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- W2038445360 abstract "Micro-aerial vehicle (MAV) swarms are a new class of mobile sensor networks with many applications, including search and rescue, urban surveillance, radiation monitoring, etc. These sensing applications require autonomously navigating a high number of low-cost, low-complexity MAV sensor nodes in hazardous environments. The lack of preexisting localization infrastructure and the limited sensing, computing, and communication abilities of individual nodes makes it challenging for nodes to autonomously navigate to suitable preassigned locations. In this paper, we present a collaborative and adaptive algorithm for resource-constrained MAV nodes to quickly and efficiently navigate to preassigned locations. Using radio fingerprints between flying and landed MAVs acting as radio beacons, the algorithm detects intersections in trajectories of mobile nodes. The algorithm combines noisy dead-reckoning measurements from multiple MAVs at detected intersections to improve the accuracy of the MAVs' location estimations. In addition, the algorithm plans intersecting trajectories of MAV nodes to aid the location estimation and provide desired performance in terms of timeliness and accuracy of navigation. We evaluate the performance of our algorithm through a real testbed implementation and large-scale physical feature based simulations. Our results show that, compared to existing autonomous navigation strategies, our algorithm achieves up to 6X reduction in location estimation errors, and as much as 3X improvement in navigation success rate under the given time and accuracy constraints." @default.
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- W2038445360 date "2015-11-01" @default.
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- W2038445360 title "DrunkWalk" @default.
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- W2038445360 doi "https://doi.org/10.1145/2809695.2809724" @default.
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