Advertisement

Neural Computing and Applications

, Volume 31, Supplement 2, pp 1275–1289 | Cite as

Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

  • Weria KhaksarEmail author
  • Tang Sai Hong
  • Khairul Salleh Mohamed Sahari
  • Mansoor Khaksar
  • Jim Torresen
Original Article
  • 191 Downloads

Abstract

Despite the proven advantages of sampling-based motion planning algorithms, their inability to handle online navigation tasks and providing low-cost solutions make them less efficient in practice. In this paper, a novel sampling-based algorithm is proposed which is able to plan in an unknown environment and provides solutions with lower cost in terms of path length, runtime and stability of the results. First, a fuzzy controller is designed which incorporates the heuristic rules of Tabu search to enable the planner for solving online navigation tasks. Then, an adaptive neuro-fuzzy inference system (ANFIS) is proposed such that it constructs and optimizes the fuzzy controller based on a set of given input/output data. Furthermore, a heuristic dataset generator is implemented to provide enough data for the ANFIS using a randomized procedure. The performance of the proposed algorithm is evaluated through simulation in different motion planning queries. Finally, the proposed planner is compared to some of the similar motion planning algorithms to support the claim of superiority of its performance.

Keywords

Sampling-based motion planning Fuzzy controller Tabu search ANFIS 

Notes

Acknowledgements

This work is partially supported by The Research Council of Norway as a part of the Multimodal Elderly Care Systems (MECS) Project, under Grant Agreement 247697 and by Malaysia Fundamental Research Grant Scheme (FRGS) (Grant No. FRGS/2/2014/TK06/UNITEN/02/7).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Choset HM (2005) Principles of robot motion: theory, algorithms, and implementations. MIT Press, CambridgezbMATHGoogle Scholar
  2. 2.
    Canny J (1988) The complexity of robot motion planning. MIT Press, CambridgezbMATHGoogle Scholar
  3. 3.
    Tang S, Khaksar W, Ismail N, Ariffin M (2012) A review on robot motion planning approaches. Pertanika J Sci Technol 20:15–29Google Scholar
  4. 4.
    Lozano-Perez T (1983) Spatial planning: a configuration space approach. IEEE Trans Comput 100:108–120MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Asano T, Asano T, Guibas L, Hershberger J, Imai H (1985) Visibility-polygon search and euclidean shortest paths. In: 26th annual symposium on foundations of computer science. IEEE, pp 155–164Google Scholar
  6. 6.
    Liu YS, Ramani K, Liu M (2011) Computing the inner distance of volumetric models for articulated shape description with a visibility graph. IEEE Trans Pattern Anal Mach Intell 33:2538–2544CrossRefGoogle Scholar
  7. 7.
    Ramer C, Reitelshofer S, Franke J (2013) A robot motion planner for 6-DOF industrial robots based on the cell decomposition of the workspace. In: Proceedings of the 44th international symposium on robotics (ISR), pp 1–4Google Scholar
  8. 8.
    Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res 5:90–98CrossRefGoogle Scholar
  9. 9.
    Ng J, Bräunl T (2007) Performance comparative of bug navigation algorithms. J Intell Rob Syst 50:73–84CrossRefGoogle Scholar
  10. 10.
    Ivan V, Zarubin D, Toussaint M, Komura T, Vijayakumar S (2013) Topology-based representations for motion planning and generalisation in dynamic environments with interactions. Int J Robot Res 32(9–10):1151–1163CrossRefGoogle Scholar
  11. 11.
    Hsu D, Tingting J, Reif J, Zheng S (2003) The bridge test for sampling narrow passages with probabilistic roadmap planners. In: IEEE international conference on robotics and automation, 2003. Proceedings, ICRA ‘03, vol 4423, pp 4420–4426Google Scholar
  12. 12.
    Khaksar W, Hong TS, Khaksar M, Motlagh O (2013) A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning. Int J Adv Robot Syst 10(11):397CrossRefGoogle Scholar
  13. 13.
    Yershova A, Jaillet L, Siméon T, LaValle SM (2005) Dynamic-domain RRTs: efficient exploration by controlling the sampling domain. In: Proceedings of the 2005 IEEE international conference on robotics and automation, 2005, ICRA 2005. IEEE, pp 3856–3861Google Scholar
  14. 14.
    Masehian E, Sedighizadeh D (2013) An improved particle swarm optimization method for motion planning of multiple robots. In: Martinoli A et al (eds) Distributed autonomous robotic systems, vol 83, pp 175–188Google Scholar
  15. 15.
    Buniyamin N, Sariff N, Wan Ngah W, Mohamad Z (2011) Robot global path planning overview and a variation of ant colony system algorithm. Int J Math Comput Simul 5:9–16Google Scholar
