[en] Rapid prototyping environments can speed up the research of visual control algorithms. We have designed and implemented a software framework for fast prototyping of visual control algorithms for Micro Aerial Vehicles (MAV). We have applied a combination of a proxy-based network communication architecture and a custom Application Programming Interface. This allows multiple experimental configurations, like drone swarms or distributed processing of a drones video stream. Currently, the framework supports a low-cost MAV: the Parrot AR.Drone. Real tests have been performed on this platform and the results show comparatively low figures of the extra communication delay introduced by the framework, while adding new functionalities and flexibility to the selected drone. This implementation is open-source and can be downloaded from www.vision4uav.com/?q=VC4MAV-FW
Disciplines :
Computer science
Author, co-author :
Mellado-Bataller, Ignacio; Universidad Politecnica de Madrid (UPM) > Computer Vision Group (CVG)
Campoy, Pascual; Universidad Politecnica de Madrid > Computer Vision Group
Olivares Mendez, Miguel Angel ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Mejias, Luis; Queensland University of Technology (QUT) > Australian Research Centre for Aerospace Automation (ARCAA)
Language :
English
Title :
Rapid Prototyping Framework for Visual Control of Autonomous Micro Aerial Vehicles
Publication date :
2012
Event name :
12th International Conference of Intelligent Autonomous Systems
ROS driver for the Parrot AR.Drone, http://code.google.com/p/brown-ros- pkg/wiki/ardrone-brown
Visser, A., Dijkshoorn, N., van der Veen, M., Jurriaans, R.: Closing the gap between simulation and reality in the sensor and motion models of an autonomous AR.Drone. In: Proc. International Micro Air Vehicle Conference and Flight Competition (IMAV 2011), pp. 40-47 (September 2011)
Bills, C., Chen, J., Saxena, A.: Autonomous MAV flight in indoor environments using single image perspective cues. In: Int. Conf. Robotics and Automation (ICRA), Shanghai, China, pp. 5776-5783 (May 2011)
Koval, M.C., Mansley, C.R., Littman, M.L.: Autonomous quadrotor control with reinforcement learning, http://mkoval.org/projects/quadrotor/files/ quadrotor-rl.pdf
ARDRONE open API platform, https://projects.ardrone.org
See and avoid with a fuzzy controller, http://vision4uav.eu/?q= researchline/SeeAndAvoidCE
Branicky, M.S., Phillips, S.M., Zhang, W.: Stability of networked control systems: explicit analysis of delay. In: Proc. American Control Conf, pp. 2352-2357 (June 2000)
