Skip to main content
Log in

A type of biased consensus-based distributed neural network for path planning

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

In this paper, a unified scheme is proposed for solving the classical shortest path problem and the generalized shortest path problem, which are highly nonlinear. Particularly, the generalized shortest path problem is more complex than the classical shortest path problem since it requires finding a shortest path among the paths from a vertex to all the feasible destination vertices. Different from existing results, inspired by the optimality principle of Bellman’s dynamic programming, we formulate the two types of shortest path problems as linear programs with the decision variables denoting the lengths of possible paths. Then, biased consensus neural networks are adopted to solve the corresponding linear programs in an efficient and distributed manner. Theoretical analysis guarantees the performance of the proposed scheme. In addition, two illustrative examples are presented to validate the efficacy of the proposed scheme and the theoretical results. Moreover, an application to mobile robot navigation in a maze further substantiates the efficacy of the proposed scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Kim, S., Lewis, M.E., White, C.C.: State space reduction for nonstationary stochastic shortest path problems with real-time traffic information. IEEE Trans. Intell. Transp. Syst. 6, 273–284 (2005)

    Article  Google Scholar 

  2. Cota-Ruiz, J., Rivas-Perea, P., Sifuentes, E., Gonzalez-Landaeta, R.: A recursive shortest path routing algorithm with application for wireless sensor network localization. IEEE Sens. J. 16, 4631–4637 (2016)

    Article  Google Scholar 

  3. Junior, J.J., Cortex, P.C., Backes, A.R.: Color texture classification using shortest paths in graphs. IEEE Trans. Image Process. 23, 3751–3761 (2014)

    Article  MathSciNet  Google Scholar 

  4. Zhang, Y., Yan, X., Chen, D., Guo, D., Li, W.: QP-based refined manipulability-maximizing scheme for coordinated motion planning and control of physically constrained wheeled mobile redundant manipulators. Nonlinear Dyn. 85, 245–261 (2016)

    Article  MathSciNet  Google Scholar 

  5. Lawler, E.L.: Combinatorial Optimization: Networks and Matroids. Holt, Rinehart, and Winston, New York (1976)

    MATH  Google Scholar 

  6. Wang, J.: A recurrent neural network for solving the shortest path problem. IEEE Trans. Circuits Syst. I(43), 482–486 (1996)

    Article  Google Scholar 

  7. Xia, Y., Wang, J.: A discrete-time recurrent neural network for shortest-path routing. IEEE Trans. Autom. Control 45(11), 2129–2134 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  8. Sun, C.-C., Jan, G.E., Leu, S.-W., Yang, K.-C., Chen, Y.-C.: Near-shortest path planning on a quadratic surface with \(O(n \text{ log } n)\) time. IEEE Sens. J. 15, 6079–6080 (2015)

    Article  Google Scholar 

  9. Jan, G.E., Sun, C.-C., Tsai, W.C., Lin, T.-H.: An \(O(n \text{ log }n)\) shortest path algorithm based on Delaunay triangulation. IEEE/ASME Trans. Mechatron. 19, 660–666 (2014)

    Article  Google Scholar 

  10. Ma, S., Feng, K., Li, J., Wang, H., Cong, G., Huai, J.: Proxies for shortest path and distance queries. IEEE Trans. Knowl. Data Eng. 28(7), 1835–1849 (2016)

    Article  Google Scholar 

  11. Lei, G., Dou, Y., Li, R., Xia, F.: An FPGA implementation for solving the large single-source-shortest-path problem. IEEE Trans. Circuits Syst. II(63), 473–477 (2016)

    Article  Google Scholar 

  12. Li, X., Rakkiyappan, R., Velmurugan, G.: Dissipativity analysis of memristor-based complex-valued neural networks with time-varying delays. Inf. Sci. 294, 645–665 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  13. Xiao, L., Lu, R.: Finite-time solution to nonlinear equation using recurrent neural dynamics with a specially-constructed activation function. Neurocomputing 151, 246–251 (2016)

    Article  Google Scholar 

  14. Xiao, L., Zhang, Y.: A new performance index for the repetitive motion of mobile manipulators. IEEE Trans. Cybern. 44, 280–292 (2014)

    Article  Google Scholar 

  15. Wang, Y., Cheng, L., Hou, Z.-G., Yu, J., Tan, M.: Optimal formation of multirobot systems based on a recurrent neural network. IEEE Trans. Neural Netw. Learn. Syst. 27, 322–333 (2016)

    Article  MathSciNet  Google Scholar 

  16. Liu, Q., Wang, J.: Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions. IEEE Trans. Neural Netw. 22, 601–613 (2011)

    Article  Google Scholar 

  17. Xia, Y., Wang, J.: A bi-projection neural network for solving constrained quadratic optimization problems. IEEE Trans. Neural Netw. Learn. Syst. 27, 214–224 (2016)

    Article  MathSciNet  Google Scholar 

  18. Zhang, S., Xia, Y., Wang, J.: A complex-valued projection neural network for constrained optimization of real functions in complex variables. IEEE Trans. Neural Netw. Learn. Syst. 26, 3227–3238 (2015)

    Article  MathSciNet  Google Scholar 

  19. Li, X., Song, S.: Impulsive control for existence, uniqueness, and global stability of periodic solutions of recurrent neural networks with discrete and continuously distributed delays. IEEE Trans. Neural. Netw. Learn. Syst. 24, 868–877 (2013)

