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A new approach to smooth global path planning of mobile robots with kinematic constraints

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Abstract

This paper is concerned with the planning problem of smooth global path for mobile robots with kinematic constraints. A new approach is proposed that is based on a modified particle swarm optimization (MPSO) combined with the η3-splines. Some preliminaries on η3-splines are first introduced to interpolate an arbitrary sequence of points with assigned kinematic parameters associated with the motion and control of mobile robots. Then, the MPSO algorithm with adaptive random fluctuations (ARFs) is proposed to deal with the frequently encountered local convergence issue in the planning of the global smooth path. In the MPSO algorithm, the evolutionary state is classified averagely at each iteration according to the evaluated evolutionary factor, where the velocity updating dynamics switches among various modes according to the evolutionary state and the ARFs are enforced on the global/local best particles in each mode of the current iteration. The proposed MPSO is verified to outperform five other well-known PSO algorithms via comprehensive simulation experiments on a collection of benchmark functions. Finally, the new approach by combining the MPSO algorithm with the η3-splines is exploited to handle a double-layer smooth global path planning problem with several kinematic constraints.

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References

  1. Atyabi A, Powers DMW (2013) Review of clasical and heuristic-based navigation and path planning approaches. Int J Adv Comput Technol 5:1–14

    Google Scholar 

  2. Berglund T, Jonsson H, Soderkvist I (2003) An obstacle-avoiding minimum variation B-spline problem. In: Proceedings of international conference on geometric modeling and graphics, London, pp 156–161

  3. Chang H, Liu J (2009) High-quality path planning for autonomous mobile robots with η 3-splines and parallel genetic algorithms. In: Proceedings of IEEE international conference on robotics and biomimetics, pp 1671–1677

  4. Chen X, Li Y (2006) Smooth path planning of a mobile robot using stochastic particle swarm optimization. In: Proceedings of IEEE international conference on mechatronics and automation, Luoyang, pp 1722–1727

  5. Chen Y, Yu J, Mei Y, Wang Y, Su X (2016) Modified central force optimization (MCFO) algorithm for 3D UAV path planning. Neurocomputing 171(1):878–888

    Article  Google Scholar 

  6. Chhikara RR, Sharma P, Singh L (2016) A hybrid feature selection approach based on improved PSO and filter approaches for image steganalysis. Int J Mach Learn Cybern 7(6):1195–1206

    Article  Google Scholar 

  7. Choi J, Huhtala K (2014) Constrained path optimization with Bezier curve primitives. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems, Chicago, pp 246–251

  8. Clerc M, Kennedy J (2002) The particle swarm: explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  9. Contreras-Cruz MA, Ayala-Ramirez V, Hernandez-Belnonte UH (2015) Mobile robot path planning using artificial bee colony and evolutonary programming. Appl Soft Comput J 30:319–328

    Article  Google Scholar 

  10. Cruz DL, Yu W (2017) Path planning of multi-agent systems in unknown environment with neural kernel smoothing and reinforcement learning. Neurocomputing 233(12):34–42

    Article  Google Scholar 

  11. Ghilardelli F, Lini G, Piazzi A (2014) Path generation using η 4-splines for a truck and trailer vehicle. IEEE Trans Autom Sci Eng 11(1):187–204

    Article  Google Scholar 

  12. Bianco CGL, Piazzi A, Romano M (2004) Smooth motion generation for unicycle mobile robots via dynamic path inversion. IEEE Trans Robot 20(5):884–891

    Article  Google Scholar 

  13. Bianco CGL, Gerelli O (2010) Generation of paths with minimum curvature derivative with η 3-splines. IEEE Trans Autom Sci Eng 7(2):249–256

  14. Henrich D (1997) Fast motion planning by parallel processing—a review. J Intell Robot Syst 20:45–69

    Article  Google Scholar 

  15. Ho YJ, Liu JS (2009) Collision-free curvature-bounded smooth path planning using composite Bezier curve based on Voronoi diagram. In: Proceedings of IEEE international symposium on computational intelligence in robotics and automation, Daejeon, pp 463–468

  16. Huang HC, Tsai CC (2011) Global path planning for autonomous robot navigation using hybrid metaheuristic GA-PSO algorithm. In: Proceedings of SICE annual conference, Tokyo, pp 1338–1348

  17. Huang HC (2014) FPGA-based parallel metaheuristic PSO algorithm and its application to global path planning for autonomous robot navigation. J Intell Robot Syst 76:475–488