  16. 16.
    Jaradat MAK, Garibeh MH, Feilat EA (2012) Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field. Soft Comput 16:153–164CrossRefGoogle Scholar
  17. 17.
    Motlagh O, Nakhaeinia D, Tang SH, Karasfi B, Khaksar W (2013) Automatic navigation of mobile robots in unknown environments. Neural Comput Appl 24(7–8):1569–1581Google Scholar
  18. 18.
    Ahmed F, Deb K (2013) Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms. Soft Comput 17:1283CrossRefGoogle Scholar
  19. 19.
    Mei H, Tian Y, Zu L (2006) A hybrid ant colony optimization algorithm for path planning of robot in dynamic environment. Int J Inf Technol 12:78–88Google Scholar
  20. 20.
    Kala R (2012) Multi-robot path planning using co-evolutionary genetic programming. Expert Syst Appl 39:3817–3831CrossRefGoogle Scholar
  21. 21.
    Tsianos KI, Sucan IA, Kavraki LE (2007) Sampling-based robot motion planning: towards realistic applications. Comput Sci Rev 1:2–11CrossRefGoogle Scholar
  22. 22.
    Ferguson D, Kalra N, Stentz A (2006) Replanning with rrts. In: Proceedings 2006 IEEE international conference on robotics and automation, 2006, ICRA 2006. IEEE, pp 1243–1248Google Scholar
  23. 23.
    Um D, Gutierrez MA, Bustos P, Kang S (2013) Simultaneous planning and mapping (SPAM) for a manipulator by best next move in unknown environments. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems, pp 5273–5278Google Scholar
  24. 24.
    Khaksar W, Hong TS, Khaksar M, Motlagh ORE (2012) Sampling-based Tabu search approach for online path planning. Adv Robot 26:1013–1034Google Scholar
  25. 25.
    Boor V, Overmars MH, van der Stappen AF (1999) The Gaussian sampling strategy for probabilistic roadmap planners. In: IEEE international conference on robotics and automation, 1999. Proceedings. IEEE, pp 1018–1023Google Scholar
  26. 26.
    Wedge NA, Brtanicky MS (2011) Using path-length localized RRT-like search to solve challenging planning problems. In: Proceedings of IEEE international conference on robotics and automation, pp 3713–3718 (2011)Google Scholar
  27. 27.
    LaValle SM (2006) Planning algorithms. Cambridge University Press, CambridgeCrossRefzbMATHGoogle Scholar
  28. 28.
    Dobson A, Bekris K (2013) Sparse roadmap spanners for asymptotically near-optimal motion planning. Int J Robot Res 33:18–47CrossRefGoogle Scholar
  29. 29.
    LaValle SM, Kuffner JJ (2001) Randomized kinodynamic planning. Int J Robot Res 20:378–400CrossRefGoogle Scholar
  30. 30.
    Karaman S, Frazzoli E (2011) Sampling-based algorithms for optimal motion planning. Int J Robot Res 30:846–894CrossRefzbMATHGoogle Scholar
  31. 31.
    Nieuwenhuisen D, Overmars MH (2004) Useful cycles in probabilistic roadmap graphs. In: 2004 IEEE international conference on robotics and automation, 2004. Proceedings, ICRA’04. IEEE, pp 446–452Google Scholar
  32. 32.
    Thomas S, Morales M, Tang X, Amato NM (2007) Biasing samplers to improve motion planning performance. In: 2007 IEEE international conference on robotics and automation. IEEE, pp 1625–1630Google Scholar
  33. 33.
    Tian Y, Yan L, Park G-Y, Yang S-H, Kim Y-S, Lee S-R, Lee C-Y (2007) Application of RRT-based local path planning algorithm in unknown environment. In: International symposium on computational intelligence in robotics and automation, 2007. CIRA 2007. IEEE, pp 456–460Google Scholar
  34. 34.
    Bekris KE, Kavraki LE (2007) Greedy but safe replanning under kinodynamic constraints. In: 2007 IEEE international conference on robotics and automation. IEEE, pp 704–710Google Scholar
  35. 35.
    Chang-an L, Jin-gang C, Guo-dong L, Chun-yang L (2008) Mobile robot path planning based on an improved rapidly-exploring random tree in unknown environment. In: IEEE international conference on automation and logistics, 2008, ICAL 2008. IEEE, pp 2375–2379Google Scholar
  36. 36.
    Nieto J, Slawinski E, Mut V, Wagner B (2010) Online path planning based on rapidly-exploring random trees. In: 2010 IEEE international conference on industrial technology (ICIT). IEEE, pp 1451–1456Google Scholar
  37. 37.
    Chakravorty S, Kumar S (2011) Generalized sampling-based motion planners. IEEE Trans Syst Man Cybern Part B Cybern 41:855–866CrossRefGoogle Scholar
  38. 38.
    Jaillet L, Yershova A, La Valle SM, Siméon T (2005) Adaptive tuning of the sampling domain for dynamic-domain RRTs. In: 2005 IEEE/RSJ international conference on intelligent robots and systems, 2005 (IROS 2005). IEEE, pp 2851–2856Google Scholar
  39. 39.
    Luna R, Sucan IA, Moll M, Kavraki LE (2013) Anytime solution optimization for sampling-based motion planning. In: Proceedings of the IEEE international conference on robotics and automation, pp 5068–5074Google Scholar
