Abstract
In the classical search problem on the line or in higher dimension one is asked to find the shortest (and often the fastest) route to be adopted by a robot R from the starting point s towards the target point t located at unknown location and distance D. It is usually assumed that robot R moves with a fixed unit speed 1. It is well known that one can adopt a “zig-zag” strategy based on the exponential expansion, which allows to reach the target located on the line in time \(\le 9D,\) and this bound is tight. The problem was also studied in two dimensions where the competitive factor is known to be O(D).
In this paper we study an alteration of the search problem in which robot R starts moving with the initial speed 1. However, during search it can encounter a point or a sequence of points enabling faster and faster movement. The main goal is to adopt the route which allows R to reach the target t as quickly as possible. We study two variants of the considered search problem: (1) with the global knowledge and (2) with the local knowledge. In variant (1) robot R knows a priori the location of all intermediate points as well as their expulsion speeds. In this variant we study the complexity of computing optimal search trajectories. In variant (2) the relevant information about points in P is acquired by R gradually, i.e., while moving along the adopted trajectory. Here the focus is on the competitive factor of the solution, i.e., the ratio between the solutions computed in variants (2) and (1). We also consider two types of search spaces with points distributed on the line and subsequently with points distributed in two-dimensional space.
This work was initiated while the first author visited Kyushu University. The work is partly supported by JST PRESTO Grant Number JPMJPR16E4 and Networks Sciences and Technologies (NeST) EEECS School initiative, University of Liverpool.
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References
Asano, T., Bereg, S., Kirkpatrick, D.: Finding nearest larger neighbors. In: Albers, S., Alt, H., Näher, S. (eds.) Efficient Algorithms. LNCS, vol. 5760, pp. 249–260. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03456-5_17
Baeza-Yates, R.A., Culberson, J.C., Rawlins, G.J.E.: Searching with uncertainty extended abstract. In: Karlsson, R., Lingas, A. (eds.) SWAT 1988. LNCS, vol. 318, pp. 176–189. Springer, Heidelberg (1988). https://doi.org/10.1007/3-540-19487-8_20
Baeza-Yates, R.A., Culberson, J.C., Rawlins, G.J.E.: Searching in the plane. Inf. Comput. 106(2), 234–252 (1993)
Baeza-Yates, R.A., Schott, R.: Parallel searching in the plane. Comput. Geom. Theory Appl. 5(3), 143–154 (1995)
Bampas, E., et al.: Linear search by a pair of distinct-speed robots. In: Suomela, J. (ed.) SIROCCO 2016. LNCS, vol. 9988, pp. 195–211. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48314-6_13
Bellman, R.: Minimization problem. Bull. AMS 62(3), 270 (1956)
Bender, M.A., Fernández, A., Ron, D., Sahai, A., Vadhan, S.P.: The power of a pebble: exploring and mapping directed graphs. In: STOC 1998, pp. 269–278 (1998)
Bose, P., De Carufel, J.-L., Durocher, S.: Revisiting the problem of searching on a line. In: Bodlaender, H.L., Italiano, G.F. (eds.) ESA 2013. LNCS, vol. 8125, pp. 205–216. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40450-4_18
Chrobak, M., Gąsieniec, L., Gorry, T., Martin, R.: Group search on the line. In: Italiano, G.F., Margaria-Steffen, T., Pokorný, J., Quisquater, J.-J., Wattenhofer, R. (eds.) SOFSEM 2015. LNCS, vol. 8939, pp. 164–176. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46078-8_14
Czyzowicz, J., Gąsieniec, L., Georgiou, K., Kranakis, E., MacQuarrie, F.: The beachcombers’ problem: walking and searching with mobile robots. Theor. Comput. Sci. 608, 201–218 (2015)
Czyzowicz, J., Gąsieniec, L., Kosowski, A., Kranakis, E.: Boundary patrolling by mobile agents with distinct maximal speeds. In: Demetrescu, C., Halldórsson, M.M. (eds.) ESA 2011. LNCS, vol. 6942, pp. 701–712. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23719-5_59
Devillers, O.: Improved incremental randomized Delaunay triangulation. In: Symposium on Computational Geometry, pp. 106–115 (1998)
Fortune, S.: A sweepline algorithm for Voronoi diagrams. Algorithmica 2, 153–174 (1987)
Ghosh, S.K., Klein, R.: Online algorithms for searching and exploration in the plane. Comput. Sci. Rev. 4(4), 189–201 (2010)
Karavelas, M.I., Yvinec, M.: Dynamic additively weighted Voronoi diagrams in 2D. In: Möhring, R., Raman, R. (eds.) ESA 2002. LNCS, vol. 2461, pp. 586–598. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45749-6_52
Hammar, M., Nilsson, B.J., Schuierer, S.: Parallel searching on \(m\) rays. Comput. Geom. 18(3), 125–139 (2001)
Jeż, A., Łopuszański, J.: On the two-dimensional cow search problem. Inf. Process. Lett. 131(11), 543–547 (2009)
Kao, M.Y., Reif, J.H., Tate, S.R.: Searching in an unknown environment: an optimal randomized algorithm for the cow-path problem. Inf. Comput. 109(1), 63–79 (1996)
Kawamura, A., Kobayashi, Y.: Fence patrolling by mobile agents with distinct speeds. Distrib. Comput. 28(2), 147–154 (2015)
Kirkpatrick, D.G.: Optimal search in planar subdivisions. SIAM J. Comput. 12(1), 28–35 (1983)
Koutsoupias, E., Papadimitriou, C., Yannakakis, M.: Searching a fixed graph. In: Meyer, F., Monien, B. (eds.) ICALP 1996. LNCS, vol. 1099, pp. 280–289. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61440-0_135
Li, H., Chong, K.P.: Search on lines and graphs. In: Proceedings of 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference (CDC/CCC 2009), vol. 109, no. 11, pp. 5780–5785 (2009)
Shortt, D.: Gravity assist, 27 September 2013. www.planetary.org
Temple, T., Frazzoli, E.: Whittle-indexability of the cow path problem. In: American Control Conference (ACC), pp. 4152–4158 (2010)
Acknowledgements
The authors would like to thank Jurek Czyzowicz for early discussions on the studied problem and the anonymous reviewers for a number of corrections and suggestions which helped us to improve the presentation.
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Gąsieniec, L., Kijima, S., Min, J. (2018). Searching with Increasing Speeds. In: Izumi, T., Kuznetsov, P. (eds) Stabilization, Safety, and Security of Distributed Systems. SSS 2018. Lecture Notes in Computer Science(), vol 11201. Springer, Cham. https://doi.org/10.1007/978-3-030-03232-6_9
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