Abstract
The computation of optimal route for various kinds of automatic or manned vehicles on hilly terrain is an important task in route planning applications. In the absence of a road network, the topography factors of the terrain (slope, elevation, etc.) and the climbing angle of the vehicle play an important role in the computation of the optimal route between two points. This kind of problem has been addressed in the artificial intelligence domain, and the graph search algorithms can be applied to find a solution. The A* algorithm and various versions of A* have been reported in the literature to achieve the faster results while maintaining the optimality criteria of the solution. The speed-up in all the versions of A* algorithms is achieved through the use of heuristic knowledge available in the problem domain. There may exist various heuristic functions that fulfil the admissibility criteria but produce different results as far as the speed and the optimality are concerned. This paper presents a performance measure for quantitative comparison of the various heuristics functions that can be used for optimal route selection in hilly terrain.
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References
Khantanapoka K, Chinnasarn K (2009) Pathfinding of 2D & 3D game real-time strategy with depth direction A* algorithm for multi-layer. In: Eight IEEE international symposium on natural language processing, pp 184–188
Dijkstra W (1959) A note on two problems in connection with graphs. Numer Math 1(1):269–271
Nilsson JN (1995) Principles of artificial intelligence. Narosa Publishing house, New Delhi
Wouter VT, Geraerts R (2015) Dynamically pruned A* for re-planning in navigation meshes. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), Congress Center Hamburg, Germany, pp 2051–2057, 28 Sept–02 Oct
Ma H, Liang R (2013) Using bidirectional search to compute optimal shortest paths over multi-weight graphs. In: IEEE international conference on information science and cloud computing companion, pp 66–71
Phillips M, Likhachev M (2015) Speeding up heuristic computation in planning with experience graphs. In: IEEE international conference on robotics and automation (ICRA), Washington State Convention Center, Seattle, Washington, pp 893–899, 26–30 May
Bander JL, White CC III (1998) A heuristic search algorithm for path determination with learning. IEEE Trans Syst Man Cybern Part A: Syst Hum 28(1)
Lingkun W, Xiaokui X, Dingxiong D (2012) Shortest path and distance queries on road networks: an experimental evaluation. Proc VLDB Endow 5(5):406–417
Geisberger R, Sanders P, Schultes D (2008) Contraction hierarchies: faster and simpler hierarchical routing in road networks. In: International workshop on WEA, Springer, Heidelberg, pp 319–333
Abraham I, Fiat A, Goldberg AV et al (2010) Highway dimension, shortest paths, and provably efficient algorithms. In: Proceedings of the twenty first annual ACM-SIA Symposium on discrete algorithms, Society for Industrial and Applied Mathematics, pp 782–793
Bast H, Funke S, Matijecvic D (2006) Transit: ultrafast shortest path queries with linear time pre-processing. In Important proceedings of the 9th DIMACS implementation challenge, pp 175–192
Pearl J (1984) Heuristics: intelligent strategies for computer problem solving. Addison-Wesley, MA
http://asterweb.jpl.nasa.gov. Accessed 12 Nov 2018
Acknowledgements
This work was done at Image Analysis Center (IAC), Defense Electronics Applications Laboratory (DEAL), Dehradun, India. The author is grateful to Dr. RS Pundir, Director, DEAL, for providing all his support and resources to complete this work.
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Porwal, S., Khare, S. (2020). A Quantitative Comparator of Heuristic Methods for Optimal Route in Hilly Terrain. In: Sastry, P.S., CV, J., Raghavamurthy, D., Rao, S.S. (eds) Advances in Small Satellite Technologies. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-1724-2_3
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DOI: https://doi.org/10.1007/978-981-15-1724-2_3
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