Abstract
Micro Aerial Vehicles (MAVs) are increasingly re-garded as a valid low-cost alternative to UAVs and ground robots in surveillance missions and a number of other civil and military applications. Research on au-tonomous MAVs is still in its infancy and has focused almost exclusively on integrating control and computer vision techniques to achieve reliable autonomous flight. In this paper, we describe our approach to using automated planning in order to elicit high-level intelli-gent behaviour from autonomous MAVs engaged in surveillance applications. Planning offers effective tools to handle the unique challenges faced by MAVs that relate to their fast and unstable dynamics as well as their low endurance and small payload capabilities. We demonstrate our approach by focusing on the "Par-rot AR.Drone2.0" quadcopter and Search-and-Tracking missions, which involve searching for a mobile target and tracking it after it is found.
Original language | English |
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Title of host publication | ResearchGate |
Publisher | AAAI Press |
Pages | 445-453 |
Number of pages | 9 |
Volume | 2014-January |
Publication status | Published - Jun 2014 |