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FPGA-Based Parallel Metaheuristic PSO Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation

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Abstract

This paper presents a field-programmable gate array (FPGA)-based parallel metaheuristic particle swarm optimization algorithm (PPSO) and its application to global path planning for autonomous robot navigating in structured environments with obstacles. This PPSO consists of three parallel PSOs along with a communication operator in one FPGA chip. The parallel computing architecture takes advantages of maintaining better population diversity and inhibiting premature convergence in comparison with conventional PSOs. The collision-free discontinuous path generated from the PPSO planner is then smoothed using the cubic B-spline and system-on-a-programmable-chip (SoPC) technology. Experimental results are conducted to show the merit of the proposed FPGA-based PPSO path planner and smoother for global path planning of autonomous mobile robot navigation.

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References

  1. Cowlagi, R.V., Tsiotras, P.: Multiresolution motion planning for autonomous agents via wavelet-based cell decompositions. IEEE Trans. Sys. Man Cybern. Part B Cybern. 42(5), 1455–1469 (2012)

    Article  Google Scholar 

  2. Cai, C., Ferrari, S.: Information-driven sensor path planning by approximate cell decomposition. IEEE Trans. Sys. Man Cybern. Part B Cybern. 39(3), 672–689 (2009)

    Article  Google Scholar 

  3. Ren, J., McIsaac, K.A., Patel, R.V.: Modified Newton’s method applied to potential field-based navigation for mobile robots. IEEE Trans. Robot. 22(2), 384–390 (2006)

    Article  Google Scholar 

  4. Cupertino, F., Giordano, V., Naso, D., Delfine, L.: Fuzzy control of a mobile robot. IEEE Robot. Autom. Mag. 13(4), 74–81 (2006)

    Article  Google Scholar 

  5. Valavanis, K.P., Doitsidis, L., Long, M., Murphy, R.R.: A case study of fuzzy-logic-based robot navigation. IEEE Robot. Autom. Mag. 13(3), 93–107 (2006)

    Article  Google Scholar 

  6. Yang, S.X., Luo, C.: A neural network approach to complete coverage path planning. IEEE Trans. Sys. Man Cybern. Part B Cybern. 34(1), 718–724 (2004)

    Article  Google Scholar 

  7. Li, H., Yang, S.X., Seto, M.L.: Neural-network-based path planning for a multirobot system with moving obstacles. IEEE Trans. Sys. Man Cybern. Part C Appl. Rev. 39(4), 410–419 (2009)

    Article  Google Scholar 

  8. Hocaoglu, C., Sanderson, A.C.: Planning multiple paths with evolutionary speciation. IEEE Trans. Evol. Comput. 5(3), 169–191 (2001)

    Article  Google Scholar 

  9. Allaire, F.C., Tarbouchi, M., Labonte, G., Fusina G.: FPGA implementation of genetic algorithm for UAV real-time path planning. J Intell. Robot. Syst. 54(1), 495–510 (2009)

    Article  Google Scholar 

  10. Gallagher, J.C., Vigraham, S., Kramer, G.: A family of compact genetic algorithms for intrinsic evolvable hardware. IEEE Trans. Evol. Comput. 8(2), 111–126 (2004)

    Article  Google Scholar 

  11. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)

  12. Gaing, Z.L.: A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans. Energy Convers. 19(2), 384–391 (2004)

    Article  Google Scholar 

  13. Wai, R.J., Lee, J.D., Chuang, K.L.: Real-time PID control strategy for maglev transportation system via particle swarm optimization. IEEE Trans. Ind. Electron. 58(2), 629–646 (2011)

    Article  Google Scholar 

  14. Song, Y., Chen, Z., Yuan, Z.: New chaotic PSO-based neural network predictive control for nonlinear process. IEEE Trans. Neural Netw. 18(2), 595–601 (2007)

    Article  Google Scholar 

  15. Chatterjee, A., Pulasinghe, K., Watanabe, K., Izumi, K.: A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems. IEEE Trans Ind. Electron. 52(6), 1478–1489 (2005)

