Test bed for applications of heterogeneous unmanned vehicles. (20th January 2017)

• Record Type: Journal Article
• Authors: Palacios, Filiberto Muñoz; Quesada, Eduardo Steed Espinoza; Sanahuja, Guillaume; Salazar, Sergio; Salazar, Octavio Garcia; Carrillo, Luis Rodolfo Garcia
• Is Part Of: International journal of advanced robotic systems. Volume 14:Number 1(2017:Jan./Feb.)
• Publication Date: 2017-01-20
• Subjects: Test bed -- heterogeneous vehicles -- laboratory environments; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881416687111
• Languages: English
• ISSNs: 1729-8806

Abstract

This 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. Finally, 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.