Abstract
We define and study Euclidean and spatial network variants of a new path finding problem: given a set of safe or preferred zones with zero or low cost, find paths that minimize the cost of travel from an origin to a destination. In this problem, the entire space is passable, with preference given to safe or preferred zones. Existing algorithms for problems that involve unsafe regions to be avoided strictly are not effective for this new problem. To solve the Euclidean variant, we devise a transformation of the continuous data space with safe zones into a discrete graph upon which shortest path algorithms apply. A naive transformation yields a large graph that is expensive to search. In contrast, our transformation exploits properties of hyperbolas in Euclidean space to safely eliminate graph edges, thus improving performance without affecting correctness. To solve the spatial network variant, we propose a different graph-to-graph transformation that identifies critical points that serve the same purpose as do the hyperbolas, thus also avoiding the extraneous edges. Having solved the problem for safe zones with zero costs, we extend the transformations to the weighted version of the problem, where travel in preferred zones has nonzero costs. Experiments on both real and synthetic data show that our approaches outperform baseline approaches by more than an order of magnitude in graph construction time, storage space, and query response time.
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Notes
We do a best-first traversal on the spatial network, and hence, only a limited number of points are tested.
References
Abraham, I., Delling, D., Goldberg, A.V., Werneck, R.F.: A hub-based labeling algorithm for shortest paths in road networks. In: SEA, pp. 230–241 (2011)
Aljubayrin, S., Qi, J., Jensen, C.S., Zhang, R., He, Z., Wen, Z.: The safest path via safe zones. In: ICDE, pp. 531–542 (2015)
Bast, H., Delling, D., Goldberg, A., Müller-Hannemann, M., Pajor, T., Sanders, P., Wagner, D., Werneck, R.F.: Route planning in transportation networks. In: Kliemann, L., Sanders, P. (eds.) Algorithm Engineering, vol. 9220, pp. 19–80. Springer (2016)
Berg, J., Overmars, M.: Planning the shortest safe path amidst unpredictably moving obstacles. In: Algorithmic Foundation of Robotics VII, pp. 103–118 (2008)
Bortoff, S.A.: Path planning for UAVs. In: American Control Conference, pp. 364–368 (2000)
Dehn, E.: Algebraic equations: an introduction to the theories of Lagrange and Galois. Courier Corporation (2012)
Delling, D., Goldberg, A.V., Nowatzyk, A., Werneck, R.F.: PHAST: Hardware-accelerated shortest path trees. J. Paral. Distrib. Comput. 73(7), 940–952 (2013)
Efentakis, A., Pfoser, D.: ReHub: Extending hub labels for reverse k-nearest neighbor queries on large-scale networks. J. Exp. Alg. 21(1), 1–13 (2016)
Eunus Ali, M., Zhang, R., Tanin, E., Kulik, L.: A motion-aware approach to continuous retrieval of 3d objects. In: ICDE, pp. 843–852 (2008)
Robert, W.F.: Algorithm 97: shortest path. Commun. ACM 5(6), 345 (1962)
Geisberger, R., Sanders, P., Schultes, D., Delling, D.: Contraction hierarchies: faster and simpler hierarchical routing in road networks. In: SEA, pp. 319–333 (2008)
Gray, A., Abbena, E., Salamon, S.: Modern differential geometry of curves and surfaces with mathematica. Chapman and Hall/CRC, London (2006)
Hallam, C., Harrison, K.J., Ward, J.A.: A multiobjective optimal path algorithm. Dig. Signal Process. 11(2), 133–143 (2001)
Helgason, R.V., Kennington, J.L., Lewis, K.H.: Shortest path algorithms on grid graphs with applications to strike planning. Technical report, DTIC Document (1997)
Jagadish, H.V., Ooi, B.C., Tan, K.-L., Yu, C., Zhang, R.: iDistance: An adaptive B+-tree based indexing method for nearest neighbor search. TODS 30(2), 364–397 (2005)
Kala, R., Shukla, A., Tiwari, R.: Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning. Artif. Intell. Rev. 33(4), 307–327 (2010)
Koudas, N., Ooi, B.C., Tan, K.-L., Zhang, R.: Approximate NN queries on streams with guaranteed error/performance bounds. In: VLDB, pp. 804–815, (2004)
Lambert, A., Bouaziz, S., Reynaud, R.: Shortest safe path planning for vehicles. In: Intelligent Vehicles Symposium, pp. 282–286 (2003)
Lambert, A., Gruyer, D.: Safe path planning in an uncertain-configuration space. Robot. Autom. 3, 4185–4190 (2003)
Leenen, L., Terlunen, A., Le Roux, H.: A constraint programming solution for the military unit path finding problem. Mob. Intell. Auto. Syst. 9(1), 225–240 (2012)
Li, C., Gu, Y., Qi, J., Yu, G., Zhang, R., Deng, Q.: INSQ: an influential neighbor set based moving knn query processing system. In: ICDE, pp. 1338–1341 (2016)
Lu, Y., Shahabi, C.: An arc orienteering algorithm to find the most scenic path on a large-scale road network. In: SIGSPATIAL, pp. 46:1–46:10 (2015)
Mittal, S., Deb, K.: Three-dimensional offline path planning for UAVs using multiobjective evolutionary algorithms. Congr. Evolut. Comput. 7(1), 3195–3202 (2007)
Mora, A.M., Merelo, J.J., Millan, C., Torrecillas, J., Laredo, J.L.J., Castillo, P.A.: Enhancing a MOACO for solving the bi-criteria pathfinding problem for a military unit in a realistic battlefield. In: Applications of Evolutionary Computing, pp. 712–721 (2007)
Nutanong, S., Zhang, R., Tanin, E., Kulik, L.: The V*-diagram: a query-dependent approach to moving KNN queries. PVLDB 1(1), 1095–1106 (2008)
Samet, H.: The quadtree and related hierarchical data structures. ACM Comput. Surv. 16(2), 187–260 (1984)
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Aljubayrin, S., Qi, J., Jensen, C.S. et al. Finding lowest-cost paths in settings with safe and preferred zones. The VLDB Journal 26, 373–397 (2017). https://doi.org/10.1007/s00778-017-0455-8
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DOI: https://doi.org/10.1007/s00778-017-0455-8