Test bed for applications of heterogeneous unmanned vehicles Article first published online:Issue published: AbstractThis article addresses the development and implementation of a test bed for applications of heterogeneous unmanned vehicle systems. The test bed consists of unmanned aerial vehicles (Parrot AR.Drones versions 1 or 2, Parrot SA, Paris, France, and Bebop Drones 1.0 and 2.0, Parrot SA, Paris, France), ground vehicles (WowWee Rovio, WowWee Group Limited, Hong Kong, China), and the motion capture systems VICON and OptiTrack. Such test bed allows the user to choose between two different options of development environments, to perform aerial and ground vehicles applications. On the one hand, it is possible to select an environment based on the VICON system and LabVIEW (National Instruments) or robotics operating system platforms, which make use the Parrot AR.Drone software development kit or the Bebop_autonomy Driver to communicate with the unmanned vehicles. On the other hand, it is possible to employ a platform that uses the OptiTrack system and that allows users to develop their own applications, replacing AR.

Drone’s original firmware with original code. We have developed four experimental setups to illustrate the use of the Parrot software development kit, the Bebop Driver (AutonomyLab, Simon Fraser University, British Columbia, Canada), and the original firmware replacement for performing a strategy that involves both ground and aerial vehicle tracking. buy a drone londonFinally, in order to illustrate the effectiveness of the developed test bed for the implementation of advanced controllers, we present experimental results of the implementation of three consensus algorithms: static, adaptive, and neural network, in order to accomplish that a team of multiagents systems move together to track a target.parrot ar drone dealsReferencesChooseTop of pageAbstractIntroductionTest bed descriptionDeveloped platformsMASs applicationExperimental resultsConclusionReferences <<1.parrot ar drone release date

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If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click on download. For more information or tips please see 'Downloading to a citation manager' in the Help menu. RIS (ProCite, Reference Manager)EndNoteBibTexMedlarsRefWorks Test bed for applications of heterogeneous unmanned vehicles View permissions information for this article Send me a copyRobots, UAV’s and wearable technology were the show-stoppers at CES 2014; and Parrot launched their drones amidst this tech frenzy. Now Parrot AR. Drone 2.0 has managed to steal the limelight at Fendi Women’s Fall/Winter 2014-15 Fashion Show for Milan Fashion Week on Thursday by live streaming all the action from the fashion arena across the world. Fendi partnered with Parrot to give its viewers a very unique experience of watching the fashion show via high definition 720p camera (30FPS) aboard the AR. Drone 2.0 which flew all over the ramp to capture unique angles of mesmerizing catwalks at the Fendi Show.

Parrot AR. Drone 2.0 has a cutting edge EEP design fitted with 3 carbon fiber tubes for robust structural strength and can be controlled via your smartphone too using the AR.FreeFlight app for Android and iOS devices. The drone can be pre-programmed to shoot videos in variety of movements for those niche views. AR. Drone is powered by a 1GHz 32 bit ARM Cortex A8 processor, runs on Linux 2.6.32 OS, has 3 axis gyroscope accelerometer and magnetometer for precision control and automatic stabilization. It weighs just 31 grams and measures 77.7 x 38.3 x 12.5 mm making it a lightweight UAV that can be maneuvered easily around the park. Drone 2.0 Elite Edition comes in three color options while the AR.Drone 2.0 Power Edition has two HD battery 1500 mAh compared to the 1000 mAh in Elite Edition.ROBOT2013: First Iberian Robotics Conference pp 55-63Visual Quadrotor Swarm for the IMAV 2013 Indoor CompetitionDOI: 253 of the book series Advances in Intelligent Systems and Computing (AISC)Cite this paper as: Sanchez-Lopez J.L., Pestana J., de la Puente P., Carrio A., Campoy P. (2014) Visual Quadrotor Swarm for the IMAV 2013 Indoor Competition.

In: Armada M., Sanfeliu A., Ferre M. (eds) ROBOT2013: First Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 253. Springer, ChamAbstractThis paper presents a low-cost framework for visual quadrotor swarm prototyping which will be utilized to participate in the 2013 International Micro Air Vehicle Indoor Flight Competition. The testbed facilitates the swarm design problem by utilizing a cost-efficient quadrotor platform, the Parrot AR Drone 2.0; by using markers to simplify the visual localization problem, and by broadcoasting the estimated location of the swarm members to obviate the partner dectection problem. The development team can then focus their attention on the design of a succesful swarming behaviour for the problem at hand. ArUco Codes [2] are used to sense and map obstacles and to improve the pose estimation based on the IMU data and optical flow by means of an Extended Kalman Filter localization and mapping method. A free-collision trajectory for each drone is generated by using a combination of well-known trajectory planning algorithms: probabilistic road maps, the potential field map algorithm and the A-Star algorithm.