Abstract
Planning collision-free paths for multiple robots traversing a shared space is a problem that grows combinatorially with the number of robots. The naive centralised approach soon becomes intractable for even a moderate number of robots. Decentralised approaches, such as priority planning, are much faster but lack completeness.
Previously I have demonstrated that the search can be significantly reduced by adding a level of abstraction [1]. I first partition the map into subgraphs of particular known structures, such as cliques, halls and rings, and then build abstract plans which describe the transitions of robots between the subgraphs. These plans are constrained by the structural properties of the subgraphs used. When an abstract plan is found, it can easy be resolved into a complete concrete plan without further search.
In this paper, I show how this method of planning can be implemented as a constraint satisfaction problem (CSP). Constraint propagation and intelligent search ordering further reduces the size of the search problem and allows us to solve large problems significantly more quickly, as I demonstrate this in a realistic planning problem based on a map of the Patrick Port Brisbane yard. This implementation also opens up opportunities for the application of a number of other search reduction and optimisation techniques, as I will discuss.
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References
Ryan, M.R.K.: Exploiting subgraph structure in multi-robot path planning. Journal of Artificial Intelligence Research 31, 497–542 (2008)
LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)
Gecode Team: Gecode: Generic constraint development environment (2006), http://www.gecode.org
Botea, A., Müller, M., Schaeffer, J.: Using abstraction for planning in sokoban. In: Schaeffer, J., Müller, M., Björnsson, Y. (eds.) CG 2002. LNCS, vol. 2883, pp. 360–375. Springer, Heidelberg (2003)
Junghanns, A., Schaeffer, J.: Sokoban: Enhancing general single-agent search methods using domain knowledge. Artificial Intelligence 129(1-2), 219–251 (2001)
van Beek, P., Chen, X.: CPlan: A constraint programming approach to planning. In: Proceedings of the AAAI National Conference, pp. 585–590 (1999)
Blum, A., Furst, M.: Fast planning through planning graph analysis. Artificial Intelligence 90(1-2), 281–300 (1997)
Do, M., Kambhampati, S.: Planning as constraint satisfaction: Solving the planning graph by compiling it into CSP. Artificial Intelligence 132(2), 151–182 (2001)
Lopez, A., Bacchus, F.: Generalizing GraphPlan by Formulating Planning as a CSP. In: Proceeding of the International Joint Conference on Artificial Intelligence, IJCAI 2003 (2003)
Kautz, H., Selman, B., Hoffmann, J.: SatPlan: Planning as Satisfiability. In: Abstracts of the 5th International Planning Competition (2006)
Mann, M., Tack, G., Will, S.: Decomposition during search for propagation-based constraint solvers. Technical Report arXiv:0712.2389v2, Cornell University Library (2007)
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Ryan, M. (2008). Constraint-Based Multi-agent Path Planning. In: Wobcke, W., Zhang, M. (eds) AI 2008: Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science(), vol 5360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89378-3_12
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DOI: https://doi.org/10.1007/978-3-540-89378-3_12
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