    Article  Google Scholar 

  20. Hopfield, J.J., Tank, D.W.: “Neural” computation of decisions in optimization problems. Biol. Cybern. 52, 141–152 (1985)

    MATH  Google Scholar 

  21. Tank, D.W., Hopfield, J.J.: Simple “neural” optimization networks: an A/D converter, signal decision circuit, and a linear programming circuit. IEEE Trans. Circuits Syst. 33, 533–541 (1986)

    Article  Google Scholar 

  22. Araújo, F., Ribeiro, B., Rodrigues, L.: A neural network for shortest path computation. IEEE Trans. Neural Netw. 12(5), 1067–1073 (2001)

    Article  Google Scholar 

  23. Taccari, L.: Integer programming formulations for the elementary shortest path problem. Eur. J. Oper. Res. 252, 122–130 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  24. Nazemi, A., Omidi, F.: An efficient dynamic model for solving the shortest path problem. Transport. Res. C Emerg. 26, 1–19 (2013)

    Article  Google Scholar 

  25. Zhang, Y., Wu, L., Wei, G., Wang, S.: A novel algorithm for all pairs shortest path problem based on matrix multiplication and pulse coupled neural network. Digit. Signal Process. 21, 517–521 (2011)

    Article  Google Scholar 

  26. Sang, Y., Lv, J., Qu, H., Yi, Z.: Shortest path computation using pulse-coupled neural networks with restricted autowave. Knowl. Based Syst. 114, 1–11 (2016)

    Article  Google Scholar 

  27. Li, X., Ma, Y., Feng, X.: Self-adaptive autowave pulse-coupled neural network for shortest-path problem. Neurocomputing 115, 63–71 (2013)

    Article  Google Scholar 

  28. Qu, H., Yi, Z., Yang, S.X.: Efficient shortest-path-tree computation in network routing based on pulse-coupled neural networks. IEEE Trans. Cybern. 43, 995–1010 (2013)

    Article  Google Scholar 

  29. Bellman, R.: On a routing problem. Q. Appl. Math. 16(1), 87–90 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  30. Li, H., Chen, G., Liao, X., Huang, T.: Leader-following consensus of discrete-time multiagent systems with encoding-decoding. IEEE Trans. Circuits Syst. II Express Briefs 63, 401–405 (2016)

    Article  Google Scholar 

  31. Cheng, S., Yu, L., Zhang, D., Huo, L., Ji, J.: Consensus of second-order multi-agent systems using partial agents’ velocity measurements. Nonlinear Dyn. 86, 1927–1935 (2016)

    Article  Google Scholar 

  32. Li, H., Chen, G., Huang, T., Dong, Z., Zhu, W., Gao, L.: Event-triggered distributed average consensus over directed digital networks with limited communication bandwidth. IEEE Trans. Cybern. 46, 3098–3110 (2016)

    Article  Google Scholar 

  33. Wen, G.-X., Chen, C.L.P., Liu, Y.-J., Liu, Z.: Neural network-based adaptive leader-following consensus control for a class of nonlinear multiagent state-delay systems. IEEE Trans. Cybern. (2016). doi:10.1109/TCYB.2016.2608499

  34. Li, H., Chen, G., Huang, T., Dong, Z.: High-performance consensus control in networked systems with limited bandwidth communication and time-varying directed topologies. IEEE Trans. Neural Netw. Learn. Syst. (2016). doi:10.1109/TNNLS.2016.2519894

  35. Zhang, Y., Chen, D., Guo, D., Liao, B., Wang, Y.: On exponential convergence of nonlinear gradient dynamics system with application to square root finding. Nonlinear Dyn. 79, 983–1003 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  36. Xiao, Y.: A nonlinearly activated neural dynamics and its finite-time solution to time-varying nonlinear equation. Neurocomputing 173, 1983–1988 (2016)

    Article  Google Scholar 

  37. Ma, Z., Wang, Y., Li, X.: Cluster-delay consensus in first-order multi-agent systems with nonlinear dynamics. Nonlinear Dyn. 83(3), 1303–1310 (2016)

  38. Chen, C.L.P., Wen, G.-X., Liu, Y.-J., Liu, Z.: Observer-based adaptive backstepping consensus tracking control for high-order nonlinear semi-strict-feedback multiagent systems. IEEE Trans. Cybern. 46, 1591–1601 (2016)

    Article  Google Scholar 

  39. Zhou, B., Liao, X.: Leader-following second-order consensus in multi-agent systems with sampled data via pinning control. Nonlinear Dyn. 78, 555–569 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  40. Khalil, H.K.: Nonlinear Systems. Prentice-Hall, Englewood Cliffs (2002)

    MATH  Google Scholar 

  41. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge Univ. Press, Cambridge (2004)

    Book  MATH  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (with number 61401385), by Hong Kong Research Grants Council Early Career Scheme (with number 25214015) and also by Departmental General Research Fund of Hong Kong Polytechnic University (with number G.61.37.UA7L). Besides, the authors would like to thank the editors and anonymous reviewers for valuable comments and constructive suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shuai Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Li, S. & Guo, H. A type of biased consensus-based distributed neural network for path planning. Nonlinear Dyn 89, 1803–1815 (2017). https://doi.org/10.1007/s11071-017-3553-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-017-3553-7

Keywords

Navigation