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Scheduling an autonomous robot searching for hidden targets

  • T. C. E. Cheng
  • B. Kriheli
  • E. LevnerEmail author
  • C. T. Ng
S.I.: CoDIT2017-Combinatorial Optimization
  • 13 Downloads

Abstract

The problem of searching for hidden or missing objects (called targets) by autonomous intelligent robots in an unknown environment arises in many applications, e.g., searching for and rescuing lost people after disasters in high-rise buildings, searching for fire sources and hazardous materials, etc. Until the target is found, it may cause loss or damage whose extent depends on the location of the target and the search duration. The problem is to efficiently schedule the robot’s moves so as to detect the target as soon as possible. The autonomous mobile robot has no operator on board, as it is guided and totally controlled by on-board sensors and computer programs. We construct a mathematical model for the search process in an uncertain environment and provide a new fast algorithm for scheduling the activities of the robot which is used before an emergency evacuation of people after a disaster.

Keywords

Intelligent robot Search-and-rescue Emergency evacuation Scheduling algorithm 

Notes

Acknowledgements

The authors wish to thank the Editor and anonymous reviewers for their very useful comments and suggestions. This research was supported in part by the Research Grants Council of Hong Kong under grant no. PolyU 152148/15E.

References

  1. Benkoski, S. J., Monticino, M. G., & Weisinger, J. R. (1991). A survey of the search theory literature. Naval Research Logistics, 38, 469–494.CrossRefGoogle Scholar
  2. Casper, J., & Murphy, R. R. (2003). Human-robot interactions during the robot-assisted urban search and rescue response at the world trade center. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 33(3), 367–385.CrossRefGoogle Scholar
  3. Chung, T. H., & Burdick, J. W. (2012). Analysis of search decision making using probabilistic search. IEEE Transactions on Robotics, 28, 132–144.CrossRefGoogle Scholar
  4. De Groot, M. H. (1970). Optimal statistical decisions. New York: McGraw-Hill.Google Scholar
  5. De Groot, M. H. (1978). Probability and statistics (pp. 258–259). Boston: Addison-Wesley.Google Scholar
  6. Dell, R. F., Eagle, J. N., Martins, G. H. A., & Santos, A. G. (1996). Using multiple searchers in constrained-path, moving-target search problems. Naval Research Logistics, 43, 463–480.CrossRefGoogle Scholar
  7. Hsu, H.-H., Chang, J.-K., Peng, W.-J., Shih, T. K., Pai, T. W., & Man, K. L. (2018). Indoor localization and navigation using smartphone sensory data. Annals of Operations Research, 265(2), 187–204.CrossRefGoogle Scholar
  8. Kantor, G., Singh, S., Peterson, R., Rus, D., Das, A., Kumar, V., et al. (2006). Distributed search and rescue with robot and sensor teams. In S. Yuta, H. Asama, E. Prassler, T. Tsubouchi, & S. Thrun (Eds.), Field and service robotics (pp. 529–538). Berlin: Springer.CrossRefGoogle Scholar
  9. Kress, M., Lin, K. Y., & Szechtman, R. (2008). Optimal discrete search with imperfect specificity. Mathematical Methods of Operations Research, 68, 539–549.CrossRefGoogle Scholar
  10. Kriheli, B., Levner, E., & Spivak, A. (2016). Optimal search for hidden targets by unmanned aerial vehicles under imperfect inspections. American Journal of Operations Research, 6(2), 53–66.CrossRefGoogle Scholar
  11. Levner, E. (1994). Infinite-horizon scheduling algorithms for optimal search for hidden objects. International Transactions on Operational Research, 1, 241–250.CrossRefGoogle Scholar
  12. Murphy, R. R., Tadokoro, S., Nardi, D., Jacoff, A., Fiorini, P., Choset, H., et al. (2008). Search and rescue robotics. In B. Siciliano & O. Khatib (Eds.), Springer handbook of robotics (pp. 1151–1173). Berlin: Springer.CrossRefGoogle Scholar
  13. Ninh, A., & Pham, M. (2018). Logconcavity, twice-logconcavity and Turán-type inequalities. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2923-y.Google Scholar
  14. Rabinowitz, G., & Emmons, H. (1997). Optimal and heuristic inspection schedules for multistage production systems. IIE Transactions, 29(12), 1063–1071.Google Scholar
  15. Rushton, A., Oxley, J., & Croucher, P. (2000). The handbook of logistics and distribution management (pp. 107–108). London: Kogan Page.Google Scholar
  16. Stone, L. D. (1989). Theory of optimal search. New York: Academic Press.Google Scholar
  17. Tripathi, R. C. (2006). Negative binomial distribution. Encyclopedia of statistical sciences. New York: Wiley.Google Scholar
  18. Trummel, K. E., & Weisinger, J. R. (1986). The complexity of the optimal searcher path problem. Operations Research, 34, 324–327.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Logistics and Maritime StudiesThe Hong Kong Polytechnic UniversityHong Kong SARChina
  2. 2.Department of EconomicsAshkelon Academic CollegeAshkelonIsrael
  3. 3.Department of Computer ScienceHolon Institute of TechnologyHolonIsrael

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