Optimal multi-agent path planning for fast inverse modeling in UAV-based flood sensing applications

2014 International Conference on Unmanned Aircraft Systems, ICUAS 2014 - Conference Proceedings

Volumn , Issue , 2014, Pages 64-71

  Abdelkader, Mohamed ^a   Shaqura, Mohammad ^a   Ghommem, Mehdi ^a   Collier, Nathan ^a   Calo, Victor ^a   Claudel, Christian ^a  

^a KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

INVERSE PROBLEMS; MOTION PLANNING;

EMERGENCY RESPONSE; FUTURE RESEARCH DIRECTIONS; INVERSE MODELING; MODEL PARAMETER ESTIMATION; NATURAL DISASTERS; NUMERICAL SCHEME; SENSING APPLICATIONS; UNMANNED AIR VEHICLE (UAVS);

FLOODS;

EID: 84904554500     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICUAS.2014.6842239     Document Type: Conference Paper

References (25)

1. ISDR disaster statistics, http://www.unisdr.org/.
2. S. Adams, C. Friedland, and M. Levitan. Unmanned Aerial Vehicle Data Acquisition for Damage Assessment in Hurricane Events. In 8th International Workshop on Remote Sensing for Disaster Management, Tokyo, Japan, 2010.
3. E. Basha, S. Ravela, and D. Rus. Model-based monitoring for early warning flood detection. In Proceedings of the 6th ACM conference on Embedded network sensor systems, pages 295-308. ACM, 2008.
4. L. Li, Y. Hong, J. Wang, R. Adler, F. Policelli, S. Habib, D. Irwin, T. Korme, and L. Okello. Evaluation of the real-time TRMMbased multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria, Africa. Natural hazards, 50(1):109-123, 2009.
5. M. Abdelkader, M. Shaqura, C. Claudel, and W. Gueaieb. A UAV based system for real time flash flood monitoring in desert environments using Lagrangian microsensors. In ICUAS, 2013, pages 25-34, 2013.
6. E. Ackerman. Japan Earthquake: Global Hawk UAV May Be Able to Peek Inside Damaged Reactors. IEEE Spectrum, 17 March, 2011.
7. R. Alonso, M. Santillana, and C. Dawson. On the difussive wave approximation of the shallow water equations. J. Appl.Math., 19(5):575-606, 2008.
8. R. J. Alonso, M. Santillana, and C. Dawson. Analysis of the diffusive wave approximation of water equations. J. Appl.Math., 2007.
9. H.-L. Choi and J. P. How. A multi-uav targeting algorithm for ensemble forecast improvement. In AIAA Guidance, Navigation, and Control Conference, 2007.
10. N. Collier, H. Radwan, L. Dalcin, and V. M. Calo. Diffusive wave approximation to the shallow water equations: Computational approach. Procedia Computer Science, 4(0):1828-1833, 2011.
11. R. G. Ghanem and P. D. Spanos. Stochastic Finite Elements: A Spectral Approach. Dover Publications, Inc., 2003.
12. M. Ghommem, M. R. Hajj, C. L. Pettit, and P. S. Beran. Stochastic modeling of incident gust effects on aerodynamic lift. Journal of Aircraft, 47:1720-1727, 2010.
13. L. Gottschalk and R. Weingartner. Distribution of peak flow derived from a distribution of rainfall volume and runoff coefficient, and a unit hydrograph. Journal of Hydrology, 208(34):148-162, 1998.
14. S. Hosder, R. W. Walters, and P. Rafael. A non-intrusive polynomial chaos method for uncertainty propagation in cfd simulations. AIAA Paper 2006-891, AIAA, 2006. 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada.
15. J. P. How, N. Roy, H.-L. Choi, and S. Park. Learning covariance dynamics for path planning of uav sensors in a large-scale dynamic environment. 2013.
16. A. Krause, C. Guestrin, A. Gupta, and J. Kleinberg. Near-optimal sensor placements: Maximizing information while minimizing communication cost. In Proceedings of the 5th international conference on Information processing in sensor networks, pages 2-10. ACM, 2006.
17. A. Krause, A. Singh, and C. Guestrin. Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies. The Journal of Machine Learning Research, 9:235-284, 2008.
18. G. Muscato, E. Bonaccorso, L. Cantelli, D. Longo, and C. Melita. Volcanic environments: Robots for exploration and measurement. IEEE Robotics & Automation Magazine, 19(1):40-49, 2012.
19. E. Pereira, C. M. Kirsch, R. Sengupta, and J. B. de Sousa. Bigactors-a model for structure-aware computation. In 4th International Conference on Cyber-Physical Systems. ACM/IEEE, 2013.
20. C. L. Pettit, M. R. Hajj, and P. S. Beran. A stochastic approach for modeling incident gust effects on flow quantities. Probabilistic Engineering Mechanics, 25:153-162, 2010.
21. S. Ragi and E. K. Chong. Uav path planning in a dynamic environment via partially observable markov decision process. Aerospace and Electronic Systems, IEEE Transactions on, 49(4):2397-2412, 2013.
22. M. Reagan, H. N. Najm, R. G. Ghanem, and O. M. Knio. Uncertainty quantification in reacting flow simulations through non-intrusive spectral projection. Combustion and Flame, 132:545-555, 2003.
23. M. Santillana and C. Dawson. A numerical approach to study the properties of solutions of the diffusive wave approximation of the shallow water equations. Journal of Computational Geosciences, 2009.
24. A. Tinka, I. Strub, Q. Wu, and A. Bayen. Quadratic programming based data assimilation with passive drifting sensors for shallow water flows. International Journal of Control, 83(8):1686-1700, 2010.
25. D. Xiu and G. E. Karniadakis. Modeling uncertainty in flow simulations via generalized polynomial chaos. Journal of Computational Physics, 187:137-167, 2003.