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Dynamic Clustering of Maps in Autonomous Agents
November 1996 (vol. 18 no. 11)
pp. 1080-1091

Abstract—The problem of organizing and exploiting spatial knowledge for navigation is an important issue in the field of autonomous mobile systems. In particular, partitioning the environment map into connected clusters allows for significant topological features to be captured and enables decomposition of path-planning tasks through a divide-and-conquer policy. Clustering by discovery is a procedure for identifying clusters in a map being learned by exploration as the agent moves within the environment, and yields a valid clustering of the available knowledge at each exploration step. In this work, we define a fitness measure for clustering and propose two incremental heuristic algorithms to maximize it. Both algorithms determine clusters dynamically according to a set of topological and metric criteria. The first one is aimed at locally minimizing a measure of "scattering" of the entities belonging to clusters, and partially rearranges the existing clusters at each exploration step. The second estimates the positions and dimensions of clusters according to a global map of density. The two algorithms are compared in terms of optimality, efficiency, robustness, and stability.

[1] O. Causse and J.L. Crowley, "Navigation with Constraints for an Autonomous Mobile Robot," Robotics and Autonomous Systems, vol. 12, pp. 213-221, 1994.
[2] B.B. Chaudhuri, "Dynamic Clustering for Time Incremental Data," Pattern Recognition Letters, vol. 15, pp. 27-34, 1994.
[3] H.I. Christensen, N.O. Kirkeby, S. Kristensen, L. Knudsen, and E. Granum, "Model-Driven Vision for In-Door Navigation," Robotics and Autonomous Systems, vol. 12, pp. 199-207, 1994.
[4] P. Ciaccia and B. Montanari, "Reinforcement-Based Systems for Solving Navigational Tasks in Large Domains," Proc. Int'l Workshop Mechatronical Computer Systems for Perception and Action, pp. 367-374,Halmstad, Sweden, 1993.
[5] L. Davis and M. Steenstrup, "Genetic Algorithms and Simulated Annealing: An Overview," Genetic Algorithms and Simulated Annealing, L. Davis, ed., pp. 1-11.Los Altos, Calif.: Morgan Kaufmann, 1987.
[6] R. Duda and P. Hart, Pattern Classification and Scene Analysis. John Wiley&Sons, 1973.
[7] A. Elfes, "Using Occupancy Grids for Mobile Robot Perception and Navigation," Computer, vol. 22, no. 6, pp. 46-57, 1989.
[8] B. Faverjon and P. Tournassoud, "The Mixed Approach for Motion Planning: Learning Global Strategies from a Local Planner," Proc. Int'l Joint Conf. Artificial Intelligence, vol. 2, pp. 1,131-1,137, 1987.
[9] D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass.: Addison-Wesley, 1989.
[10] J.A Hartigan,Clustering Algorithms, John Wiley and Sons, New York, N.Y., 1975.
[11] P.D. Holmes and E.R. Jungert, “Symbolic and Geometric Connectivity Graph Methods for Route Planning in Digitized Maps,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 5, pp. 549-565, 1992.
[12] A.K. Jain and R.C. Dubes, Algorithms for Clustering Data. Englewood Cliffs, N.J.: Prentice Hall, 1988.
[13] S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, "Optimization by Simulated Annealing," Science, no. 220, pp. 671-680, 1983.
[14] B.J. Kuipers and Y.T. Byun, "A Robust, Qualitative Method for Robot Spatial Learning," Proc. Nat'l Conf. Artificial Intelligence (AAAI88), vol. 2, pp. 774-779,St. Paul, Minn., 1988.
[15] D. Maio and S. Rizzi, "Clustering by Discovery on Maps," Pattern Recognition Letters, vol. 13, no. 2, pp. 89-94, 1992.
[16] D. Maio and S. Rizzi, "Map Learning and Clustering in Autonomous Systems," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 12, pp. 1,286-1,297, Dec. 1993.
[17] D. Maio and S. Rizzi, "Knowledge Architecture for Environment Representation in Autonomous Agents," Proc. Eighth Int'l Symp. Computer and Information Sciences, pp. 4-11,Istanbul, 1993.
[18] D. Maio and S. Rizzi, "A Hybrid Approach to Path Planning in Autonomous Agents," Proc. Second Int'l Conf. Expert Systems for Development, pp. 222-227,Bangkok, 1994.
[19] D. Maio, D. Maltoni, and S. Rizzi, "Topological Clustering of Maps Using a Genetic Algorithm," Pattern Recognition Letters, vol. 16, no. 1, pp. 89-96, 1995.
[20] W.A. Phillips, P.J.B. Hancock, N.J. Willson, and L.S. Smith, "On the Acquisition of Object Concepts from Sensory Data," Neural Computers, NATO ASI Series, vol. F41, R. Eckmiller and Ch.v.d. Malsburg, eds., pp. 159-168.Springer-Verlag, 1988.
[21] K. Rose, E. Gurewitz, and G.C. Fox, A Deterministic Annealing Approach to Clustering Pattern Recognition Letters, vol. 11, no. 9, pp. 589-594, 1990.
[22] S.P. Singh, "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks," Machine Learning, vol. 8, pp. 323-339, 1992.
[23] G. Vercelli, R. Zaccaria, and P. Morasso, "A Theory of Sensor-Based Robot Navigation Using Local Information," Proc. Congresso dell'Associazione Italiana Intelligenza Artificiale,Italy, pp. 342-351, 1991.

Index Terms:
Autonomous agents, clustering, environment maps, heuristic algorithms, knowledge representation.
Dario Maio, Davide Maltoni, Stefano Rizzi, "Dynamic Clustering of Maps in Autonomous Agents," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 11, pp. 1080-1091, Nov. 1996, doi:10.1109/34.544077
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