    Article  Google Scholar 

  18. Hussein AM, Elnagar A (1997) On smooth and safe trajectory planning in 2D environments. Proc IEEE Int Conf Robot Autom 4:3118–3123

    Article  Google Scholar 

  19. Jolly KG, Kumar RS, Vijayakumar R (2009) A Bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits. Robot Auton Syst 57(1):23–33

    Article  Google Scholar 

  20. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural network, Perth, pp 1942–1948

  21. Lini G, Piazzi A, Consolini L (2011) Multi-optimization of η 3-splines for autonomous parking. In: Proceedings of IEEE conference on decision and control and European control conference, Orlando, pp 6367–6372

  22. Liu S, Sun D (2014) Minimizing energy consumption of wheeled mobile robots via optimal motion planning. IEEE/ASME Trans Mechatron 19(2):401–411

    Article  Google Scholar 

  23. Liu Y, Wang F, Doaie AM, He G, Zhuang Y (2017) Comparison of 2D image models in segmentation performance for 3D laser point clouds. Neurocomputing. doi:10.1016/j.neucom.2017.04.030 (in Press)

  24. Mallick S, Kar R, Mandal D, Ghoshal SP (2017) Optimal sizing of CMOS analog circuits using gravitational search algorithm with particle swarm optimization. Int J Mach Learn Cybern 8(1):309–331

    Article  Google Scholar 

  25. Masehian E, Sedighizadeh D (2007) Classic and heuristic approaches in robot motion planning—a chronological review. Int J Mech Aerosp Ind Mechatron Manuf Eng 1(5):228–233

    Google Scholar 

  26. Mohajer B, Kiani K, Samiei E, Sharifi M (2013) A new online random particles optimization algorithm for mobile robot path planning in dynamic environments. Math Prob Eng 2013:1–9

    Article  Google Scholar 

  27. Nelson W (1989) Continuous curvature paths for autonomous vehicles. In: Proceedings of IEEE/RSJ international conference on robotics and automation, Scottsdale, pp 1260–1264

  28. Piazzi A, Bianco CGL (2000) Quintic \(G^2\)-splines for trajectory planning of autonomous vehicles. In: Proceedings of IEEE intelligent vehicles symposium, pp 198–203

  29. Piazzi A, Guarino Lo Bianco C, Romano M (2007) η 3-splines for the smooth path generation of wheeled mobile robots. IEEE Trans Robot 23(5):1089–1095

    Article  Google Scholar 

  30. Qu H, Xing K, Alexander T (2013) An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing 120:509–517

    Article  Google Scholar 

  31. Raja P, Pugazhenthi S (2012) Optimal path planning of mobile robots: a review. Int J Phys Sci 7(9):1314–1320

    Article  Google Scholar 

  32. Reuter J (1998) Mobile robots trajectories with continuously differentiable curvature: an optimal control approach. Proc IEEE/RSJ Int Conf Intell Robot Syst 1:38–43

    Google Scholar 

  33. Ratnaweera A, Halgamure SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8:240–255

    Article  Google Scholar 

  34. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of IEEE international conference on evolutionary computation, Anchorage, pp 69–73

  35. Song B, Tain G, Zhou F (2010) A comparison study on path smoothing algorithms for laser robot navigated mobile robot path planning in intelligent space. J Inf Comput Sci 7(1):2943–2950

    Google Scholar 

  36. Song B, Wang Z, Sheng L (2016) A new genetic algorithm approach to smooth path planning for mobile robots. Assem Autom 36(2):138–145

    Article  Google Scholar 

  37. Song B, Wang Z, Zou L (2017) On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm. Cognit Comput 9:5–17

    Article  Google Scholar 

  38. Souravlias D, Parsopoulos KE (2016) Particle swarm optimization with neighborhood-based budget allocation. Int J Mach Learn Cybern 7(3):451–477

    Article  Google Scholar 

  39. Tang Y, Wang Z, Fang J (2011) Parameters identification of unknown delayed genetic regulatory networks by a switching particle swarm optimization algorithm. Expert Syst Appl 38:2523–2535

    Article  Google Scholar 

  40. Villagra J, Mounier H (2005) Obstacle-avoiding path planning for high velocity wheeled mobile robots. Proc IFAC World Congr 38(1):49–54

    Google Scholar 

  41. Wang S, Han Z, Liu F, Tang Y (2015) Nonlinear system identification using least squares support vector machine tuned by an adaptive particle swarm optimization. Int J Mach Learn Cybern 6(6):981–992

