Abstract
Pathfinding on large maps is time-consuming. Classical search algorithms such as Dijkstra’s and A* algorithms may solve difficult problems in polynomial time. However, in real-world pathfinding examples where the search space increases dramatically, these algorithms are not appropriate. Hierarchical pathfinding algorithms that provide abstract plans of future routing, such as HPA* and PRA*, have been explored by previous researchers based on classical ones. Although the two hierarchical algorithms show improvement in efficiency, they only obtain near optimal solutions. In this paper, we introduce the Hierarchical Shortest Path algorithm (HSP) and a hybrid of the HSP and A* (HSPA*) algorithms, which find optimal solutions in logarithmic time for numerous examples. Our empirical study shows that HSP and HSPA* are superior to the classical algorithms on realistic examples, and our experimental results illustrate the efficiency of the two algorithms. We also demonstrate their applicability by providing an overview of our Route Planner project that applies the two algorithms proposed in this paper.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Minsky, M.: Steps toward artificial intelligence (1961)
Russell, S., Norvig, P.: Solving problems by searching. Artificial Intelligence: A Modern Approach (1995)
Holte, R., Mkadmi, T., Zimmer, R.M., MacDonald, A.J.: Speeding up problem solving by abstraction: A graph oriented approach. Artifical Intelligence 85(1-2), 321–361 (1996)
Rabin, S.: A* aesthetic optimizations. In: Game Programming Gems, pp. 264–271 (2000)
Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. on Systems Science and Cybern. 4, 100–107 (1968)
Korf, R.: Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence 27(1), 97–109 (1985)
Korf, R., Reid, M., Edelkamp, S.: Time complexity of iterative deepening-A*. Artificial Intelligence, 199–218 (2001)
Sturtevant, N., Buro, M.: Partial pathfinding using map abstraction and refinement. In: AAAI-05, pp. 1392–1397 (2005)
Botea, A., Müller, M., Schaeffer, J.: Near optimal hierarchical path-finding. J. of Game Develop., 7–28 (2004)
Kautz, H., Fox, D., Etzioni, O., Borriello, G., Arnstein, L.: An overview of the assisted cognition project. American Association for Artificial Intelligence, Menlo Park (2002)
Moffatt, K., McGrenere, J., Purves, B., Klawe, M.: The partricipatory design of a sound and image enhanced daily planner for people with aphasia. In: Proceedings of ACM CHI, pp. 501–510 (2005)
McGrenere, J., Davies, R., Findlater, L., Graf, P., Klawe, M., Moffatt, K., Purves, B., Yang, S.: Insights from the aphasia project. In: Proceedings of ACM Conference on Universal Usability, pp. 112–118 (2003)
Wheelchair Foundation (2002), http://wheelchairfoundation.ca/
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Yang, S., Mackworth, A.K. (2007). Hierarchical Shortest Pathfinding Applied to Route-Planning for Wheelchair Users. In: Kobti, Z., Wu, D. (eds) Advances in Artificial Intelligence. Canadian AI 2007. Lecture Notes in Computer Science(), vol 4509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72665-4_46
Download citation
DOI: https://doi.org/10.1007/978-3-540-72665-4_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72664-7
Online ISBN: 978-3-540-72665-4
eBook Packages: Computer ScienceComputer Science (R0)