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Real-Time Global Optimal Path Planning of Mobile Robots Based on Modified Ant System Algorithm

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Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4222))

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Abstract

A novel method for the real-time global optimal path planning of mobile robots is proposed based on the modified ant system (AS) algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the Dijkstra algorithm to find a sub-optimal collision-free path, and the third step is adopting the modified AS algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path. The results of simulation experiments confirm that the proposed method is effective and has better performance in convergence speed, solution variation, dynamic convergence behavior, and computation efficiency as compared with the path planning method based on the real-coded genetic algorithm.

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References

  1. Ge, S.S., Cui, Y.J.: New Potential Functions for Mobile Robot Path Planning. IEEE Transactions on Robotics and Automation 16(5), 615–620 (2000)

    Article  Google Scholar 

  2. Boschian, V., Pruski, A.: Grid Modeling of Robot Cells: a Memory-Efficient Approach. Journal of Intelligent and Robotic Systems 8(2), 201–223 (1993)

    Article  Google Scholar 

  3. Yao, X. (ed.): Evolutionary Computation: Theory and Applications. World Scientific Publishing Co., Singapore (1999)

    Google Scholar 

  4. Tan, K.C., Lim, M.H., Yao, X., Wang, L.P. (eds.): Recent Advances in Simulated Evolution And Learning. World Scientific Publishing Co., Singapore (2004)

    MATH  Google Scholar 

  5. Lebedev, D.: Neural Network Model for Robot Path Planning in Dynamically Changing Environment. Modeling and Analysis of Information Systems 18(1), 12–18 (2001)

    Google Scholar 

  6. Liu, C.H., Hu, J.Q., Qi, X.N.: Path Design of Robot with Continuous Space Based on Hybrid Genetic Algorithm. Journal of Wuhan University of Technology 27(6), 819–821 (2003) (in Chinese)

    Google Scholar 

  7. Yung, N.H.C., Cang, Y.: An Intelligent Mobile Vehicle Navigator Based on Fuzzy Logic and Reinforcement Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 29(2), 314–321 (1999)

    Article  Google Scholar 

  8. Dorigo, M., Bonabeau, E., Theraulaz, G.: Ant Algorithms and Stigmergy. Future Generation Computer Systems 16(8), 851–871 (2000)

    Article  Google Scholar 

  9. Habib, M.K., Asama, H.: Efficient Method to Generate Collision Free Paths for Autonomous Mobile Robot Based on New Free Space Structuring Approach. In: Proceedings of the IEEE/RSJ Int. Workshop on Intelligent Robots and Systems, Osaka, Japan, pp. 563–567 (1991)

    Google Scholar 

  10. Dorigo, M., Gambardella, L.M.: Ant Colony System: a Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)

    Article  Google Scholar 

  11. Krohling, R.A., Jaschek, H., Rey, J.P.: Designing PI/PID Controller for a Motion Control System Based on Genetic Algorithm. In: Proceedings of the 12th IEEE International Symposium on Intelligent Control, Istanbul, Turkey, pp. 125–130 (1997)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Tan, G., Mamady I, D. (2006). Real-Time Global Optimal Path Planning of Mobile Robots Based on Modified Ant System Algorithm. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_26

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  • DOI: https://doi.org/10.1007/11881223_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45907-1

  • Online ISBN: 978-3-540-45909-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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