    Article  Google Scholar 

  42. Zhan Z, Zhang J, Li Y, Chung H (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B Cybern 39(6):1362–1381

    Article  Google Scholar 

  43. Zhang S, Sun L, Chen Z, Lu X, Liu J (2014) Smooth path planning for a home service robot using \(\eta ^3\)-splines. In: Proceedings of IEEE international conference on robotics and biomimetics, pp 1910–1915

  44. Zeng N, Wang Z, Zhang H, Alsaadi FE (2016) A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay. Cognit Comput 8:143–152

    Article  Google Scholar 

  45. Zhou F, Song B, Tian G (2011) Bezier curve based smooth path planning for mobile robot. J Inf Comput Sci 8(1):2441–2450

    Google Scholar 

  46. Ding D, Wang Z, Ho DWC, Wei G (2017) Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks. Automatica 78:231–240

    Article  MathSciNet  MATH  Google Scholar 

  47. Ding D, Wang Z, Wei G, Alsaadi FE (2016) Event-based security control for discrete-time stochastic systems. IET Control Theory Appl 10(15):1808–1815

    Article  MathSciNet  Google Scholar 

  48. Luo Y, Wang Z, Wei G, Alsaadi FE (2017) Robust \(H_{\infty }\) filtering for a class of two-dimensional uncertain fuzzy systems with randomly occurring mixed delays. IEEE Trans Fuzzy Syst 25(1):70–83

    Article  Google Scholar 

  49. Luo Y, Wang Z, Liang J, Wei G, Alsaadi FE (2017) \(H_{\infty }\) control for 2-D fuzzy systems with interval time-varying delays and missing measurements. IEEE Trans Cybern 47(2):365–377

    Google Scholar 

  50. Wang L, Wang Z, Huang T, Wei G (2016) An event-triggered approach to state estimation for a class of complex networks with mixed time delays and nonlinearities. IEEE Trans Cybern 46(11):2497–2508

    Article  Google Scholar 

  51. Zhang J, Ma L, Liu Y (2016) Passivity analysis for discrete-time neural networks with mixed time-delays and randomly occurring quantization effects. Neurocomputing 216:657–665

    Article  Google Scholar 

  52. Zhang W, Wang Z, Liu Y, Ding D, Alsaadi FE (2017) Event-based state estimation for a class of complex networks with time-varying delays: a comparison principle approach. Phys Lett A 381(1):10–18

    Article  MathSciNet  Google Scholar 

  53. Liu Y, Liu W, Obaid MA, Abbas IA (2016) Exponential stability of Markovian jumping Cohen–Grossberg neural networks with mixed mode-dependent time-delays. Neurocomputing 177:409–415

    Article  Google Scholar 

  54. Liu S, Wei G, Song Y, Liu Y (2016) Extended Kalman filtering for stochastic nonlinear systems with randomly occurring cyber attacks. Neurocomputing 207:708–716

    Article  Google Scholar 

  55. Liu D, Liu Y, Alsaadi FE (2016) A new framework for output feedback controller design for a class of discrete-time stochastic nonlinear system with quantization and missing measurement. Int J Gen Syst 45(5):517–531

    Article  MathSciNet  MATH  Google Scholar 

  56. Wen C, Cai Y, Liu Y, Wen C (2016) A reduced-order approach to filtering for systems with linear equality constraints. Neurocomputing 193:219–226

    Article  Google Scholar 

  57. Zeng N, Wang Z, Zhang H (2016) Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter. Sci China Inf Sci. doi:10.1007/s11432-016-0280-9

  58. Chen H, Liang L, Wang Z (2016) Pinning controllability of autonomous Boolean control networks. Sci China Inf Sci. doi:10.1007/s11432-016-5579-8

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Acknowledgements

This work was supported in part by the Research Fund for the Taishan Scholar Project of Shandong Province of China and the Higher Educational Science and Technology Program of Shandong Province of China under Grant J14LN34.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Baoye Song, Zidong Wang, Lei Zou & Lin Xu

  2. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK

    Zidong Wang

  3. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Fuad E. Alsaadi

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  1. Baoye Song
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  2. Zidong Wang
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  3. Lei Zou
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  4. Lin Xu
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  5. Fuad E. Alsaadi
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Correspondence to Zidong Wang.

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Song, B., Wang, Z., Zou, L. et al. A new approach to smooth global path planning of mobile robots with kinematic constraints. Int. J. Mach. Learn. & Cyber. 10, 107–119 (2019). https://doi.org/10.1007/s13042-017-0703-7

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  • DOI: https://doi.org/10.1007/s13042-017-0703-7

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