Abstract
| Language | English |
|---|---|
| Title of host publication | Unknown Host Publication |
| Pages | 1771-1776 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 6 Dec 2010 |
| Event | Proceedings of the 1st International Workshop on Wireless Networking for Unmanned Aerial Vehicles - Miami, USA Duration: 6 Dec 2010 → … |
Workshop
| Workshop | Proceedings of the 1st International Workshop on Wireless Networking for Unmanned Aerial Vehicles |
|---|---|
| Period | 6/12/10 → … |
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Maintaining Connectivity in UAV Swarm Sensing. / Teacy, WTL; Nie, J; McClean, S; Parr, G.
Unknown Host Publication. 2010. p. 1771-1776.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Maintaining Connectivity in UAV Swarm Sensing
AU - Teacy, WTL
AU - Nie, J
AU - McClean, S
AU - Parr, G
N1 - Reference text: A. Ryan, M. Zennaro, A. Howell, R. Sengupta, and J. Hedrick, "An overview of emerging results in cooperative uav control," in the 43rd IEEE Conference on Decision and Control, vol. 1, 2004, pp. 602-607. A. Singh, A. Krause, C. Guestrin, W. Kaiser, and M. Batalin, "Efficient planning of informative paths for multiple robots," in Proceedings of IJCAI 2007, 2007, pp. 2204-2211. R. Stranders, A. Farinelli, A. Rogers, and N. Jennings, "Decentralised coordination of mobile sensors using the max-sum algorithm," in Proceedings of IJCAI 2009, 2009, pp. 299-304. M. A. Hsieh, A. Cowley, V. Kumar, and C. J. Taylor, "Maintaining network connectivity and performance in robot teams," Journal of Field Robotics, vol. 25, no. 1, pp. 111-131, 2008. S. Poduri and G. S. Sukhatme, "Constrained coverage for mobile sensor networks," in Proceedings of the IEEE International Conference on Robotics and Automation, 2004, pp. 165-172. D. J. C. MacKay, Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003. A. Krause, A. Singh, and C. Guestrin, "Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies," Journal of Machine Learning Research, vol. 9, pp. 235-284, 2008. A. Krause, H. B. McMahan, C. Guestrin, and A. Gupta, "Robust submodular observation selection," Journal of Machine Learning Research, vol. 9, pp. 2761-2801, 2008. F. R. Kschischang, B. J. Frey, and H. A. Loeliger, "Factor graphs and the sum-product algorithm," IEEE Trans. on Information Theory, vol. 42, no. 2, pp. 498-519, 2001. R. Stranders, A. Farinelli, A. Rogers, and N. J. Jennings, "Decentralised control of continously valued control parameters using the max-sum algorithm," in Proceedings of AAMAS'09, 2009, pp. 601-608. Y. Weiss and W. T. Freeman, "On the optimality of solutions of the max-product belief propagation algorithm in arbitrary graphs," IEEE Trans. on Information Theory, vol. 47, no. 2, pp. 723-735, 2001. A. Farinelli, A. Rogers, A. Petcu, and N. R. Jennings, "Decentralised coordination of low-power embedded devices using the max-sum algorithm," in Proc. of AAMAS 2008, 2008, pp. 639-646. B. J. Frey and D. Dueck, "Clustering by passing messages between data points," Science, vol. 315, no. 5814, pp. 972-976, February 2007. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. The MIT Press, 2006. N. J. Johnson, S. Kotz, and N. Balakrishnan, Continuous univariate distributions, 2nd ed. Wiley, 1994, vol. 1. W. T. L. Teacy, J. Nie, S. McClean, G. Parr, S. Hailes, S. Julier, N. Trigoni, and S. Cameron, "Collaborative sensing by unmanned aerial vehicles," in Proceedings of ATSN'09, 2009, pp. 13-16.
PY - 2010/12/6
Y1 - 2010/12/6
N2 - In many applications, Unmanned Aerial Vehicles (UAVs) provide an indispensable platform for gathering information about the situation on the ground. However, to maximise information gained about the environment, such platforms require increased autonomy to coordinate the actions of multiple UAVs. This has led to the development of flight planning and coordination algorithms designed to maximise information gain during sensing missions. However, these have so far neglected the need to maintain wireless network connectivity. In this paper, we address this limitation by enhancing an existing multi-UAV planning algorithm with two new features that together make a significant contribution to the state-of-the-art: (1) we incorporate an on-line learning procedure that enables UAVs to adapt to the radio propagation characteristics of their environment, and (2) we integrate flight path and network routing decisions, so that modelling uncertainty and the affect of UAV position on network performance is taken into account.
AB - In many applications, Unmanned Aerial Vehicles (UAVs) provide an indispensable platform for gathering information about the situation on the ground. However, to maximise information gained about the environment, such platforms require increased autonomy to coordinate the actions of multiple UAVs. This has led to the development of flight planning and coordination algorithms designed to maximise information gain during sensing missions. However, these have so far neglected the need to maintain wireless network connectivity. In this paper, we address this limitation by enhancing an existing multi-UAV planning algorithm with two new features that together make a significant contribution to the state-of-the-art: (1) we incorporate an on-line learning procedure that enables UAVs to adapt to the radio propagation characteristics of their environment, and (2) we integrate flight path and network routing decisions, so that modelling uncertainty and the affect of UAV position on network performance is taken into account.
U2 - 10.1109/GLOCOMW.2010.5700246
DO - 10.1109/GLOCOMW.2010.5700246
M3 - Conference contribution
SP - 1771
EP - 1776
BT - Unknown Host Publication
ER -