Abstract
Problem of finding 2D paths of special shape, e.g. paths comprised of line segments having the property that the angle between any two consecutive segments does not exceed the predefined threshold, is considered in the paper. This problem is harder to solve than the one when shortest paths of any shape are sought, since the planer’s search space is substantially bigger as multiple search nodes corresponding to the same location need to be considered. One way to reduce the search effort is to fix the length of the path’s segment and to prune the nodes that violate the imposed constraint. This leads to incompleteness and to the sensitivity of the’s performance to chosen parameter value. In this work we introduce a novel technique that reduces this sensitivity by automatically adjusting the length of the path’s segment on-the-fly, e.g. during the search. Embedding this technique into the known grid-based angle-constrained path finding algorithm LIAN, leads to notable increase of the planner’s effectiveness, e.g. success rate, while keeping efficiency, e.g. runtime, overhead at reasonable level. Experimental evaluation shows that LIAN with the suggested enhancements, dubbed eLIAN, solves up to 20% of tasks more compared to the predecessor. Meanwhile, the solution quality of eLIAN is nearly the same as the one of LIAN.
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References
Botea, A., Müller, M., Schaeffer, J.: Near optimal hierarchical path-finding. J. Game Dev. 1(1), 7–28 (2004)
Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)
Daniel, K., Nash, A., Koenig, S., Felner, A.: Theta*: any-angle path planning on grids. J. Artif. Intell. Res. 39, 533–579 (2010)
Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)
Harabor, D., Grastien, A., Oz, D., Aksakalli, V.: Optimal any-angle pathfinding in practice. J. Artif. Intell. Res. 56, 89–118 (2016)
Harabor, D.D., Grastien, A.: Online graph pruning for pathfinding on grid maps. In: Proceedings of The 25th AAAI Conference on Artificial Intelligence (AAAI-2011), pp. 1114–1119 (2011)
Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)
Kim, H., Kim, D., Shin, J.U., Kim, H., Myung, H.: Angular rate-constrained path planning algorithm for unmanned surface vehicles. Ocean Eng. 84, 37–44 (2014)
Nash, A., Koenig, S., Tovey, C.: Lazy theta*: any-angle path planning and path length analysis in 3D. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI-2010), pp. 147–154. AAAI Press (2010)
Silver, D.: Cooperative pathfinding. In: Proceedings of The 1st Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-2005), pp. 117–122 (2005)
Sturtevant, N.R.: Benchmarks for grid-based pathfinding. IEEE Trans. Comput. Intell. AI Games 4(2), 144–148 (2012)
Thrun, S.: Learning occupancy grid maps with forward sensor models. Auton. Robots 15(2), 111–127 (2003)
Xu, H., Shu, L., Huang, M.: Planning paths with fewer turns on grid maps. In: Proceedings of The 6th Annual Symposium on Combinatorial Search (SoCS-2013), pp. 193–201 (2013)
Yakovlev, K., Baskin, E., Hramoin, I.: Grid-based angle-constrained path planning. In: Hölldobler, S., Krötzsch, M., Peñaloza, R., Rudolph, S. (eds.) KI 2015. LNCS (LNAI), vol. 9324, pp. 208–221. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24489-1_16
Yap, P.: Grid-based path-finding. In: Cohen, R., Spencer, B. (eds.) AI 2002. LNCS (LNAI), vol. 2338, pp. 44–55. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47922-8_4
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The work was partially supported by the “RUDN University Program 5–100” and by the special program of the presidium of Russian Academy of Sciences.
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Andreychuk, A., Soboleva, N., Yakovlev, K. (2018). eLIAN: Enhanced Algorithm for Angle-Constrained Path Finding. In: Kuznetsov, S., Osipov, G., Stefanuk, V. (eds) Artificial Intelligence. RCAI 2018. Communications in Computer and Information Science, vol 934. Springer, Cham. https://doi.org/10.1007/978-3-030-00617-4_19
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