    Article  Google Scholar 

  16. Villegas, F.J.: Parallel genetic-algorithm optimization of shaped beam coverage areas using planar 2-D phased arrays. IEEE Trans. Antennas Propag. 55(6), 1745–1753 (2007)

    Article  Google Scholar 

  17. Kim, J.Y., Jeong, H.M., Lee, H.S., Park, J.H.: PC cluster based parallel PSO algorithm for optimal power flow. IEEE Int. Conf. Intell. Syst. Appl. Power Syst. 1–6 (2007)

  18. Olman, V., Mao, F., Wu, H., Xu, Y.: Parallel clustering algorithm for large data sets with applications in bioinformatics. IEEE/ACM Trans. Comput. Biol. Bioinforma. 6(2), 344–352 (2009)

    Article  Google Scholar 

  19. Henrich, D.: Fast motion planning by parallel processing-a review. J. Intell. Robot. Syst. 20(1), 45–69 (1997)

    Article  Google Scholar 

  20. Huang, Q., Hu, S., Matin, R.: Fast degree elevation and knot insertion for B spline curve. Comput. Aided Geom. Des. 22(2), 183–197 (2005)

    Article  MATH  Google Scholar 

  21. Tsai, C.C., Huang, H.C., Chan, C.K.: Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation. IEEE Trans. Ind. Electron. 58(10), 4813–4821 (2011)

    Article  Google Scholar 

  22. Thévenaz, P., Blu, T., Unser, M.: Interpolation revisited [medical images application]. IEEE Trans. Med. Imaging 19(7), 739–758 (2000)

    Article  Google Scholar 

  23. Wang, Q., Wu, L.: Translation invariance and sampling theorem of wavelet. IEEE Trans. Signal Proc. 48(5), 1471–1474 (2000)

    Article  MATH  Google Scholar 

  24. Johnson, C.G., Marsh, D.: Modelling robot manipulators with multivariate B-splines. Robotica 17(3), 239–247 (1999)

    Article  Google Scholar 

  25. Huang, H.C.: Intelligent motion control for omnidirectional mobile robots using ant colony optimization. Appl. Artif. Intell. 27(3), 151–169 (2013)

    Article  Google Scholar 

  26. Huang, H.C., Tsai, C.C., Lin, S.C.: Adaptive polar-space motion control for embedded omnidirectional mobile robots with parameter variations and uncertainties. J. Intell. Robot. Syst. 62(1), 81–102 (2011)

    Article  Google Scholar 

  27. Jang, J.O.: Adaptive neuro-fuzzy network control for a mobile robot. J. Intell. Robot. Syst. 62, 567–586 (2011)

    Article  MATH  Google Scholar 

  28. Juang, C.F., Hsu, C.H.: Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control. IEEE Trans. Ind. Electron. 56(10), 3931–3940 (2009)

    Article  Google Scholar 

  29. Menegaldo, L.L., Ferreira, G.A., Santos, M.F., Guerato, R.S.: Development and navigation of a mobile robot for floating production storage and offloading ship hull inspect. IEEE Trans. Ind. Electron. 56(9), 3717–3722 (2009)

    Article  Google Scholar 

  30. Serra, M., Slater, T., Muzio, J.C., Miller, D.M.: The analysis of one-dimensional linear cellular automata and their aliasing properties. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 9(7), 767–778 (1990)

    Article  Google Scholar 

  31. Siegwart, R., Nourbakh, I.R.: Introduction to Autonomous Mobile Robots. The MIT Press, Cambridge (2004)

    Google Scholar 

  32. Engelbrecht, A.P.: Computational Intelligence. John Wiley & Sons, Ltd, New York (2007)

    Book  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, National Ilan University, No. 1, Sec. 1, Shen-Lung Road, Yilan City, Taiwan

    Hsu-Chih Huang

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  1. Hsu-Chih Huang
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Correspondence to Hsu-Chih Huang.

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Huang, HC. FPGA-Based Parallel Metaheuristic PSO Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation. J Intell Robot Syst 76, 475–488 (2014). https://doi.org/10.1007/s10846-013-9884-9

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  • DOI: https://doi.org/10.1007/s10846-013-9884-9

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