Hostname: page-component-7c8c6479df-p566r Total loading time: 0 Render date: 2024-03-29T12:58:13.626Z Has data issue: false hasContentIssue false

Search for smart evaders with sweeping agents

Published online by Cambridge University Press:  20 April 2021

Roee M. Francos*
Affiliation:
Faculty of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel
Alfred M. Bruckstein
Affiliation:
Faculty of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel
*
*Corresponding author. Email: roee.francos@cs.technion.ac.il
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Suppose in a given planar circular region, there are smart mobile evaders and we want to find them using sweeping agents. We assume the sweeping agents are in a line formation whose total length is predetermined. We propose procedures for designing a sweeping process that ensures the successful completion of the task, thereby deriving conditions on the sweeping velocity of the linear formation and its path. Successful completion of the task means that evaders with a given limit on their velocity cannot escape the sweeping agents. We present results on the search time given the initial conditions.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

References

Tisue, S. and Wilensky, U., “Netlogo: A Simple Environment for Modeling Complexity,” Proceedings of the International Conference on Complex Systems, vol. 21 (2004) pp. 1621.Google Scholar
Stone, L. D., Royset, J. O. and Washburn, A. R., Optimal Search for Moving Targets, International Series in Operations Research & Management Science, vol. 237 (Springer, Cham, Switzerland, 2016).Google Scholar
Rekleitis, I., Lee-Shue, V., New, A. P. and Choset, H., “Limited Communication, Multi-Robot Team Based Coverage,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4 (2004) pp. 34623468.Google Scholar
Passino, K., Polycarpou, M., Jacques, D., Pachter, M., Liu, Y., Yang, Y., Flint, M. and Baum, M., “Cooperative Control for Autonomous Air Vehicles,” In: Cooperative Control and Optimization (Springer, Boston, MA, 2002) pp. 233271.CrossRefGoogle Scholar
Zhijun, T. and Ozguner, U., “On Non-escape Search for a Moving Target by Multiple Mobile Sensor Agents,” Proceedings of the IEEE American Control Conference (2006).CrossRefGoogle Scholar
Bertuccelli, L. F. and How, J. P., “Search for Dynamic Targets with Uncertain Probability Maps,” Proceedings of the IEEE American Control Conference (2006).10.1109/ACC.2006.1655444CrossRefGoogle Scholar
Chung, H., Polak, E., Royset, J. O. and Sastry, S., “On the optimal detection of an underwater intruder in a channel using unmanned underwater vehicles,” Naval Res. Logist. 58(8), 804820 (2011).CrossRefGoogle Scholar
Koopman, B. O., Search and Screening: General Principles with Historical Applications (Pergamon Press, New York, 1980).Google Scholar
Patrick, V. and Rubin, I., “A Framework and Analysis for Cooperative Search Using UAV Swarms,” Proceedings of the ACM Symposium on Applied Computing (2004).Google Scholar
Altshuler, Y., Yanovsky, V., Wagner, I. A. and Bruckstein, A. M., “Efficient cooperative search of smart targets using UAV swarms,” Robotica 26(4), 551557 (2008).CrossRefGoogle Scholar
Wagner, I. A. and Bruckstein, A. M., “Cooperative Cleaners: A Case of Distributed Ant-Robotics,” In: Communications, Computation, Control, and Signal Processing: A Tribute to Thomas Kailath (Kluwer Academic Publishers, The Netherlands, 1997) pp. 289308.CrossRefGoogle Scholar
Altshuler, Y., Bruckstein, A. M. and Wagner, I. A., “Swarm Robotics for a Dynamic Cleaning Problem,” Proceedings 2005 IEEE Swarm Intelligence Symposium (2005).Google Scholar
Altshuler, Y., Yanovsky, V., Wagner, I. A. and Bruckstein, A. M., “Multi-agent cooperative cleaning of expanding domains,” Int. J. Rob. Res. 30(8), 10371071 (2011).CrossRefGoogle Scholar
Bressan, A., “Differential inclusions and the control of forest fires,” J. Differ. Equations 243(2), 179207 (2007).CrossRefGoogle Scholar
Bressan, A., Burago, M., Friend, A. and Jou, J., “Blocking strategies for a fire control problem,” Anal. Appl. 6(03), 229246 (2008).10.1142/S0219530508001146CrossRefGoogle Scholar
Bressan, A. and De Lellis, C., “Existence of optimal strategies for a fire confinement problem,” Commun. Pure Appl. Math. J. Issued Courant Inst. Math. Sci. 62(6), 789830 (2009).10.1002/cpa.20271CrossRefGoogle Scholar
Bressan, A. and Wang, T., “On the optimal strategy for an isotropic blocking problem,” Calc. Var. Partial differ. Equations 45(1–2), 125145 (2012).CrossRefGoogle Scholar
Bressan, A., “Dynamic Blocking Problems for a Model of Fire PropagationIn: Advances in Applied Mathematics, Modeling, and Computational Science (Boston, MA, Springer, 2013) pp. 1140.CrossRefGoogle Scholar
Klein, R., Langetepe, E., Schwarzwald, B., Levcopoulos, C. and Lingas, A., “On a fire fighter’s problem,” Int. J. Found. Comput. Sci. 30(2), 231246 (2019).10.1142/S0129054119500023CrossRefGoogle Scholar
Brown, D. and Sun, L., “Dynamic exhaustive mobile target search using unmanned aerial vehicles,” IEEE Trans. AerospaceElectr. Syst. 55(6), 34133423 (2019).10.1109/TAES.2019.2907391CrossRefGoogle Scholar
McGee, T. G. and Hedrick, J. K., “Guaranteed Strategies to Search for Mobile Evaders in the Plane,” Proceedings of the IEEE American Control Conference (2006).CrossRefGoogle Scholar
Hew, P. C., “Linear and concentric arc patrols against smart evaders,” Mil. Oper. Res. 20(3), 3948 (2015).Google Scholar