  40. 40.
    LaValle SM (2011) Motion planning, part I: the essentials. IEEE Robot Autom Mag 18:79–89CrossRefGoogle Scholar
  41. 41.
    Hsu D, Sánchez-Ante G, Sun Z (2005) Hybrid PRM sampling with a cost-sensitive adaptive strategy. In: Proceedings of the 2005 IEEE international conference on robotics and automation, 2005, ICRA 2005. IEEE, pp 3874–3880Google Scholar
  42. 42.
    Burns B, Brock O (2005) Sampling-based motion planning using predictive models. In: Proceedings of the 2005 IEEE international conference on robotics and automation, 2005, ICRA 2005. IEEE, pp 3120–3125Google Scholar
  43. 43.
    Rodriguez S, Tang X, Lien JM, Amato NM (2006) An obstacle-based rapidly-exploring random tree. In: Proceedings 2006 IEEE international conference on robotics and automation, 2006, ICRA 2006. IEEE, pp 895–900Google Scholar
  44. 44.
    Szádeczky-Kardoss E, Kiss B (2006) Extension of the rapidly exploring random tree algorithm with key configurations for nonholonomic motion planning. In: IEEE international conference on mechatronics, 2006. IEEE, pp 363–368Google Scholar
  45. 45.
    W. Wang, Y. Li, X. Xu, S.X. Yang (2010) An adaptive roadmap guided Multi-RRTs strategy for single query path planning. In: 2010 IEEE international conference on robotics and automation (ICRA). IEEE, pp 2871–2876Google Scholar
  46. 46.
    Fleury S, Soueres P, Laumond J-P, Chatila R (1995) Primitives for smoothing mobile robot trajectories. IEEE Trans Robot Autom 11:441–448CrossRefGoogle Scholar
  47. 47.
    Shiller Z, Dubowsky S (1991) On computing the global time-optimal motions of robotic manipulators in the presence of obstacles. IEEE Trans Robot Autom 7:785–797CrossRefGoogle Scholar
  48. 48.
    Urmson C, Simmons R (2003) Approaches for heuristically biasing RRT growth. In: Proceedings. 2003 IEEE/RSJ international conference on intelligent robots and systems, 2003 (IROS 2003). IEEE, pp 1178–1183Google Scholar
  49. 49.
    Ferguson D, Stentz A (2006) Anytime RRTs. In: 2006 IEEE/RSJ international conference on intelligent robots and systems. IEEE, pp 5369–5375Google Scholar
  50. 50.
    Wedge NA, Branicky MS (2008) On heavy-tailed runtimes and restarts in rapidly-exploring random trees. In: Twenty-third AAAI conference on artificial intelligence, pp 127–133Google Scholar
  51. 51.
    Dan Zhu Q, Bin Wu Y, Qiang Wu G, Wang X (2009) An improved anytime RRTs algorithm. In: International conference on artificial intelligence and computational intelligence, 2009, AICI’09. IEEE, pp 268–272Google Scholar
  52. 52.
    Clawson Z, Ding X, Englot B, Frewen TA, Sisson WM, Vladimirsky A (2015) A bi-criteria path planning algorithm for robotics applications. arXiv preprint arXiv:1511.01166
  53. 53.
    Pareekutty N, James F, Ravindran B, Shah SV (2016) RRT-HX: RRT with heuristic extend operations for motion planning in robotic systems. In: ASME 2016 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers, pp V05AT07A052–V05AT07A052Google Scholar
  54. 54.
    Yang L, Xiao J, Qi J, Yang L, Wang L, Han J (2016) GART: an environment-guided path planner for robots in crowded environments under kinodynamic constraints. Int J Adv Rob Syst 13(6):1–18Google Scholar
  55. 55.
    Wang C, Meng MQH (2016) Variant step size RRT: an efficient path planner for UAV in complex environments. In: IEEE international conference on real-time computing and robotics (RCAR), pp 555–560Google Scholar
  56. 56.
    Glover F (1989) Tabu search—part I. OSRA J Comput 3:190–206zbMATHGoogle Scholar
  57. 57.
    Khaksar W, Tang SH, Khaksar M, Motlagh O (2014) A fuzzy-Tabu real time controller for sampling-based motion planning in unknown environment. Appl Intell. doi: 10.1007/s10489-014-0572-7 Google Scholar
  58. 58.
    Negnevitsky M (2005) Artificial intelligence: a guide to intelligent systems. Pearson Education, Upper Saddle RiverGoogle Scholar
  59. 59.
    Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRefGoogle Scholar
  60. 60.
    Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278Google Scholar
  61. 61.
    Jang J-S (1996) Input selection for ANFIS learning. In: Proceedings of the fifth IEEE international conference on fuzzy systems. IEEE, pp 1493–1499Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Robotics and Intelligent Systems Group (ROBIN), Department of InformaticsUniversity of OsloOsloNorway
  2. 2.Department of Mechanical and Manufacturing EngineeringUniversity Putra MalaysiaSerdangMalaysia
  3. 3.Centre for Advanced Mechatronics and Robotics (CAMaRo)Universiti Tenaga NasionalKajangMalaysia
  4. 4.Department of Industrial Engineering, Sanandaj BranchIslamic Azad UniversitySanandajIran

Personalised recommendations