Stochastic strategies for autonomous robotic surveillance

Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05

Volumn 2005, Issue , 2005, Pages 2200-2205

  Grace, Jeremy ^a   Baillieul, John ^a  

^a BOSTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL STRUCTURE; STEADY-STATE DISTRIBUTIONS; STOCHASTIC TECHNIQUES;

COMPUTATIONAL COMPLEXITY; COMPUTER SIMULATION; GRAPH THEORY; MOBILE ROBOTS; MONTE CARLO METHODS; MULTI AGENT SYSTEMS; PARAMETER ESTIMATION; PROBLEM SOLVING;

RANDOM PROCESSES;

EID: 33847197954     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CDC.2005.1582488     Document Type: Conference Paper

References (21)

1. Chvatal, V. A Combinatorial Theorem in Plane Geometry. Journal of Combinatorial Theory (B) 18, 39-41, 1975.
2. O'Rourke, J. Art Gallery Theorems and Algorithms. Oxford University Press, 1987.
3. Chin, W. and Ntafos, S. Optimum Watchman Routes. Symposium on Computational Geometry, 24-33, 1987.
4. Chin, W. and Ntafos, S. Shortest Watchman Routes in Simple Polygons. Discrete and Computational Geometry6: 9-31, 1991.
5. Carlsson, S., Nilsson, B. and Ntafos, S. Optimum Guard Covers and m-Watchmen Routes for Restricted Polygons. International Journal of Computational Geometry and Applications3(1):85-105, 1993.
6. Carlsson, S., Jonsson, H. and Nilsson, B. Finding the Shortest Watchman Route in a Simple Polygon. ISAAC 58-67, 1993.
7. Yamauchi, B. A Frontier-Based Approach for Autonomous Exploration. IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), 1997.
8. Simmons, R., Apfelbaum, D., Burgard, W, Fox, D., Moors, M., Thrun, S.M. and Younes, H. Coordination for Multi-Robot Exploration and Mapping. Proceedings of 17th National Conference on Artificial Intelligence 852-858, 2000.
9. Burgard, W. Moors, M., Fox, D., Reid S., and Thrun, S. Collaborative Multi-Robot Exploration. IEEE International Conference on Artificial Intelligence 852-858, 2000.
10. Acar, E., and Choset, H. Robust Sensor-based Coverage of Unstructured Environments. IEEE International Conference on Robotics and Automation (ICRA), 2000.
11. Yang, Y., Minai, A., and Polycarpou, M. Analysis of Opportunity Method for Cooperative Search by Mobile Agents, IEEE Conference on Decision and Control, 2002.
12. Flint, M., Polycarpou, M. and Fernandez-Gaucherand, E. Cooperative Control for Multiple Autonomous UAV's Searching for Targets. IEEE Conference on Decision and Control, 2002.
13. Hespanha, J., Kim, H., Sastry, S. Multiple-Agent Probabilistic Pursuit-Evasion Games. IEEE CDC3:2432-2437, 1999.
14. Kim, H., Vidal,R., Shim, D., Shakeria, O. and Sastry, S. A Hierarchical Approach to Probabilistic Pursuit-Evasion Games with Unmanned Ground and Aerial Vehicles. IEEE CDC, 2001.
15. Kemeny, J. and Snell, J. Finite Markov Chains. Springer-Verlag, 1976.
16. Feller, W. An Introduction to Probability Theory and Its Applications. Wiley, 1968.
17. Rosenthal, J. Convergence Rates of Markov Chains. SIAM Review37:387-405, 1994.
18. Diaconis, P. and Stroock, D. Geometric Bounds for Eigenvalues of Markov Chains. Annals of Applied Probability1-36-61, 1991.
19. Boyd, S., Diaconis, P. and Xiao, L. Fastest Mixing Markov Chain on a Graph. SIAM Review, accepted Mar 2004.
20. Berge, C., Graphs and Hypergraphs. North Holland, 1970.
21. Shannon, C, The Mathematical Theory of Communication, University of Illinois, 1963.