ACM Home Page
Please provide us with feedback. Feedback
Simulation-aided path planning of UAV
Full text PdfPdf (503 KB)
Source Winter Simulation Conference archive
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come table of contents
Washington D.C.
SESSION: Military applications: UAV simulation table of contents
Pages 1306-1314  
Year of Publication: 2007
ISBN:1-4244-1306-0
Authors
Farzad Kamrani  School of Information and Communication Technology, Stockholm, SE, Sweden
Rassul Ayani  School of Information and Communication Technology, Stockholm, SE, Sweden
Sponsors
INFORMS-SIM : Institute for Operations Research and the Management Sciences: Simulation Society
NIST : National Institute of Standards and Technology
(SCS) : The Society for Modeling and Simulation International
ACM/SIGSIM : Association for Computing Machinery: Special Interest Group on Simulation
IIE : Institute of Industrial Engineers
ASA : American Statistical Association
IEEE/SMC : Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society
Publisher
IEEE Press  Piscataway, NJ, USA
Bibliometrics
Downloads (6 Weeks): 6,   Downloads (12 Months): 59,   Citation Count: 0
Additional Information:

abstract   references   collaborative colleagues  

Tools and Actions: Review this Article  

ABSTRACT

The problem of path planning for Unmanned Aerial Vehicles (UAV) with a tracking mission, when some a priori information about the targets and the environment is available can in some cases be addressed using simulation. Sequential Monte Carlo Simulation can be used to assess the state of the system and target when the UAV reaches the area of responsibility and during the tracking task. This assessment of the future is then used to compare the impact of choosing different alternative paths on the expected value of the detection time. A path with a lower expected value of detection time is preferred. In this paper the details of this method is described. Simulations are performed by a special purpose simulation tool to show the feasibility of this method and compare it with an exhaustive search.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Ahlberg, S., P. Hörling, K. Jöred, C. Mårtenson, G. Neider, J. Schubert, H. Sidenbladh, P. Svenson, P. Svensson, K. Undén, and J. Walter. 2004, Jun. The IFD03 information fusion demonstrator. In Proceedings of the Seventh International Conference on Information Fusion, Volume II, 936--943. Mountain View, CA.
 
2
Arulampalam, S., S. Maskell, N. Gordon, and T. Clapp. 2002, February. A tutorial on particle filters for online non-linear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing 50 (2): 174--188.
 
3
Doucet, A., N. de Freitas, and N. Gordon. 2001. Sequential monte carlo methods in practice. Springer Verlag.
 
4
Gordon, N. J., D. J. Salmond, and A. F. M. Smith. 1993, April. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F 140 (2): 107--113.
 
5
Kamrani, F., M. Garcia Lozano, and R. Ayani. 2006, October 23--25, Path planning for UAVs using symbiotic simulation. In Proceedings of the 20th annual European Simulation and Modelling Conference, ESM'2006, 215--238. Toulouse, France.
 
6
Ristic, B., S. Arulampalam, and N. Gordon. 2004. Beyond the Kalman filter: Particle filters for tracking applications. Artech House Radar Library. Artech House Series Publishers.
 
7
Svenson, P., and C. Mårtenson. 2006, May 16--18, SB-Plan: Simulation-based support for resource allocation and mission planning. In Proceedings of the Conference on Civil and Military Readiness (CIMI 2006). Enköping, Sweden.
 
8
Weisstein, E. W. 2000. Topological sort, From Mathworld--A Wolfram web resource. Available via <mathworld.wolfram.com/TopologicalSort.html> {accessed March 29, 2007}.
 
9
Weisstein, E. W. 2003. Acyclic digraph, From Mathworld---A Wolfram web resource. Available via <mathworld.wolfram.com/AcyclicDigraph.html> {accessed March 29, 2007}.
 
10
Xiong, N., and P. Svensson. 2002. Multi-sensor management for information fusion: issues and approaches. Information Fusion 3 (2): 163--186.
Collaborative Colleagues:
Farzad Kamrani: colleagues
Rassul Ayani: colleagues