Abstract
Quadcopters are small-sized aerial vehicles with four fixed-pitch propellers. These robots have great potential since they are inexpensive with affordable hardware, and with appropriate software solutions they can accomplish assignments autonomously. They could perform daily tasks in the future, such as package deliveries, inspections, and rescue missions. In this chapter, after an extensive introduction to object recognition and tracking, we present an approach for vision-based autonomous flying of an unmanned quadcopter in various structured environments, such as hallway-like scenes. The desired flight direction is obtained visually, based on perspective clues, in particular the vanishing point. This point is the intersection of parallel lines viewed in perspective, and is sought on the front camera image. For a stable guidance the position of the vanishing point is filtered with different types of probabilistic filters, such as linear Kalman filter, extended Kalman filter, unscented Kalman filter and particle filter. These are compared in terms of the tracking error and also for computational time. A switching control method is implemented. Each of the modes focuses on controlling only one state variable at a time and the objective is to center the vanishing point on the image. The selected filtering and control methods are tested successfully, both in simulation and in real indoor and outdoor environments.
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Notes
- 1.
Amazon testing drones for deliveries, BBC News 2013.
- 2.
German railways to test anti-graffiti drones. BBC News 2013.
- 3.
Video link: https://www.youtube.com/watch?v=S7VQWP7O91k.
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Acknowledgments
This work was supported by the Romanian National Authority for Scientific Research, CNCS-UEFISCDI (Project No. PNII-RU-TE-2012-3-0040) and by grant nr. C.I.2/1.2./2015 of the Technical University of Cluj-Napoca.
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Páll, E., Tamás, L., Buşoniu, L. (2015). Vision-Based Quadcopter Navigation in Structured Environments. In: Busoniu, L., Tamás, L. (eds) Handling Uncertainty and Networked Structure in Robot Control. Studies in Systems, Decision and Control, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-26327-4_11
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DOI: https://doi.org/10.1007/978-3-319-26327-4_11
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