video stream from the AR Drone’s cameras or navigation data from the on-board sensors.  parrot ar drone torino

You can use the many included image processing algorithms to analyze images from the AR Drone and recognize objects, track people, navigate through enclosed spaces, or anything else you can think of. If you want the AR Drone LabVIEW Toolkit you can download it from here.  Follow the instructions in the READ ME file to install the Toolkit.  It should work with the base package of LabVIEW 2010 or later on Windows, Mac, and Linux.parrot ar drone newegg Check out these YouTube videos to see what myself and other people have created with the AR Drone LabVIEW Toolkit.parrot ar drone chile falabella Still reading this and have a lot of free time on your hands?  You can read my masters thesis about this project here. This entry was posted in Uncategorized. For this research the AR Drone quadrotor by Parrot was reverse engineered to work with the graphical programming environment LabVIEW in an attempt to create a low cost, easy to use, entry level aerial robotics platform for students, educators, and researchers.

The result of this work is the AR Drone LabVIEW Toolkit. The Parrot AR Drone is a low-cost consumer oriented quadrotor equipped with video cameras and WiFi communication. The AR Drone LabVIEW Toolkit can maneuver the AR Drone, read data from its on-board sensors, decode the video stream from the two cameras, and analyze the video frames with a variety of included image processing algorithms. This thesis consist of a discussion of how the AR Drone LabVIEW Toolkit functions followed by demonstrations of some sophisticated autonomous behaviors programmed for the AR Drone and an evaluation of the AR Drone LabVIEW Toolkit based on classroom testing and educator testimonial. Access the complete dissertation: Find an electronic copy at your library. Use the link below to access a full citation record of this graduate work: If your library subscribes to the ProQuest Dissertations & Theses (PQDT) database, you may be entitled to a free electronic version of thisIf not, you will have the option to purchase one, and access

a 24 page preview for free (if available). About ProQuest Dissertations & Theses With nearly 4 million records, the ProQuest Dissertations & Theses (PQDT) Global database is the most comprehensive collection of dissertations and theses in theIt is the database of record for graduate research. PQDT Global combines content from a range of the world's premier universities - from the Ivy League to the Russell Group. Of the nearly 4 million graduate works included in the database, ProQuest offers more than 2.5 million in full textOf those, over 1.7 million are available in PDF format. 90,000 dissertations and theses are added to the database each year. If you have questions, please feel free to visit the ProQuest Web site - Tue Jul 02 21:00:18 PDT 2013 A GroupAdmin can edit this text to add an overview for the group or any other important information. Get started by reading the document here: Using Groups in the NI Community. recently received their Leap Motion developer’s kit.

Within a day they were able to control the AR.Drone with Leap Motion gestures based on the AR.Have a look at the video for some awesome Mission Impossible style Quadrotor controls.This project follows the development of a natural user interface for controlling a Parrot 2.0 AR Drone using body gestures with a Microsoft Kinect (v1). Through this document I will share the different techniques used in the gesture recognition task and implementation. This project was made some time ago, but finally took the time to publish it here, I started the project first as a hobby, but later new team members added, thanks to all of them for all the help with tests and documentation. (/en-us/download/details.aspx?id=40278)*Toolkits (Credits to their respective developers):The main challenge found when trying to make a body gestural interaction interface, is to select which are going to be the movements or patterns you are going to use. First they have to be easy and natural actions to the body, and moreover, you need to be able to detect them with all kind of body complexities.

With this I refer to a problem found commonly when smaller or taller people than the average (i.e kids) tried to use the system, it would be hard for them to execute a good control.As the gestures selected for the control are the following:*Takeoff: Both hands to the front of the chest, fully extended.*Emergency stop: Both hands at the head.*Landing: Take drone to low position and stay still.*Flight control was made to resemble the control used in the Parrot app, with the two joysticks in which right hand would control pitch and roll, and left hand controls yaw and altitude.Fig. 1. Working area for gesture detection. (Note the colors inverted to match left & right hand of the user with left and right joystick in the app)Reference for roll, pitch and yaw movements.In order to avoid problems when little or tall people used the control system, a coordinate normalization was proposed using the distance from the head to the hip of the user and all further measurements would be divided by this distance to be just a proportion of the height of the person, and not to take absolute coordinates.

To detect the desired gesture, a simple subtraction was made between the distance of the hand of the user and his/her shoulders.After taking some measurements of the minimum and maximum distances made by a natural extension of the arms, the following mean values were found.So with this, an active region of gesture detection was made as shown in the next figure by the blue surface. Be aware that outside of that region, no signal would be send to the drone for safety reasons. This also applies to Z axis, so that you need to have your hands extended to the front in order to take control.The first approach to control the drone was to send velocities proportional to how far your hand was extended. This is still documented in the attached documentation, but was later found to be better to just have an ON-OFF control with a limited max velocity. Explanation on how the program works can also be found there. (Btw, the document is still in Spanish –since I´m from México-, I’ll try to upload an English version later)